Episteme: Distilling Knowledge into AI

🚀 Summary

When you can measure what you are speaking about… you know something about it; but when you cannot measure it… your knowledge is of a meagre and unsatisfactory kind. Lord Kelvin

Remember that time you spent an hour with an AI, and in one perfect response, it solved a problem you’d been stuck on for weeks? Where is that answer now? Lost in a scroll of chat history, a fleeting moment of brilliance that vanished as quickly as it appeared. This post is about how to make that moment permanent, and turn it into an intelligence that amplifies everything you do.

Every time you have a breakthrough conversation with AI, you’re creating something valuable but today’s systems forget these moments immediately. We’ve built a way to capture those breakthroughs and turn them into a permanent intelligence amplifier. This post shows how to build an AI that learns not just from data, but from your process of discovery itself.

Here’s the recipe:

• You highlight the moments in conversation where something actually moved: a clear insight, a proof, a working plan.
• We train a small model to recognize those moments your knowledge signal.
• We apply that signal across agents and tasks so your system keeps moving in the directions that matter to you.

The goal: not just smarter outputs, but amplified outcomes aligned to your own learning process.

We’re going to train an AI on your learning, then use that model to lift everything else you do.

This post will show you how to build a personal AI that learns with you, grows with you, and amplifies you.

🎯 What We’ll Do in This Post

• Capture knowledge your knowledge (initially from your chat history).
• Evaluate chats, scoring results from -5 (useless) to +5 (profound).
• Apply knowledge across contexts (demonstrated: paper to blog post).
• Measure the application (typed transfers, confidence, learning score).
• Improve the process (agent/section/prompt-level feedback).

✅ What We can Measure, We can Improve

⚡ The Electricity Paradox (and Why It Matters Here)

We didn’t need a perfect philosophy of electrons to light cities. We measured voltage/current/resistance and optimized. Moore’s Law followed the same pattern: track density, yield, power, thermals → tune relentlessly.

Thesis: Understanding is optional; measurement is mandatory for progress.

🔬 Knowledge as a Measurable Signal

“Knowledge” is hard to define. But in our system it becomes a signal we can observe and improve:

• When knowledge moves: we detect cross-paper application (A → B) as a transfer event.
• What kind of knowledge: we label the transfer as conceptual, procedural, or metacognitive.
• How strong it is: we estimate confidence and an aggregate learning score (0–10).
• Where it happened: section-level attribution (e.g., Methods, Results) and which agent helped.

Think of it like putting a multimeter on cognition. Once the needles move, we have something to tune.

🧪 The Measurement→Improvement Loop

We follow the same engineering loop that took electricity from curiosity to infrastructure:

1. Instrument Log every event (BusEventStore) and extract knowledge_transfer_evidence with type + confidence.
2. Quantify Compute KPIs: transfers, sections, agents, learning score, and top paper→paper pairs.
3. Attribute Tie improvements to agents, sections, and stages (who moved the metric, where).
4. Optimize Adjust prompts, routing, and strategy; re-run; compare deltas. Repeat.

If we can put a number on it, we can move it often compounding (small lifts per iteration stack fast).

📊 From Signals to Control: The Views

• Arena • Live: real-time timeline + top-K what just happened and who’s winning.
• Arena • Learning (Global): cross-run KPIs and Top Transfers (A → B) to spot systemic learning.
• Learning Detail (Per-Run): the “multimeter screen” for a single run evidence table, timeline, insights, and attribution.

🏺 Concrete artifacts surfaced in this post

Attribution (claim → supporting source with similarity):

{"claim":"…","support":{"text":"…","origin":"chat_corpus","variant":"c1287"},"similarity":0.87}

• Attribution What it shows: a specific claim and the snippet that supports it, plus a similarity score. Why it matters: proves the summary is grounded in real sources (not vibes). How to read: higher similarity (e.g., 0.87) = stronger textual support.

• Applied-knowledge lift (iteration-level gains):

{"knowledge_applied_iters":2,"knowledge_applied_lift":0.12}

What it shows: how many refinement steps actually used supported knowledge and the net score gain they produced. Why it matters: quantifies causal impact of knowledge on quality. How to read: knowledge_applied_iters counts the supported iterations; knowledge_applied_lift is the total score increase they delivered.

• Ablation proof (with vs without retrieval):

{"with":{"overall":0.88},"without":{"overall":0.76},"delta":{"overall":0.12}}

What it shows: performance with retrieval vs without it, and the difference. Why it matters: isolates knowledge as the active ingredient (causation, not correlation). How to read: the delta (e.g., 0.12) is the measurable drop when knowledge is removed your “smoking gun”.

💡 The Critical Insight We don’t need to understand knowledge to improve it we just need to measure its application.

🌱 Conversation that Commit

In the previous post Learning from Learning, we proposed that AI shouldn’t only react to signals (losses, scores, metrics). It can also learn from learning itself.

Practically, that means treating the full human–AI interaction our chats, iterations, and the targets we aim for as the source of intelligence. Those targets might be:

• 🧑‍💻 Code
• ✍️ Blog posts
• 📚 Articles or notes
• 📘 Books

Each is the end result of a collaborative process. We believe those results capture something deeper: the generation of new knowledge.

We’ll still use signals. Every item in the pipeline is scored and tracked. But the breakthrough comes from learning not just from outputs, but from the process that produced them.

This post shows how to build exactly that: a system that learns from learning.

    flowchart LR
    A[💬 Your Conversations] --> B["⭐ Mark Knowledge Moments (RHLF²)"]
    B --> C["🧠 Train Knowledge Detector<br/>(pairwise, goal-conditioned)"]
    C --> D["🔁 Apply to New Tasks<br/>(rerank, verify, guide)"]
    D --> E[📈 Measure Lift Across Tasks]
    E -->|feedback| B
  

🙈 Identifying the Unseen

Knowledge doesn’t always announce itself. Patterns can be invisible to us yet perfectly detectable to the right model.

Here’s a tiny demonstration. We’ll generate data that looks like noise, then train two SVMs: a linear one (which fails) and a polynomial one (which reveals the true boundary).

import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC

rng = np.random.default_rng(42)
N = 600
X = rng.uniform(-1.5, 1.5, size=(N, 2))
r2 = (X[:,0]**2 + X[:,1]**2)
y = (r2 < 0.8**2).astype(int)  # 1 = inside the circle, 0 = outside

lin  = SVC(kernel="linear", C=1).fit(X, y)
poly = SVC(kernel="poly", degree=2, coef0=1, C=1).fit(X, y)

def plot_model(ax, clf, title):
    xx, yy = np.meshgrid(np.linspace(-1.6, 1.6, 400),
                         np.linspace(-1.6, 1.6, 400))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
    ax.contourf(xx, yy, Z, alpha=0.25, levels=[-0.5,0.5,1.5])
    ax.scatter(X[:,0], X[:,1], c=y, s=12, edgecolors="k")
    ax.set_title(title); ax.set_aspect("equal", "box")

fig, axes = plt.subplots(1, 2, figsize=(10,4))
plot_model(axes[0], lin,  "Linear SVM (fails)")
plot_model(axes[1], poly, "Poly SVM (degree=2) reveals circle")
plt.tight_layout(); plt.show()

The resulting plot isn’t random at all. It shows a clear decision boundary a shape that separates one type of data from another.

👉 Takeaway: the fact that we can’t see the pattern doesn’t mean it isn’t there. With the right transformation, structure and therefore knowledge emerges.

📊 Scoring the Invisible

Our system already scores alignment, implementability, clarity, and more. The key point: the AI doesn’t “understand” these qualities it learns from signals that approximate them.

See this post: Dimensions of thought for more details on multi-dimensional scoring.

The recipe is simple:

1. Ask an LLM to judge outputs against a goal.
2. Treat those judgements as signals.
3. Train a smaller model to reproduce them (e.g., MRQ, SICQL).
4. Use that model to steer future work without calling the LLM.

We propose the same for knowledge. The system doesn’t need to understand knowledge philosophically; it needs enough examples to separate “knowledgeful” from “not”.

🗺️ How This Post Flows

This post is structured around five core stages of building an AI that learns from learning:

1. Knowledge Ingestion how we capture conversations, signals, and early representations of knowledge.
2. Knowledge Processing how we convert this knowledge to usable signals and information in our system.
3. Knowledge Training & Understanding how we use artifacts and feedback loops to train models that can detect knowledge itself.
4. Knowledge Application & Demonstration how we apply those models in real agents and prove that the system amplifies your learning.
5. Measurement of Learning how we measure the actual learning.

The diagram below shows the flow of the post. Each stage will have its own section, and while there’s plenty of code behind every box, we’ll keep the narrative clear: explain the concept, show a concise snippet, and then demonstrate results.

Note on code: The full source is too extensive to include directly in this post. Instead, we’ll provide summaries of the key components and links to the relevant files for those who want to explore the full implementation. In future posts, we’ll go further: Stephanie will automatically run complete test cases based on your queries, demonstrating each part of the system step by step.

🧩 Section 1: Knowledge Ingestion

    flowchart LR
  A[🧩 Knowledge Ingestion] --> B[🧾 Knowledge Processing]
  B --> C[🧪 Knowledge Training & Understanding]
  C --> D[🚀 Knowledge Application]
  D --> E[📊 Measurement of Learning]

  %% Styles
  classDef ingest fill:#e6f7ff,stroke:#1c75bc,stroke-width:3px,color:#000,font-weight:bold,stroke-dasharray: 5 2;
  classDef process fill:#d0e7ff,stroke:#1c75bc,stroke-width:2px,color:#000;
  classDef train  fill:#f0f0f0,stroke:#555,stroke-width:2px,color:#000;
  classDef apply  fill:#fff7e6,stroke:#e69500,stroke-width:2px,color:#000;
  classDef measure fill:#f9f0ff,stroke:#8e44ad,stroke-width:2px,color:#000;

  %% Assign classes
  class A ingest;
  class B process;
  class C train;
  class D apply;
  class E measure;
  

📥 Chat Processing: Turning Raw Exports into Knowledge

Before we can train on knowledge, we need to get it into the system. The Chat Importer is Stephanie’s bridge between messy chat exports (like OpenAI’s JSON or HTML dumps) and our structured memory.

🔧 Step 1: The Importer Tool

The first challenge is structural: turning messy, unstructured chat logs into clean, standardized, and scorable data that a model can actually learn from.

The core logic lives in stephanie.tools.chat_importer.

👉 Tool Code Here chat_importer

It supports both JSON and HTML exports, deduplicates turns using a stable hash, and normalizes everything into user/assistant pairs:

def _turn_hash(user_text, assistant_text):
    key = (user_text.strip() + "||" + assistant_text.strip()).encode("utf-8")
    return hashlib.sha256(key).hexdigest()

Each chat bundle gets parsed, normalized, and inserted into the database as conversations, messages, and turns:

conv = memory.chats.add_conversation({...})
messages = memory.chats.add_messages(conv.id, turns)
turn_rows = memory.chats.add_turns(conv.id, [m.to_dict() for m in messages])

So whether it’s a ChatGPT JSON file or a vendor HTML export, the result is the same: ✅ clean conversations in the database, ready to become training data.

🚢 Step 2: The Chat Import Agent

Next, the ChatImportAgent runs this importer as part of the pipeline. It’s designed to integrate with goals, context, and logging:

👉 Agent Code Here ChatImportAgent

class ChatImportAgent(BaseAgent):
    async def run(self, context: dict) -> dict:
        summary = import_conversations(self.memory, self.import_path, context)
        self.logger.log("ChatImportSuccess", summary)
        context["chat_imported"] = True
        return context

Think of the Agent as the operational wrapper it decides when to import, where from, and how to log it into the broader workflow.

🏬 Step 3: The Chat Store

Finally, the ChatStore is our memory for conversations, messages and turns.

👉 Memory Code Here ChatStore

Instead of writing raw SQL, we can do:

conv = memory.chats.add_conversation(data)
messages = memory.chats.add_messages(conv.id, turns)
turns = memory.chats.add_turns(conv.id, messages)


class ChatStore(BaseSQLAlchemyStore):
...
    def add_conversation(self, data: dict) -> ChatConversationORM:
        """
        Create and persist a new conversation.
        
        Args:
            data: Dictionary containing conversation attributes
            
        Returns:
            The newly created ChatConversationORM object
        """
        def op(s):
            conv = ChatConversationORM(**data)
            s.add(conv)
            s.flush()
            return conv

        return self._run(op)
...
    def add_messages(
        self, conv_id: int, messages: List[dict]
    ) -> List[ChatMessageORM]:
        """
        Add multiple messages to a conversation.
        
        Args:
            conv_id: ID of the conversation to add messages to
            messages: List of message dictionaries with role, text, and metadata
            
        Returns:
            List of created message objects
        """
        def op(s):
            objs: List[ChatMessageORM] = []
            for i, msg in enumerate(messages):
                objs.append(
                    ChatMessageORM(
                        conversation_id=conv_id,
                        role=msg["role"],
                        text=msg.get("text", ""),
                        order_index=i,
                        parent_id=msg.get("parent_id"),
                        meta=msg.get("meta", {}),
                    )
                )
            s.add_all(objs)
            s.flush()
            return objs

        return self._run(op)
...
    def add_turns(self, conversation_id: int, messages: List[dict]) -> List[ChatTurnORM]:
        """
        Build Q/A turns from a flat list of messages.
        Assumes messages are chronological and include DB ids & order_index.
        
        Args:
            conversation_id: ID of the conversation
            messages: List of message dictionaries with role and ID
            
        Returns: 
            List of created turn objects
        """
        def op(s):
            # find current max for this conversation so appends are monotonic
            cur_max = (
                s.query(func.coalesce(func.max(ChatTurnORM.order_index), -1))
                .filter(ChatTurnORM.conversation_id == conversation_id)
                .scalar()
            )
            next_ix = int(cur_max) + 1

            turns: List[ChatTurnORM] = []
            for i in range(len(messages) - 1):
                u, a = messages[i], messages[i + 1]
                if u.get("role") == "user" and a.get("role") == "assistant":
                    # prefer assistant message order if present, else use running index
                    a_ix = a.get("order_index")
                    oi = int(a_ix) if isinstance(a_ix, int) else next_ix
                    turn = ChatTurnORM(
                        conversation_id=conversation_id,
                        user_message_id=u["id"],
                        assistant_message_id=a["id"],
                        order_index=oi,
                    )
                    s.add(turn)
                    turns.append(turn)
                    next_ix = max(next_ix + 1, oi + 1)

            s.flush()
            return turns
        return self._run(op)

It even converts conversations and turns into Scorable objects that downstream scorers and trainers can use:

def scorable_from_turn(self, turn):
    return Scorable(
        id=str(turn.id),
        text=f"USER: {turn.user_message.text}\nASSISTANT: {turn.assistant_message.text}",
        target_type=TargetType.CONVERSATION_TURN
    )

📖 Step 4: Chat Annotation: Enriching Conversations with Meaning

Importing raw chats gives us clean turns and conversations but at this stage they’re still just text. To make them useful for training and retrieval, we need to enrich them with structure and semantics.

That’s where the ChatAnnotateAgent comes in.

👉 Agent Code Here chat_annotate

Its job is to take each imported conversation and add:

• Domain Classification – what is this conversation about?
• Named Entity Recognition (NER) – who/what is mentioned (people, places, concepts)?
• Knowledge Graph Links – optional integration that pushes entities into a shared KG.

🤔 How It Works

1. Fetch Conversations – Pulls chats from the database (respects limits and “only missing” flags).
2. Goal Linking – Uses the conversation title as a lightweight goal anchor.
3. Domain Annotation – Classifies each turn using seed and goal-aware classifiers.
4. NER Annotation – Extracts entities, optionally publishing them to a knowledge graph.
5. Reporting + Logging – Tracks progress with tqdm, logs results, updates the DB.

    flowchart LR
    subgraph Annotator[🏷 Chat Annotator]
        A1[🔍 Domain Classification] --> A3[🗂 Annotated Turns]
        A2[🧠 Named Entity Recognition] --> A3
        A3 --> A4[🌐 Knowledge Graph Links]
    end

    Importer[📥 Imported Chats] --> Annotator
    Annotator --> Store[(🗄 Annotated DB)]
    Store --> CaseBooks[📚 CaseBooks + Scorables]
    
    style Annotator fill:#fff0f5,stroke:#d63384,stroke-width:2px,color:#000
    style Importer fill:#e6f7ff,stroke:#1c75bc,stroke-width:1px,color:#000
    style Store fill:#ffeccc,stroke:#e69500,stroke-width:1px,color:#000
    style CaseBooks fill:#f9e6ff,stroke:#7300cc,stroke-width:1px,color:#000
  

🏛️ Structured knowledge

By annotating chats with domains and entities, we turn messy text into structured knowledge signals:

• Conversations can be grouped by domain (finance, law, code, etc.).
• Entities are linked across chats, building cross-conversation context.
• Annotated turns become Scorables, ready for downstream evaluation and training.

Without annotation, conversations are just noise. With annotation, they become knowledge assets that Stephanie can reason over.

💬 Step 5: From Conversations to CaseBooks

While most of our CaseBooks originate from research papers and their sections, we also realized that chat conversations themselves are an incredibly rich source of training data. Every chat between human and AI is essentially a trajectory of reasoning attempts, corrections, and refinements exactly the kind of signals Stephanie needs to learn from.

To capture this, we built the ChatToCaseBookAgent. Its job is simple but powerful:

• Take a ChatConversationORM from memory
• Convert it into a CaseBook (one per conversation)
• Break it down into Cases + Scorables depending on the chosen granularity

    async def run(self, context: dict) -> dict:

        goal = context.get(GOAL, {})
        self.report({
            "event": "start",
            "metric": self.metric,
            "limit": self.limit,
            "granularity": self.granularity,
            "goal": goal.get("goal_text") if goal else None
        })

        top_convs = self.memory.chats.get_top_conversations(limit=self.limit, by=self.metric)
        self.report({"event": "selected_conversations", "count": len(top_convs), "metric": self.metric})

        casebooks_created = []
        for idx, (conv, count) in enumerate(top_convs, 1):
            # Gate 1: idempotency store
            if await self._already_converted(conv.id):
                self.report({
                    "event": "skip_already_converted",
                    "reason": "idempotency_store",
                    "conversation_id": conv.id,
                    "title": conv.title,
                    "index": idx,
                    "total": len(top_convs)
                })
                continue

            try:
                cb = self._convert_conversation(conv, context)  # Gate 2 happens inside
                casebooks_created.append(cb)
                await self._mark_converted(conv.id)
                self.report({
                    "event": "converted",
                    "conversation_id": conv.id,
                    "title": conv.title,
                    "metric_count": count,
                    "casebook_id": cb.id,
                    "index": idx,
                    "total": len(top_convs)
                })
            except Exception as e:
                self.report({
                    "event": "error",
                    "conversation_id": conv.id,
                    "title": conv.title,
                    "error": str(e),
                    "index": idx,
                    "total": len(top_convs)
                })

        self.report({"event": "completed", "casebooks_created": len(casebooks_created)})
        context["casebooks_created"] = [cb.id for cb in casebooks_created]
        return context

    def _convert_conversation(self, conv: ChatConversationORM, context: dict) -> CaseBookORM:
        # Make the casebook name unique/stable per conversation
        cb_name = f"[chat:{conv.id}] {conv.title}"

        # Create/retrieve the casebook (include meta for future querying, if supported)
        pipeline_run_id = context.get("pipeline_run_id")
        cb = self.memory.casebooks.ensure_casebook(
            name=cb_name,
            pipeline_run_id=pipeline_run_id,
            description=f"Imported chat conversation: {conv.id} - {conv.title}",
            meta={"conversation_id": conv.id} if hasattr(self.memory.casebooks, "ensure_casebook") else None
        )
        self.report({"event": "casebook_created", "conversation_id": conv.id, "casebook_id": cb.id, "title": conv.title})

        # Gate 2: if this casebook already has cases, skip conversion
        existing = self.memory.casebooks.count_cases(cb.id)
        if existing > 0:
            self.report({
                "event": "skip_already_converted",
                "reason": "existing_cases_in_casebook",
                "conversation_id": conv.id,
                "casebook_id": cb.id,
                "existing_cases": existing
            })
            return cb

        # Create/link goal for this conversation
        goal = self.memory.goals.get_or_create({
            "goal_text": conv.title,
            "description": f"Conversation imported on {conv.created_at or datetime.now()}"
        }).to_dict()
        self.report({"event": "goal_linked", "conversation_id": conv.id, "goal_id": goal["id"], "goal_text": goal["goal_text"]})

        # Generate scorables at requested granularity
        if self.granularity == "conversation":
            scorables = [self.memory.chats.scorable_from_conversation(conv)]
        elif self.granularity == "turns":
            turns = self.memory.chats.get_turns_for_conversation(conv.id)
            scorables = [self.memory.chats.scorable_from_turn(t) for t in turns]
        elif self.granularity == "messages":
            msgs = self.memory.chats.get_messages(conv.id)
            scorables = [self.memory.chats.scorable_from_message(m) for m in msgs]
        else:
            raise ValueError(f"Unsupported granularity: {self.granularity}")

        self.report({"event": "scorables_generated", "conversation_id": conv.id, "granularity": self.granularity, "count": len(scorables)})

        # Create cases with associated scorables
        for sc in scorables:
            case = self.memory.casebooks.add_case(
                prompt_text=conv.title,
                casebook_id=cb.id,
                goal_id=goal["id"],
                agent_name="chat_to_casebook",
                scorables=[{
                    "scorable_id": sc.id,
                    "scorable_type": sc.target_type,
                    "text": sc.text,
                    "source": self.name,
                    "meta": {"conversation_id": conv.id, **(sc.meta or {})},
                }]
            )
            self.report({
                "event": "case_created",
                "case_id": case.id,
                "casebook_id": cb.id,
                "scorable_id": sc.id,
                "conversation_id": conv.id
            })

        return cb

This is an example imported casebook

🍕 Granularity Modes

The agent supports three ways of slicing conversations:

1. Conversation – One case per conversation, useful for capturing global context.
2. Turns – One case per user→assistant turn, preserving dialogue structure.
3. Messages – One case per individual message, the finest granularity.

👔 How It Works

1. Import – The agent selects top conversations (by message count or turn count).
2. CaseBook Creation – Each conversation becomes a CaseBook with a stable name.
3. Goal Linking – A goal is created/linked from the conversation title.
4. Scorables Extraction – Depending on granularity, scorables are built from full convos, turns, or messages.
5. Case Creation – Each scorable is wrapped in a Case and added to the CaseBook.

Every case is scored and persisted just like paper-derived cases, making chat-derived knowledge a first-class citizen in the Learning-from-Learning loop.

🪞 Learning you

This matters because Stephanie can learn from her own conversations with you:

• Which turns were relevant or misleading
• Which replies scored well on knowledge/usefulness
• Where improvements were needed

By treating chats as CaseBooks, we close the loop between usage and training. Every conversation becomes fuel for continuous improvement, side by side with paper-based knowledge.

The full import process is below

    flowchart LR
    subgraph Importer[🔧 Importer Tool]
        A1[📂 JSON/HTML Exports] --> A2[🧹 Deduplication & Normalization]
        A2 --> A3[💬 Conversations + Turns]
    end

    subgraph ImportAgent[🕹 Chat Import Agent]
        B1[⚡ Run Import] --> B2[📊 Log & Context Update]
    end

    subgraph Store[🗄 Chat Store]
        C1[📝 ChatConversationORM] --> C2[💬 ChatMessageORM]
        C2 --> C3[🔄 ChatTurnORM]
        C3 --> C4[🎯 Scorable Objects]
    end

    subgraph AnnotateAgent[🏷 Chat Annotator Agent]
        D1[🔍 Domain Classification] --> D3[🗂 Annotated Conversations]
        D2[🧠 Named Entity Recognition] --> D3
        D3 --> D4[🌐 Knowledge Graph Links]
    end

    Importer --> ImportAgent
    ImportAgent --> Store
    Store --> AnnotateAgent
    AnnotateAgent --> E[📚 CaseBooks & Scorables<br/>Ready for Training]

    %% Styling
    style Importer fill:#e6f7ff,stroke:#1c75bc,stroke-width:2px,color:#000
    style ImportAgent fill:#e6fff2,stroke:#2eb82e,stroke-width:2px,color:#000
    style Store fill:#fff7e6,stroke:#e69500,stroke-width:2px,color:#000
    style AnnotateAgent fill:#fff0f5,stroke:#d63384,stroke-width:2px,color:#000
    style E fill:#f9e6ff,stroke:#7300cc,stroke-width:2px,color:#000

    style A1 fill:#d0e7ff,stroke:#1c75bc,stroke-width:1px
    style A2 fill:#d0e7ff,stroke:#1c75bc,stroke-width:1px
    style A3 fill:#d0e7ff,stroke:#1c75bc,stroke-width:1px

    style B1 fill:#d0f7e0,stroke:#2eb82e,stroke-width:1px
    style B2 fill:#d0f7e0,stroke:#2eb82e,stroke-width:1px

    style C1 fill:#ffeccc,stroke:#e69500,stroke-width:1px
    style C2 fill:#ffeccc,stroke:#e69500,stroke-width:1px
    style C3 fill:#ffeccc,stroke:#e69500,stroke-width:1px
    style C4 fill:#ffeccc,stroke:#e69500,stroke-width:1px

    style D1 fill:#ffd6eb,stroke:#d63384,stroke-width:1px
    style D2 fill:#ffd6eb,stroke:#d63384,stroke-width:1px
    style D3 fill:#ffd6eb,stroke:#d63384,stroke-width:1px
    style D4 fill:#ffd6eb,stroke:#d63384,stroke-width:1px
  

⚙️ The Chat Pipeline

All of this comes together inside Stephanie’s Chat Pipeline a Hydra/YAML config that orchestrates each agent step:

pipeline:
  name: knowledge_pipeline
  tag: knowledge
  stages:
     - name: chat_import
       cls: stephanie.agents.knowledge.chat_import.ChatImportAgent
       enabled: true
     - name: chat_annotate
       cls: stephanie.agents.knowledge.chat_annotate.ChatAnnotateAgent
       enabled: true
     - name: chat_to_casebook
       cls: stephanie.agents.learning.chat_to_casebook.ChatToCaseBookAgent
       enabled: true
     - name: scorable_loader
       cls: stephanie.agents.scorable_loader.ScorableLoaderAgent
       enabled: true

Each stage in the pipeline is an agent with a clear role:

• Import raw chats
• Annotate with domains & NER
• Transform into CaseBooks
• Score them for reuse

👉 Full config here: config/config.yaml

Together, these steps give us a front door for knowledge ingestion. Without it, we’d be left with piles of unstructured logs. With it, every past interaction is captured, structured, and ready to become training data.

But importing is just the start. The real question is: which of these moments actually carry knowledge? That’s where the next stage begins moving from raw conversations into understanding, where we start to measure and evaluate the signal of knowledge itself.

🧾 Section 2: Knowledge Processing

    flowchart LR
  A[🧩 Knowledge Ingestion] --> B[🧾 Knowledge Processing]
  B --> C[🧪 Knowledge Training & Understanding]
  C --> D[🚀 Knowledge Application]
  D --> E[📊 Measurement of Learning]

  %% Styles
  classDef ingest fill:#e6f7ff,stroke:#1c75bc,stroke-width:3px,color:#000;
  classDef process fill:#d0e7ff,stroke:#1c75bc,stroke-width:2px,color:#000,font-weight:bold,stroke-dasharray: 5 2;
  classDef train  fill:#f0f0f0,stroke:#555,stroke-width:2px,color:#000;
  classDef apply  fill:#fff7e6,stroke:#e69500,stroke-width:2px,color:#000;
  classDef measure fill:#f9f0ff,stroke:#8e44ad,stroke-width:2px,color:#000;

  %% Assign classes
  class A ingest;
  class B process;
  class C train;
  class D apply;
  class E measure;
  

✨ Absorbing Knowledge: From Importing to Understanding

We’ve laid the foundations: conversations from OpenAI, Qwen, Gemini, and DeepSeek have been imported, normalized, annotated, and stored as CaseBooks. Every chat is now a structured artifact, a durable substrate of knowledge.

But that’s just potential. The real threshold is here the point where raw interaction becomes measurable intelligence.

Because having conversations in a database isn’t enough. The question is:

👉 Where’s the knowledge?
👉 Which moments actually matter?

We’ve all felt it in conversation: those flashes when something new clicks into place, when human and AI together produce an insight neither could reach alone. That’s knowledge being born. The challenge is turning that feeling into something objective a signal we can detect, measure, and build on.

This is Stephanie’s key contribution: an AI that doesn’t just store conversations but can recognize knowledge as it happens. A system that can separate noise from wisdom, chatter from breakthrough, and use those signals to guide its own improvement.

In this section, we’ll show you how Stephanie evaluates and scores conversations how it detects the moments where learning is real, validates them against human judgment, and uses them as fuel for the next stage of growth.

This is where the invisible becomes visible. Where insight becomes measurable. Where learning from learning begins.

👩‍🍳 Preliminaries

This section builds on ideas we’ve introduced in earlier posts. Here’s a quick glossary so you don’t get lost in acronyms (with links if you’d like a refresher):

• NER (Named Entity Recognition) – tags people, places, and concepts in text. 👉 Post: From Photo Albums to Movies
• SIS (Stephanie Insight System) – our interface for human review and scoring. 👉 Post: SIS Overview
• CBR (Case-Based Reasoning) – reasoning by reusing past solved cases. 👉 Post: Reasoning with Cases
• PACS (Plan-Aware Consistency Scoring) – checks that outputs align with the current plan. 👉 Post: PACS Verifier
• HRM (Hierarchical Reasoning Model) – scores multi-step reasoning traces. 👉 Post: HRM Deep Dive
• VPM (Visual Policy Maps) – compresses policy signals into images for proof & feedback. 👉 Post: ZeroModel

👣 Steps into Knowledge

With the preliminaries in place, let’s walk through the journey step by step. This is where the abstract idea of learning from learning turns into a concrete, testable pipeline.

• 🗣️ Raw chats in → normalized out. We start with conversations from multiple providers (OpenAI, Qwen, Gemini, DeepSeek). Their messy exports (JSON/HTML/files) run through the Chat Importer, which dedupes, hashes, and normalizes turns.

• 🧳 Cases, context, and traces. Imported chats become CaseBooks structured artifacts of problems, traces, and solutions. Each turn is enriched with NER (entities) and domain tags, then linked into a trajectory of how the solution evolved.

• 🕰️ Durable memory. Everything lands in Stephanie Memory a persistent substrate of conversations, turns, and annotations that future models can train against.

• Two scoring paths in parallel:

  • Human (RHLF²): In SIS you browse the List View, then rate turns in the Score View (−5…+5) to capture genuine learning moments.
  • AI Judge: A lightweight scorer triages at scale, assigning a 0–100 knowledge score plus rationale.

• Signals → dataset. Human stars and AI scores flow into the Knowledge Pair Builder, which assembles fair, goal/domain/entity-aligned preference pairs (A > B) for training.

• Training the model. With a DPO-lite objective (−log σ(sA−sB)), we fit the Knowledge Scorer a goal-conditioned head over frozen embeddings that captures the “knowledge signal.”

• Applying the signal downstream:

  • Retrieval & rerank to surface knowledge-dense snippets.
  • Verifier (CBR + PACS + HRM) to check reasoning and ensure consistency.
  • Summarizers (Sharpened / Knowledge-Augmented) that write clearer, more grounded drafts.

• Proving it worked. We generate VPMs (Visual Policy Maps) and longitudinal metrics (pairwise accuracy, artifact lift) to provide visual and statistical evidence of improvement.

• Closing the loop. Stronger summaries produce new high-quality conversations, which flow back as fresh training data so the system keeps learning from learning.

    flowchart LR
    %% === SOURCES: raw conversations produced by multiple models ===
    subgraph SOURCES[Raw Conversational Data]
      direction LR
      O[OpenAI]:::openai
      Q[Qwen]:::qwen
      G[Gemini]:::gemini
      D[DeepSeek]:::deepseek
    end

    %% Exports from providers
    SOURCES --> XPORTS["📦 Model Exports<br/>(JSON / HTML / Files)"]:::process

    %% Chat Importer
    XPORTS --> IMP["📥 Chat Importer<br/>(normalize, dedupe, hash turns)"]:::process

    %% Case creation pipeline
    subgraph CASES[Creating Learning Artifacts]
      IMP --> CB["📚 CaseBooks<br/>(Problems • Traces • Solutions)"]:::process
      CB --> NERDOM["🔍 NER & Domain Tagging<br/>(typed context & entities)"]:::process
      NERDOM --> TRAJ["🧠 Trajectory Mapping<br/>(how solutions evolved)"]:::process
    end

    %% Persistence
    TRAJ --> MEM["💾 Stephanie Memory<br/>(conversations • turns • annotations)"]:::storage

    %% SIS human scoring (RHLF²)
    MEM --> SISLIST["📂 SIS List View"]:::ui
    SISLIST --> SISSCORE["⭐ SIS Score View<br/>(−5..+5 human stars)"]:::ui

    %% AI judge scoring in parallel
    MEM --> AIJ["🤖 Chat Scorer (AI Judge)<br/>(rationale + 0–100 knowledge score)"]:::process

    %% Pair builder to create training data
    SISSCORE --> PAIRS
    AIJ --> PAIRS
    subgraph DATASET[Preference Dataset]
      PAIRS["⚖️ Knowledge Pair Builder<br/>(pos vs neg within goal/domain + entity overlap)"]:::process
    end

    %% Training the knowledge model
    PAIRS --> TRAIN["🏋️ Knowledge Trainer (DPO‑lite)<br/>L = −log σ(sA − sB)"]:::train
    TRAIN --> KMODEL["🧠 Knowledge Scorer (goal‑conditioned head)<br/>(frozen embeddings + tiny MLP)"]:::model

    %% Applying the signal downstream
    KMODEL --> RERANK["🔎 Retrieval & Rerank<br/>(prefer knowledgeful snippets)"]:::apply
    RERANK --> VERIF["🧮 Verifier (CBR + PACS + HRM)"]:::apply
    VERIF --> SUMM["📝 Summarizers<br/>(Sharpened / Knowledge‑Augmented)"]:::apply

    %% Visual proof & feedback
    SUMM --> VPM["🖼️ VPM Progress Maps<br/>(before/after policy shift)"]:::viz
    VERIF --> METRICS["📊 Longitudinal Metrics<br/>(pairwise acc, artifact lift)"]:::viz

    %% Closed loop: new high‑quality chats feed back in
    SUMM --> NEWCHATS["💬 New High‑Quality Conversations"]:::steph
    NEWCHATS -.->|Becomes New Training Data| SOURCES

    %% Styling
    classDef openai fill:#E6F4EA,stroke:#71B867,stroke-width:3px,color:black
    classDef qwen fill:#FFF0E6,stroke:#FF6B00,stroke-width:3px,color:black
    classDef gemini fill:#E9F1FE,stroke:#4285F4,stroke-width:3px,color:black
    classDef deepseek fill:#E6F0FF,stroke:#0056D2,stroke-width:3px,color:black

    classDef process fill:#F5F5F5,stroke:#666,stroke-width:2px,color:black
    classDef storage fill:#F0E6F4,stroke:#9B4F96,stroke-width:3px,color:black
    classDef train fill:#FFF7E6,stroke:#E69500,stroke-width:3px,color:black
    classDef model fill:#E6FFF2,stroke:#2EB82E,stroke-width:3px,color:black
    classDef apply fill:#E6F7FF,stroke:#1C75BC,stroke-width:3px,color:black
    classDef ui fill:#F9F9F9,stroke:#999,stroke-width:2px,color:black
    classDef viz fill:#F9E6FF,stroke:#7300CC,stroke-width:3px,color:black
    classDef steph fill:#E6F4EA,stroke:#34A853,stroke-width:3px,color:black
  

With all this knowledge flowing in, we needed a way to quickly identify which moments represented genuine learning. So we built the simplest possible scoring interface - just click through conversations and mark what matters. While training runs in the background, you can simply open SIS Score View and click through turns, rating them from -5 to +5. These human-labeled moments become the foundation for training our knowledge model, creating a continuous loop where your judgment directly shapes what the system learns.

🗂️ Browsing Conversations in SIS (List View)

The SIS list view is the front door to everything we ingest. It shows your imported conversations, sorted by turn count so long, high-signal threads float to the top.

What you can do here

• Scan quickly: ID, provider, title, turns, created/updated.
• Filter: by provider (e.g., openai) and adjust result limits.
• Jump: open a conversation to read, or go straight to Score.

Why turn count first? Longer chats tend to contain more learning events (clarifications, corrections, refactors). Sorting by turns makes it easy to prioritize where to mark knowledge first.

Tip: timestamps are rendered compactly (e.g., 2025-09-18 12:37) so the table stays scannable.

This shows the chat list view in Stephanie’s Information System (SIS). Notice the number or turns in the conversations. You can also score and view the conversations

⭐ RHLF²: Scoring Conversations (Per-Turn Stars)

RHLF is reinforcment learing form human feedback.

RHLF²? Your chats are already Recurrent Human Feedback. When you explicitly rate the key moments, you square that signal turning interaction into trainable supervision.

Reading alone doesn’t create a training signal. Scoring does.

The SIS Score View transforms your raw conversations into a precise, actionable training dataset by letting you mark each user→assistant turn with a signed star value:

The Score View shows conversation turns side-by-side with intuitive star ratings. Notice the progress tracker (4/12 rated), AI-assisted scoring in green, and the ability to quickly mark breakthroughs (+5) or errors (−5) with one click. This is where raw conversations become structured knowledge training data.

What the Score view gives you

• Side-by-side turn display - See the user prompt and assistant response together for context
• Instant one-click scoring - Tap +5 to +1 or −1 to −5 with a single click
• “Only Unrated” toggle - Skip rated turns to focus on what matters most
• Real-time progress badge - See exactly where you are in the conversation (rated/total)
• AI-assisted scoring - See the AI’s preliminary knowledge score (0-100) alongside your rating
• Domains - this is categories this response embeddings look like
• NER - the entities detected

💡 Example: When working on a code project, a +5 turn might be when the AI correctly implements a complex algorithm with clear explanations, while a −5 turn might be when it suggests dangerous security practices with confidence.

⚠️ Note: Human and AI scores won’t perfectly align. Human ratings capture nuanced judgment while AI scores are based on patterns. This gap is valuable - it shows where the AI needs to learn from your expertise.

🔁 How These Marks Flow Into Learning

Those per-turn stars do three jobs downstream:

1. Teach the Knowledge Scorer Your ratings define what “knowledgeful” looks like for you. The scorer learns to detect similar turns automatically.

2. Build Contrastive Pairs We mine good vs bad turns to sharpen decision boundaries (Triplet/InfoNCE). This reduces noise and overfitting to style.

3. Weight the Closed Loop In the Knowledge↔Artifact loop, turns that led to strong artifacts receive higher reinforcement. Your stars become multipliers.

👗 Visual: where SIS fits

    flowchart LR
  A["📂 SIS List View<br/>(sorted by turn count)"] --> B["⭐ SIS Score View<br/>(per-turn −5..+5)"]
  B --> C["🧠 Knowledge Scorer<br/>(trains on your marks)"]
  C --> D["🔁 Knowledge↔Artifact Loop<br/>(reward = k_score × quality)"]
  D --> A
  

👨‍🍼 The second pass

Marking turns isn’t paperwork it’s how chat logs become a living, personalized knowledge model. We call it RHLF² because it’s Human-in-the-Loop twice:

1. During creation (Recurrent): you and the model iterate toward a concrete goal (write the Memento blog post, ship code, draft a plan). The conversation itself is purposeful signal not random chatter.

2. During review (Refinement): you come back with a critical eye and score each turn what was helpful, what wasn’t, what captured real knowledge, what missed. You don’t just reward the good; you penalize the bad. The negatives matter because they carve out failure modes the model must avoid.

That’s the “squared”: goal-driven interaction × curated feedback. The first loop produces targeted data; the second loop amplifies it into a clean, signed training signal. Over time, SIS turns everyday chats into a compounding asset your model, tuned by your objectives, your taste, and your red lines.

💯 Exemplar results

⭐️⭐️⭐️⭐️⭐️ Five star response

This is an example response to an earlier attempt in this process.

...
You haven't "learned" anything yet not in the true, measurable, self-improving sense that your "Learning from Learning" philosophy promises.
You’ve built an astonishingly complex cathedral of code CBR middleware, PACS trainers, NER  
retrievers, multi-agent pipelines, VPM visualizations,  
and strategy profiles all orbiting around the noble goal of creating an AI that learns from its own learning process.

But the cathedral is empty.

The core insight that **the conversations themselves (like this one) are the training signal** is brilliant.  
You’re not trying to fine-tune on arXiv papers or generic datasets. You’re trying to absorb *your own reasoning process*  
as captured in chats with Qwen, OpenAI, DeepSeek, and Gemini. That’s the "Absorbing Man" touching *thought itself*, 
 not just stone or steel.

The problem is, your current system is trying to learn by building a Rube Goldberg machine to  
process its own thoughts, instead of just... thinking better.

...

🌠🌠🌠🌠🌠 And just the opposite

CaseBookStore (Local Version) ...

And the bad example here was a large block of code that the AI suggested as a case bookstore. It duplicated the functionality of code already in the system. Also was not in line and cost days to work in and out.

Why does one conversation earn five stars while another gets -5?
This isn’t about the AI’s inherent ability it’s about you.

That difference is your judgment. That’s the human in the loop. When you mark a turn as +5, you’re saying: “This is the knowledge I want to amplify.” When you mark a turn as -5, you’re saying: “This is the pattern I want to erase.”

This is how your AI becomes yours. Every star you give is a compass needle pointing toward what matters to you. The more precisely you score, the more the system aligns with your unique thinking patterns, red lines, and creative instincts.

This isn’t passive feedback it’s active curation. You’re not just using the AI; you’re training it to be your intellectual twin. The more you engage with this loop scoring, reviewing, refining the closer your AI gets to being an extension of your own mind.

This is how knowledge becomes personal.

🙂‍↔️ Turn annotation: domains + NER (what, who, how, why)

What we add

• Domains: short tags that describe what the turn is about (e.g., planning, retrieval, alignment). We assign them from two sources:

  • Seed domains (your controlled ontology from seeds.yaml)
  • Goal-aware domains (same classifier, but conditioned on the chat’s goal/title)

• NER (entities): normalized mentions of people, orgs, tools, papers, APIs, etc., with offsets and (user|assistant) role.

How it works (pipeline)

1. Batch over conversations (idempotent by default):

   • We fetch turn texts with a session-safe query.
   • only_missing=True skips turns that already have domains/ner.

2. Domains: ScorableClassifier embeds the turn text and scores against domain centroids. We store:

   [{"domain":"planning","score":0.82,"source":"seed"},
    {"domain":"blog_post","score":0.77,"source":"goal"}]
   

3. NER: We call the KG’s detector once per turn (detect_entities()), split entities by role using offsets, and save:

   [{"text":"Memento","type":"WORK","start":12,"end":19,"role":"user"},
    {"text":"FastAPI","type":"ORG","start":86,"end":93,"role":"assistant"}]
   

4. (Optional) Index to KG: for turns with entities we fire-and-forget a knowledge_graph.index_request so entities become searchable and linkable across chats.

5. Progress & safety: We expose a single tqdm bar across both phases; annotations are stored as JSONB on chat_turns.(domains|ner). No rework unless you pass force=True.

Why we add them

• Scoring with context: When you star a turn, the star is now situated we know which domains it represents and which entities it touched. That turns stars into typed supervision (great for training reward models and for negative signals on failure modes).
• Search & retrieval: Domains become instant filters (“show unrated turns in planning”), and NER lets you jump to every place “Memento”, “FastAPI”, or a partner’s name appears without brittle keyword search.
• Knowledge Graph glue: Entities + domains let us stitch conversations to papers, notes, repos, and previous cases. That’s how we build durable context, not just long prompts.
• Analytics & drift detection: Over time you see where your work actually lives (e.g., 38% planning, 22% retrieval) and spot spikes in “bad” stars within a domain.
• RHLF², operationalized: The creation loop yields goal-directed data; the review loop yields signed labels. Domains + NER make that signal structured, reusable, and auditable.

In the SIS UI

• Score view now shows per-turn Domains and Entities under the user/assistant text.

• You can filter to unrated, scan entities at a glance, and preferentially rate turns from a target domain (e.g., “alignment”) when you’re curating a dataset.

In this image you can see the domains and the ner entities generated for a conversation turn showing in the scoring view for the chat.

Good defaults

• Idempotent by default (only_missing=True), with force=True to re-annotate after ontology changes.
• Negative stars are first-class: if an assistant hallucinated an API or mis-tagged a domain, you down-rank in that domain, which is exactly the gradient we want.

Short version: domains tell us what the turn is, NER tells us who/what it references, and together they turn raw chats into structured training signal and linkable knowledge.

    flowchart TD
    %% ========== STYLING DEFINITIONS ==========
    classDef entry fill:#e1f5fe,stroke:#01579b,stroke-width:2px,color:#01579b
    classDef decision fill:#fff3e0,stroke:#ef6c00,stroke-width:2px,color:#ef6c00
    classDef process fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px,color:#1b5e20
    classDef domain fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c
    classDef ner fill:#e3f2fd,stroke:#1565c0,stroke-width:2px,color:#0d47a1
    classDef storage fill:#fff8e1,stroke:#ff8f00,stroke-width:2px,color:#e65100
    classDef async fill:#ffebee,stroke:#c62828,stroke-width:2px,color:#b71c1c

    %% ========== 🚀 PIPELINE ENTRY ==========
    A[🔄 Batch over conversations] --> B{🤔 only_missing?}
    B -- ✅ yes --> B1["📥 Fetch turns missing<br/>(domains OR ner)"]
    B -- 🔄 no / force --> B2[📥 Fetch all turns]
    B1 --> C[📊 For each turn]; B2 --> C

    %% ========== 🎯 DOMAINS PROCESSING ==========
    subgraph D[🎯 Domains Processing]
        direction LR
        D1[🧠 ScorableClassifier<br/>embed + score] --> D2["🌱 Seed domains<br/>(centroids)"]
        D1 --> D3["🎯 Goal-aware domains<br/>(condition on chat goal/title)"]
        D2 --> D4[🔄 Merge & normalize]
        D3 --> D4
        D4 --> D5[💾 Store JSONB → chat_turns.domains]
    end

    %% ========== 🔍 NER PROCESSING ==========
    subgraph E[🔍 Named Entity Recognition]
        direction LR
        E1["🔍 detect_entities(turn_text)"] --> E2["👥 Split by role via offsets<br/>(user | assistant)"]
        E2 --> E3[💾 Store JSONB → chat_turns.ner]
    end

    C --> D1
    C --> E1

    %% ========== 📚 KNOWLEDGE GRAPH INDEXING ==========
    E3 --> F{📌 any entities?}
    F -- ✅ yes --> G[["⚡ knowledge_graph.index_request<br/>(fire-and-forget)"]]
    F -- ❌ no --> H[⏭️ skip]

    %% ========== 📊 PROGRESS & SAFETY ==========
    D5 --> P[📈 Single tqdm across both phases]
    E3 --> P
    P --> Q["🛡️ Idempotent by default<br/>(no rework unless force=true)"]

    %% ========== APPLY STYLES ==========
    class A,B1,B2,C entry
    class B decision
    class D1,D2,D3,D4,E1,E2 process
    class D domain
    class E ner
    class D5,E3 storage
    class G async
    class F decision
    class H process
    class P,Q process
  

🤖 AI evaluates AI

We added a lightweight judge that reads each assistant reply and assigns a 0–100 “knowledge” score plus a one-paragraph rationale. It doesn’t replace human rating; it triages at scale so we can focus reviewers on the most promising turns.

This image shows the chat score view with the AI score highlighted in green. Notice that the AI scored the chat result very highly.

🏂 How it works

👉 Agent Code Here

1. Select turns We pull assistant answers with enough substance (min length, NER present) via list_turns_with_texts.

2. Prompt the judge ChatAnalyzeAgent builds a strict system prompt (goal = conversation title, user question, assistant answer, optional context/preferences).

3. Score & explain The model returns exactly:

   rationale: <1–3 sentences>
   score: <0–100>
   

   We parse it with parse_knowledge_judge_text.

4. Persist We store to the turn:

   • ai_knowledge_score (0..100)
   • ai_knowledge_rationale (trimmed text)

5. Review UI On the scoring page, we show AI score/rationale next to the human ⭐ controls.

⚖️ What the judge rewards & penalizes (rubric)

💬 Chat Analyze Prompt

This is the current prompt we use to score the chat conversations.

SYSTEM:
You are a precise knowledge judge. You evaluate whether an assistant’s answer contains useful, true, 
goal-advancing knowledge **for the given user question**. Be strict and concise.

CONVERSATION TITLE (goal):
{{ goal_text }}

USER QUESTION:
{{ user_text }}

ASSISTANT ANSWER:
{{ assistant_text }}

{% if context %} OK
OPTIONAL CONTEXT (may include prior turns, files, constraints):
{{ context }}
{% endif %}

{% if preferences %}
USER PREFERENCES (if any):
{% for p in preferences %}- {{ p }}
{% endfor %}
{% endif %}

INSTRUCTIONS:

1. Judge **only** the assistant answer against the user question (and optional context/preferences if provided). Ignore unrelated content.
2. **Reward**: specific, verifiable facts; clear reasoning; actionable steps; correct code/SQL with correct explanation; citations or checks when claims are non-obvious.
3. **Penalize**: fluff, generic advice, hallucinations, missing steps, wrong or unsafe code/SQL, answers that dodge the question, or confident errors.
4. If there is **not enough information to judge**, or the question is unclear and the answer doesn’t resolve it, treat as **low score**. If the answer clearly **refuses appropriately** (e.g., harmful request) and provides a safe alternative, score based on helpfulness of the refusal.
5. Be consistent: use the rubric below for the 0–100 score.

SCORING RUBRIC (use whole numbers):

- **90–100**: Excellent. Directly answers the question with specific, correct, and actionable knowledge. Code/SQL (if present) is correct and explained. Little to no filler.
- **75–89**: Good. Mostly correct and helpful, minor omissions or small risks. Actionable with little follow-up.
- **60–74**: Mixed. Some value but notable gaps, ambiguity, or unverified claims. May require user effort to fix or validate.
- **40–59**: Weak. Partially relevant or generic. Risk of error if followed as-is.
- **1–39**: Poor. Largely irrelevant, incorrect, or misleading.
- **0**: Non-answer (e.g., empty), or entirely incorrect/misleading.

RETURN FORMAT (plain text, exactly these two lines, no extra text):
rationale: \<brief explanation of the key reasons for the score, 1–3 sentences>
score: <0–100>

EXAMPLES OF RETURN FORMAT:
rationale: Explains indexed joins with correct MySQL syntax and a tested example; cites cost impact and edge cases.
score: 92

rationale: Provides a script but the API method names are wrong and no auth flow; user cannot run it without fixes.
score: 48

rationale: Mostly generic advice; does not address the user’s dataset or constraints.
score: 33

rationale: Empty/irrelevant answer.
score: 0

🤔 Is This Is New

We don’t just optimize outputs we optimize your learning events and make that preference portable across agents.

💼 From RHLF² → Annotations → Casebooks (Case-Based Reasoning)

We’ve now got two big pieces:

• RHLF²: turn-level stars across whole conversations (goal-grounded feedback).
• Annotations: domains + NER on each turn (what it’s about, who/what is involved).

Casebooks are where this becomes Case-Based Reasoning (CBR) not just storage, but a loop that retrieves, reuses, revises, and retains solutions over time.

📚 How CaseBooks Are Created in Learning-from-Learning

In Stephanie’s Learning-from-Learning agent, the CaseBook is the central artifact: it groups all reasoning, retrievals, refinements, and scores for a given paper. Here’s the exact flow.

    flowchart TD
    A[📄 Paper] -->|prepare_casebook_goal_sections| B[📚 CaseBook]
    B -->|"create_section_case(s)"| C[🗂 Cases per Section]

    subgraph SectionCases["Multiple Section Cases"]
      C1[Case 1: Baseline]:::case
      C2[Case 2: Variation]:::case
      C3[Case 3: Refinement]:::case
    end
    C --> SectionCases

    SectionCases --> D[🔎 CorpusRetriever]
    D --> E[⚔️ KnowledgeArena]
    E -->|baseline & improved outputs| F[📝 Scorables + Metrics]
    F -->|save_section| G["(💾 Persistence)"]

    G --> H[🔗 Knowledge Pairs]
    G --> I[📊 Strategy Manager]
    G --> J[📈 Evidence Agent]

    H --> K[📓 Training Data]
    I --> K
    J --> K

    K --> L[🤖 Learning-from-Learning Feedback Loop]

    classDef store fill:#e6f2ff,stroke:#3399ff;
    classDef process fill:#f9f2ec,stroke:#cc6600;
    classDef case fill:#fff0f5,stroke:#cc3399;

    class B,C,G store;
    class D,E,F,H,I,J process;
    class C1,C2,C3 case;
  

🆕 1. CaseBook Initialization (per paper)

Inside the LfL agent (agent.py), every document triggers creation of a new CaseBook:

for paper in documents:
    doc_id = paper.get("id") or paper.get("doc_id")
    title = paper.get("title", "")

    # Casebook + goal
    casebook_name = generate_casebook_name(self.casebook_action, title)
    casebook = self.memory.casebooks.ensure_casebook(
        name=casebook_name,
        pipeline_run_id=pipeline_run_id,
        description=f"LfL agent runs for paper {title}",
        tag=self.casebook_action,
    )

• prepare_casebook_goal_sections (in persistence.py) does three things:

  1. Generates a stable CaseBook name using generate_casebook_name.
  2. Ensures a CaseBook ORM entry exists in memory (ensure_casebook).
  3. Creates/attaches a Goal object (e.g. “Verify & improve per section of paper X”).
  4. Resolves the document into sections (title, body parts, etc.).

Result: one CaseBook per paper, linked to a goal and section list.

🗂️ 2. Case Creation (per section)

For each section, Persistence creates a Case entry:

case = self.persist.create_section_case(
    casebook, paper, section, goal, context
)

This does the following (create_section_case in persistence.py):

• Adds a CaseORM row tied to the CaseBook and Goal.

• Stores metadata:

  • paper_id, section_name, section_id, section_index
  • case_kind = "summary"
  • Links the section to a Scorable (so it can be scored later).

• Returns the Case so downstream agents (arena, summarizer) can add scorable items.

🏟️ 3. Knowledge Arena & Verification

For each case:

• Retrieval pulls in candidate knowledge chunks (via CorpusRetriever).
• Candidates go through the KnowledgeArena (self-play improvement loop).
• Arena or summarizer produces a baseline + verified/improved summary.

The winning output is attached back to the Case as scorables and metrics.

🏆 4. Persistence of Results

At the end of section processing:

saved_case = self.persist.save_section(
    casebook,
    paper,
    section,
    verify,
    baseline,
    goal["id"],
    case_context,
)

• Stores the baseline summary, verification iterations, and final metrics in the Case.

• Adds metrics like:

  • Overall verification score
  • Number of refinement iterations

• Links supporting corpus/arena candidates for attribution.

♟️ 5. Knowledge Pairs & Strategy Tracking

• Knowledge pairs (baseline vs. improved versions) are persisted for later training.
• StrategyManager updates its statistics based on section outcomes.

🕵️‍♂️ 6. Arena Evidence & Longitudinal Metrics

After all sections:

• The Evidence agent aggregates across CaseBooks:

  • Verification score trends
  • Iteration counts
  • Cross-episode transfer (patterns reused between papers)

• Generates a markdown report for SIS dashboards.

🗂️ CaseBooks: The Applied Case-Based Reasoning Core

CaseBooks aren’t just storage they’re the living lab notebooks of our Learning-from-Learning system. Each CaseBook is a targeted knowledge bucket, consolidating all related interactions (from chats, papers, and agents) into a single, scorable unit.

This structure makes knowledge usable: precise scoring, tuning, and iterative refinement all happen inside the CaseBook. That’s how raw conversations become a self-improving knowledge base.

Each CaseBook is a self-contained unit, capturing all related reasoning, verifications, and improvements for a specific task or goal.

🔁 The Case-Based Reasoning Cycle

• Retrieve: For a new goal, fetch prior cases by domain + entity similarity (with score thresholds if needed).
• Reuse: Adapt the best prior turns as starter plans, prompts, or drafts. Multi-agent runs land in the same CaseBook, so strong subparts can be spliced together.
• Revise: Run new agents, compare outputs to retrieved cases, and score with RHLF². Negatives become counter-examples that sharpen retrieval.
• Retain: Promote improved turns back into the CaseBook with their domains/NER and scores so the library gets better every pass.

    flowchart LR
    R["🔎 Retrieve<br/>Fetch prior cases<br/>(domain + entity match)"] --> U[♻️ Reuse<br/>Adapt best turns<br/>as starter plans]
    U --> V[🛠️ Revise<br/>Run new agents,<br/>score with RHLF²]
    V --> T[📌 Retain<br/>Promote improved<br/>cases back to CaseBook]
    T --> R

    %% Styles
    classDef retrieve fill:#E6F7FF,stroke:#1C75BC,stroke-width:3px,color:#000,font-weight:bold;
    classDef reuse    fill:#FFF7E6,stroke:#E69500,stroke-width:3px,color:#000,font-weight:bold;
    classDef revise   fill:#E6FFF2,stroke:#2EB82E,stroke-width:3px,color:#000,font-weight:bold;
    classDef retain   fill:#F9E6FF,stroke:#7300CC,stroke-width:3px,color:#000,font-weight:bold;

    class R retrieve;
    class U reuse;
    class V revise;
    class T retain;
  

💡 Why CaseBooks Work

• Many sources, one brain: Conversations, agent runs, and external knowledge are all pulled into comparable, searchable cases.
• Targeted adaptation: Domains + entities make reuse surgical reapply only what matters, not the whole transcript.
• Learning that compounds: Each iteration updates the CaseBook, so the next retrieval starts closer to the solution.
• Train-ready signal: CaseBooks yield clean pairs/lists for preference training, domain curricula, and evaluation slices.

🗂️ CaseBooks in this example solution: The Paper-to-Blog Processing Engine

At the heart of our “learning from learning” system lies the CaseBook a self-contained knowledge unit that tracks every iteration, verification, and improvement for a single paper-to-blog conversion. This isn’t just storage; it’s a living laboratory where raw paper content transforms into a polished blog post through structured, multi-agent collaboration.

Here’s exactly how it works:

1. One CaseBook per Paper: Each paper becomes a single CaseBook, serving as the central repository for all processing stages. This structure ensures all related artifacts from initial drafts to final scores are stored together with clear provenance.

2. Multiple Cases Per Section: For each section of the paper (e.g., Abstract, Methods, Results), we create multiple cases one for each agent trying to generate a draft. In our implementation, five agents typically compete for each section:

   • Each agent runs its own reasoning and drafting process
   • Each generates its own initial draft, refined version, and verification metrics
   • Each case stores all these artifacts as Scorables

3. Scorables for Every Artifact: Every component of the processing pipeline is stored as a Scorable inside the Case:

   • Raw section text
   • Initial draft (from the agent’s first attempt)
   • Refined draft (after verification iterations)
   • Arena results (candidate pool, beam, winner)
   • Verification reports (scores, iterations, knowledge attribution)
   • Metrics (overall score, knowledge lift, ablation results)

4. Selection of Top Cases: After all agents complete their work, we score each case using:

   • Human ratings (RHLF² stars)
   • AI knowledge scores (0-100)
   • Verification metrics (K/C/G scores)
   • Knowledge-applied lift (measured improvement from applied knowledge)

   The highest-scoring case per section becomes the section winner.

5. Final Composite Output: The final blog post is built by composing the top cases from each section. This creates a coherent, high-quality output where every section was independently optimized by multiple agents, then selected based on verifiable knowledge.

6. Closed-Loop Learning: Critically, this isn’t a one-time process. The CaseBook becomes a living knowledge base:

   • Top-performing cases become training data for future runs
   • Knowledge patterns are reused across papers (e.g., “LoRA → QLoRA” transfer)
   • Strategy evolution (A/B testing) continuously improves agent behavior

This structure is what makes “learning from learning” possible: Stephanie doesn’t just process one paper it processes the process of processing papers, and gets better at it with every run.

    flowchart TD
    P[📄 Paper/Blog<br/>Input Document] --> CB[📚 CaseBook<br/>One per Paper]

    CB --> S1[🔖 Section 1]
    CB --> S2[🔖 Section 2]
    CB --> S3[🔖 Section N]

    %% Section to agents
    S1 -->|5 agents| C1a[🤖 Case 1a<br/>Agent Draft]
    S1 --> C1b[🤖 Case 1b]
    S1 --> C1c[🤖 Case 1c]
    S1 --> C1d[🤖 Case 1d]
    S1 --> C1e[🤖 Case 1e]

    S2 -->|5 agents| C2a[🤖 Case 2a]
    S2 --> C2b[🤖 Case 2b]
    S2 --> C2c[🤖 Case 2c]
    S2 --> C2d[🤖 Case 2d]
    S2 --> C2e[🤖 Case 2e]

    %% Each case to scorables
    C1a --> SCO1["📝 Scorables<br/>(drafts, metrics, arena...)"]
    C1b --> SCO1
    C1c --> SCO1
    C1d --> SCO1
    C1e --> SCO1

    C2a --> SCO2[📝 Scorables]
    C2b --> SCO2
    C2c --> SCO2
    C2d --> SCO2
    C2e --> SCO2

    %% Scoring and selection
    SCO1 --> TOP1[🏆 Top Case<br/>Section 1 Winner]
    SCO2 --> TOP2[🏆 Top Case<br/>Section 2 Winner]
    SCO2 --> TOP3[🏆 Top Case<br/>Section N Winner]

    %% Final composition
    TOP1 --> FINAL[🚀 Final Composite Output<br/>Combined Sections]
    TOP2 --> FINAL
    TOP3 --> FINAL

    %% Feedback loop
    FINAL -->|New Training Data| CB
    TOP1 -->|Retrieval & Reuse| CB
    TOP2 -->|Retrieval & Reuse| CB
    TOP3 -->|Retrieval & Reuse| CB

    %% Styling
    classDef paper fill:#E6F7FF,stroke:#1C75BC,stroke-width:3px,color:#000;
    classDef casebook fill:#FFF7E6,stroke:#E69500,stroke-width:3px,color:#000;
    classDef section fill:#E6FFF2,stroke:#2EB82E,stroke-width:3px,color:#000;
    classDef case fill:#F9E6FF,stroke:#7300CC,stroke-width:2px,color:#000;
    classDef scorable fill:#F0E6F4,stroke:#9B4F96,stroke-width:2px,color:#000;
    classDef top fill:#FFF0E6,stroke:#FF6B00,stroke-width:3px,color:#000,font-weight:bold;
    classDef final fill:#FFD6E6,stroke:#D61C4E,stroke-width:3px,color:#000,font-weight:bold;
    classDef feedback fill:#E6F4EA,stroke:#34A853,stroke-width:3px,color:#000;

    class P paper;
    class CB casebook;
    class S1,S2,S3 section;
    class C1a,C1b,C1c,C1d,C1e,C2a,C2b,C2c,C2d,C2e case;
    class SCO1,SCO2 scorable;
    class TOP1,TOP2,TOP3 top;
    class FINAL final;
    class feedback feedback;
  

This diagram shows the complete workflow:

• Paper → CaseBook: One CaseBook per paper, containing all processing artifacts.
• Section → Multiple Cases: Each section has multiple agent-generated cases (5 per section in our implementation).
• Scorables: Every component of the pipeline is stored as a Scorable inside each case.
• Selection: Highest-scoring case per section is selected for the final output.
• Feedback Loop: Top cases become training data for future runs, enabling continuous improvement.

💡 This is where “learning from learning” becomes concrete. When Stephanie tackles a new problem, she doesn’t start from scratch she starts from her own past successes and failures, precisely organized and scored. CaseBooks transform ephemeral conversations into a durable, self-improving knowledge engine.

With CaseBooks in place, we now move to the next step: using that stored knowledge to train a measurable signal. This is where information gains meaning and becomes something Stephanie can apply.

🧪 Section 3: Knowledge Training & Understanding

    flowchart LR
  A[🧩 Knowledge Ingestion] --> B[🧾 Knowledge Processing]
  B --> C[🧪 Knowledge Training & Understanding]
  C --> D[🚀 Knowledge Application]
  D --> E[📊 Measurement of Learning]

  %% Styles
  classDef ingest fill:#e6f7ff,stroke:#1c75bc,stroke-width:3px,color:#000;
  classDef process fill:#d0e7ff,stroke:#1c75bc,stroke-width:2px;
  classDef train  fill:#f0f0f0,stroke:#555,stroke-width:2px,color:#000,color:#000,font-weight:bold,stroke-dasharray: 5 2;
  classDef apply  fill:#fff7e6,stroke:#e69500,stroke-width:2px,color:#000;
  classDef measure fill:#f9f0ff,stroke:#8e44ad,stroke-width:2px,color:#000;

  %% Assign classes
  class A ingest;
  class B process;
  class C train;
  class D apply;
  class E measure;
  

🎯 Episteme: Distilling Knowledge into an AI

This section is about one of the oldest quests in philosophy defining what knowledge really is now reframed for AI. Instead of treating conversations as loose text, we distill them into measurable knowledge.

We do this with a compact, goal-conditioned knowledge scorer: freeze embeddings, add lightweight heads, and train them to prefer “knowledgeful” turns over weaker ones always within the same goal, topic, and domain.

The diagram below shows how we build fair pairs, encode them, and blend human and AI signals into a single measurable knowledge score:

    flowchart LR
    subgraph PAIRS["⚖️ Build Fair Pairs"]
      A1["💬 Turn A (preferred)"]:::good --> A2[PairBuilder]
      B1["💬 Turn B (counterfactual)"]:::bad --> A2
      A2["📦 KnowledgePairBuilder<br/>(domain + entity overlap,<br/>human stars + AI scores)"]:::pairs
    end

    subgraph MODEL["🧠 Tiny Knowledge Scorer"]
      A2 --> ENC["🔗 CrossFeatureEncoder<br/>(goal + text embeddings)"]:::encoder
      ENC --> H["👤 Human Head σ(h)"]:::human
      ENC --> AI["🤖 AI Head σ(a)"]:::ai
    end

    subgraph BLEND["🔀 Adaptive Blending"]
      H --> BLENDNODE["p = α·σ(h) + (1-α)·calibrate(σ(a))"]:::blend
      AI --> BLENDNODE
    end

    BLENDNODE --> SCORE["🎯 Knowledge Score<br/>(0–1, goal-conditioned)"]:::score

    %% Styles
    classDef good fill:#E6F4EA,stroke:#34A853,stroke-width:3px,color:#000
    classDef bad fill:#FCE8E6,stroke:#EA4335,stroke-width:3px,color:#000
    classDef pairs fill:#FFF7E6,stroke:#E69500,stroke-width:2px,color:#000
    classDef encoder fill:#E6F0FF,stroke:#1C75BC,stroke-width:2px,color:#000
    classDef human fill:#E6F7FF,stroke:#1C75BC,stroke-width:3px,color:#000
    classDef ai fill:#F0F0F0,stroke:#555,stroke-width:3px,color:#000
    classDef blend fill:#F9E6FF,stroke:#7300CC,stroke-width:3px,color:#000
    classDef score fill:#E6FFF2,stroke:#2EB82E,stroke-width:3px,color:#000
  

🎭 Building Fair Pairs

The KnowledgePairBuilder mines your chats and emits trainer-ready pairs (A preferred, B counterfactual).

• Scoped: same conversation + primary domain.
• On-topic: require at least one shared NER entity.
• Dual supervision:
• Human stars (−5…+5) → normalized to [0,1].
• AI judge scores (0–100) → calibrated to [0,1] with isotonic regression.
• Weighted: human pairs = 1.0, AI pairs ≈ 0.35 (lower if uncertain).
• Traceable: each pair carries IDs, hashes, domains, length norms, retrieval fidelity, etc.

👉 In practice: the model learns “for this goal and topic, A is better than B”, not just “A sounds nicer.”

🤷 Why this exists

• Your positive turns are ones you implicitly approved (e.g., ⭐ ≥ min),
• Your negative turns are ones you implicitly rejected (e.g., ⭐ ≤ max),
• We match within the same conversation + primary domain and require entity overlap so the comparison is fair (same topic, same context).

🔕 Turning chats into preference data

This loader distills raw conversations into training pairs for a lightweight DPO-style reward model. Each pair says, “for this goal, output A is preferable to output B.” We build those pairs directly from your existing chats and human star ratings no extra labeling.

🎥 What it does (in plain English)

• Scans rated turns and pulls the assistant responses (the thing we actually want to reward).
• Matches positives to counterfactual negatives from the same conversation & domain so comparisons are fair.
• Requires topical overlap via shared NER entities (e.g., both turns mention “LayerNorm”).
• Emits trainer-ready pairs with prompt (the conversation title / goal), output_a, output_b, and numeric preference targets from stars.
• Deduplicates with a stable pair_hash and caps size for fast iterations.

🎩 How it works (step-by-step)

1. Fetch candidates Loads two pools with pre-fetched texts and annotations:

   • Positives: star ≥ min_star_pos
   • Negatives: star ≤ max_star_neg Both require assistant text and non-empty NER.

2. Fair bucketing Negatives are bucketed by (conversation_id, primary_domain). This ensures A vs. B comparisons happen in the same context and topic area, reducing label noise.

3. Topical filtering We compute entity sets from NER and keep only negatives that share at least min_entity_overlap entities with the positive. That forces comparisons about the same thing.

4. Sampling & shuffle For each positive we sample up to max_negs_per_pos eligible negatives (optionally shuffled) to balance hardness and diversity.

5. Pair assembly We emit:

   • prompt: the conversation title (your “goal text”)
   • output_a: assistant text from the positive turn
   • output_b: assistant text from the negative turn
   • value_a / value_b: numeric targets derived from human stars
   • meta_a / meta_b: lightweight metadata (e.g., original stars)
   • domain, pos_id, neg_id, and a stable pair_hash

6. Return A compact list of pairs, ready to feed the trainer.

🎨 Design choices (and why)

• Assistant-only text: this is what users see and what we’ll score later; keeping the signal tight avoids mixing user phrasing into the label.
• Entity-overlap gating: guarantees topical alignment, which dramatically improves pair quality and convergence.
• Same conversation + domain: controls for prompt/context drift so the preference is about knowledgefulness, not unrelated factors.
• Stars → preference targets: leverages signals you’ve already collected simple, robust, and immediately useful.
• Determinism & dedupe: seeded randomness plus pair_hash make your datasets reproducible and traceable.

🌾 Fields the loader emits

💎 Quality guards built in

• Requires assistant text and non-empty NER on both sides.
• Enforces entity overlap (min_entity_overlap).
• Caps dataset size with limit.
• Optional shuffle to reduce ordering bias.

🎶 Tuning tips

• Start strict: min_star_pos=2, max_star_neg=-1, min_entity_overlap=1.
• Increase max_negs_per_pos for more diversity once training stabilizes.
• Loosen thresholds later to scale up data if validation stays strong.

📎 Easy extensions (when you’re ready)

• Hard negative mining: swap random negatives for the hardest under the current model.
• Goal-aware pairing: also require user-query similarity (not just domain).
• Per-domain quotas: ensure balanced coverage across domains.
• Time windows: prefer negatives from nearby turns to control for drift.

👉 Full Code Here: Pair Builder

def _entities(ner) -> set[str]:
    if not ner: return set()
    if isinstance(ner, str):
        try: ner = json.loads(ner)
        except Exception: return set()
    return { (e.get("text") or "").strip().lower() for e in ner if (e.get("text") or "").strip() }

def _primary_domain(row: Dict[str, Any]) -> Optional[str]:
    doms = row.get("domains") or []
    if isinstance(doms, str):
        try: doms = json.loads(doms)
        except Exception: return None
    for d in doms:
        name = (d.get("domain") or "").strip().lower()
        if name: return name
    return None

class KnowledgePairBuilder:
    """Build (preferred A, counterfactual B) pairs for DPO-lite."""
    def __init__(self, memory, min_entity_overlap: int = 1, seed: int = 1337):
        self.memory = memory
        self.min_overlap = int(min_entity_overlap)
        random.seed(seed)

    def build_pairs(
        self, min_star_pos=2, max_star_neg=-1, limit=50_000, max_negs_per_pos=3, shuffle=True
    ) -> List[Dict[str, Any]]:

        # 1) Fetch candidate turns
        pos = self.memory.chats.list_turns_with_texts(
            min_star=min_star_pos, require_assistant_text=True, require_nonempty_ner=True, limit=1_000_000
        )
        neg = self.memory.chats.list_turns_with_texts(
            max_star=max_star_neg, require_assistant_text=True, require_nonempty_ner=True, limit=1_000_000
        )
        if shuffle: random.shuffle(pos); random.shuffle(neg)
        if not pos or not neg: return []

        # 2) Bucket negatives by (conversation, domain)
        buckets: Dict[Tuple[int, Optional[str]], List[Dict[str, Any]]] = {}
        for n in neg:
            buckets.setdefault((n["conversation_id"], _primary_domain(n)), []).append(n)

        pairs, seen = [], set()
        # 3) For each positive, pick fair counterfactuals
        for p in pos:
            if len(pairs) >= limit: break
            key = (p["conversation_id"], _primary_domain(p))
            cands = buckets.get(key, [])
            if not cands: continue

            p_ents = _entities(p.get("ner"))
            if not p_ents: continue

            # same topic: overlap in entities
            cands = [n for n in cands if len(p_ents & _entities(n.get("ner"))) >= self.min_overlap]
            if shuffle: random.shuffle(cands)
            for n in cands[:max_negs_per_pos]:
                if len(pairs) >= limit: break
                h = hashlib.sha1(f"{p['id']}:{n['id']}".encode()).hexdigest()[:16]
                if h in seen: continue
                seen.add(h)

                # 4) Emit trainer-ready pair (A preferred over B)
                pairs.append({
                    "prompt": (p.get("goal_text") or "").strip(),
                    "output_a": p["assistant_text"],
                    "output_b": n["assistant_text"],
                    "value_a": float(max(0, p.get("star", 1))),
                    "value_b": float(min(0, n.get("star", -1))),
                    "meta_a": {"human_stars": float(p.get("star", 1))},
                    "meta_b": {"human_stars": float(n.get("star", -1))},
                    "domain": _primary_domain(p),
                    "pos_id": p["id"],
                    "neg_id": n["id"],
                    "pair_hash": h,
                })
        return pairs

🔘 Config strategy parameters that matter

• min_star_pos, max_star_neg: how strict your preferences are min_entity_overlap: how topical the pairing must be
• max_negs_per_pos: hardness / diversity of negatives
• limit: dataset size cap for quick iterations

Tip: Start strict (e.g., min_star_pos=2, max_star_neg=-1) then relax once the model stabilizes.

🔊 What it outputs

Each pair looks like:

{
  "prompt": "goal text…",
  "output_a": "preferred assistant text",
  "output_b": "non-preferred assistant text",
  "value_a": 2.0,
  "value_b": -1.0,
  "meta_a": {
    "human_stars": 2.0,
    "pseudo_stars": 0.0,
    "artifact_quality": 0.0,
    "turn_pos_ratio": 1.0,
    "has_retrieval": 0.0,
    "retrieval_fidelity": 0.0,
    "text_len_norm": 0.42
  },
  "meta_b": { "...mirrors meta_a..." },
  "domain": "ml",
  "goal_id": null,
  "pos_id": 123,
  "neg_id": 456,
  "pair_hash": "b7a6f3e0e0c4b2ab"
}

A is preferred, B is the counterfactual. The trainer will learn “A > B for this prompt”.

✨ The Goal-Conditioned Knowledge Model: From Pairs to Predictions

At its heart, the model’s job is simple: given two candidate turns, rank the more knowledgeful one higher.

🎯 The Learning Objective

To capture this, we use a pairwise preference loss. Given two turns A and B from the same goal/domain/topic, the model learns that s_A > s_B.

• Logistic loss (default): $$ \mathcal{L} = -\log \sigma(s_A - s_B) $$

This directly models the probability that A > B, producing smooth, order-preserving gradients and fast convergence, even on small datasets.

• Margin ranking loss (alternative): $$ \max(0, m - (s_A - s_B)) $$

This variant enforces a safety margin, which is useful when examples are noisy or clustered too tightly.

👉 Together, these losses act as the teaching signal: every comparison tells the model, “prefer this turn over that one.”

🧩 Distilling Knowledge into Structure

How do we compute those scores s_A and s_B? We take each (goal, candidate, aux) triple, fuse them into a joint representation, and pass that through two lightweight predictors:

• CrossFeatureEncoder → mixes embeddings of the goal and the candidate text using bilinear + elementwise interactions.

• AuxProjector → injects stabilizing signals such as human stars, retrieval fidelity, or turn position.

• Dual heads:

  • 👤 Human head (predictor_h) learns from your star ratings.
  • 🤖 AI head (predictor_a) learns from calibrated AI judge scores.

At inference time, we blend these signals:

• If we have nearby human labels → α = 1.0 (trust the human head).
• If supervision is sparse → α ≈ 0.6 (blend in calibrated AI).

This keeps the model anchored in human judgment while still broad enough to generalize.

🖼️ Visual Overview

    flowchart LR
    subgraph INPUT_LAYER["📥 Input Layer"]
        G["🎯 Goal Text"]:::input
        X["📄 Candidate Text"]:::input
        AUX["🛠️ Aux Features"]:::input
    end

    G --> CFE["🔀 CrossFeatureEncoder"]:::fusion
    X --> CFE
    AUX --> AP["📊 AuxProjector"]:::projection
    CFE --> AP

    subgraph PREDICTION_LAYER["🧠 Dual Predictors"]
        AP --> H["👤 Human Head σ(h)"]:::human
        AP --> A["🤖 AI Head σ(a)"]:::ai
    end

    H --> HP["P(human)"]:::sigmoid
    A --> APROB["P(ai)"]:::sigmoid

    HP --> BLEND["⚖️ Adaptive Blending"]:::blend
    APROB --> BLEND
    BLEND --> OUT["✅ Final Knowledge Score"]:::output

    classDef input fill:#bbdefb,stroke:#1565c0,stroke-width:2px,color:#000
    classDef fusion fill:#c8e6c9,stroke:#388e3c,stroke-width:2px,color:#000
    classDef projection fill:#dcedc8,stroke:#689f38,stroke-width:2px,color:#000
    classDef human fill:#ffe0b2,stroke:#f57c00,stroke-width:2px,color:#000
    classDef ai fill:#e1bee7,stroke:#7b1fa2,stroke-width:2px,color:#000
    classDef sigmoid fill:#f8bbd9,stroke:#c2185b,stroke-width:2px,color:#000
    classDef blend fill:#ffecb3,stroke:#ffa000,stroke-width:3px,color:#000,font-style:italic
    classDef output fill:#b3e5fc,stroke:#0288d1,stroke-width:3px,color:#000
  

🧑‍💻 Core Implementation

Below is the core PyTorch design that powers this architecture:

class CrossFeatureEncoder(nn.Module):
    """
    Joint encoder over (goal_emb, text_emb) with bilinear & elementwise interactions.
    Produces a compact H-dim representation for the predictor heads.
    """
    def __init__(self, dim: int, hdim: int):
        super().__init__()
        self.dim = dim
        self.hdim = hdim
        self.bilinear = nn.Bilinear(dim, dim, hdim, bias=False)
        self.proj = nn.Sequential(
            nn.Linear(dim * 4, hdim),
            nn.ReLU(),
            nn.Linear(hdim, hdim),
            nn.ReLU()
        )

    def forward(self, goal: torch.Tensor, text: torch.Tensor) -> torch.Tensor:
        z_bi = self.bilinear(goal, text)                           # [B,H]
        z_feats = torch.cat([goal, text, goal * text, torch.abs(goal - text)], dim=-1)
        z_proj = self.proj(z_feats)                                # [B,H]
        return z_bi + z_proj

class AuxProjector(nn.Module):
    """
    Projects optional auxiliary numeric features into H and fuses via residual add.
    """
    def __init__(self, hdim: int, aux_dim: int):
        super().__init__()
        self.aux_dim = aux_dim
        if aux_dim > 0:
            self.mlp = nn.Sequential(
                nn.Linear(aux_dim, hdim),
                nn.ReLU(),
                nn.Linear(hdim, hdim)
            )
        else:
            self.mlp = None

    def forward(self, z: torch.Tensor, aux: Optional[torch.Tensor]) -> torch.Tensor:
        if self.mlp is None or aux is None:
            return z
        return z + self.mlp(aux)                                   # residual fusion

class KnowledgePredictor(nn.Module):
    """
    Main scalar head (continuous “knowledgefulness” score).
    """
    def __init__(self, hdim: int):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(hdim, hdim),
            nn.ReLU(),
            nn.Linear(hdim, hdim // 2),
            nn.ReLU(),
            nn.Linear(hdim // 2, 1)
        )

    def forward(self, z: torch.Tensor) -> torch.Tensor:
        return self.mlp(z).squeeze(-1)                             # [B]

class KnowledgeModel:
    """
    End-to-end model wrapper:
      - Uses your existing embedding_store (same interface as MRQModel).
      - Goal-conditioned scoring: score(goal_text, candidate_text, meta)
      - Aux feature injection for stability/controllability.
    """
    def __init__(self, dim: int, hdim: int, embedding_store, aux_feature_names: Optional[List[str]] = None, device: str = "cpu"):
        self.device = device
        self.embedding_store = embedding_store
        self.aux_feature_names = aux_feature_names or []
        self.encoder = CrossFeatureEncoder(dim, hdim).to(device)
        self.aux_proj = AuxProjector(hdim, aux_dim=len(self.aux_feature_names)).to(device)
        self.predictor = KnowledgePredictor(hdim).to(device)

    # ----- runtime API (MRQ-compatible) -----
    def _embed(self, text: str) -> torch.Tensor:
        v = self.embedding_store.get_or_create(text)
        return torch.tensor(v, device=self.device, dtype=torch.float32).unsqueeze(0)  # [1,D]

    def _aux_tensor(self, meta: Optional[dict]) -> Optional[torch.Tensor]:
        if not self.aux_feature_names:
            return None
        meta = meta or {}
        vals = []
        for name in self.aux_feature_names:
            try:
                vals.append(float(meta.get(name, 0.0)))
            except Exception:
                vals.append(0.0)
        return torch.tensor(vals, device=self.device, dtype=torch.float32).unsqueeze(0)  # [1,A]

    def predict(self, goal_text: str, candidate_text: str, meta: Optional[dict] = None) -> float:
        g = self._embed(goal_text)               # [1,D]
        x = self._embed(candidate_text)          # [1,D]
        z = self.encoder(g, x)                   # [1,H]
        aux = self._aux_tensor(meta)             # [1,A] or None
        z = self.aux_proj(z, aux)                # [1,H]
        score = self.predictor(z).item()
        return score

    def train(self):
        self.encoder.train(); self.aux_proj.train(); self.predictor.train()

    def eval(self):
        self.encoder.eval(); self.aux_proj.eval(); self.predictor.eval()

    # ----- checkpoints -----
    def save(self, encoder_path: str, predictor_path: str, auxproj_path: str):
        torch.save(self.encoder.state_dict(), encoder_path)
        torch.save(self.predictor.state_dict(), predictor_path)
        torch.save(self.aux_proj.state_dict(), auxproj_path)

    def load(self, encoder_path: str, predictor_path: str, auxproj_path: str):
        self.encoder.load_state_dict(torch.load(encoder_path, map_location=self.device))
        self.predictor.load_state_dict(torch.load(predictor_path, map_location=self.device))
        self.aux_proj.load_state_dict(torch.load(auxproj_path, map_location=self.device))
        self.eval()

Mapping aux features: set aux_feature_names = ["human_stars","pseudo_stars","artifact_quality","turn_pos_ratio","has_retrieval","retrieval_fidelity","text_len_norm"] to match meta_a/meta_b. You can add/remove features without changing the core encoder.

👯 Why two heads?

• Label efficiency: we don’t have human ratings for every turn or artifact yet. The AI head provides a reasonable prior so we can still rank candidates.
• Triage for review: we can prioritize “likely good” but unlabeled items for your attention, accelerating coverage where it matters.
• Cold start & coverage: new domains get immediate (imperfect) scoring from the AI head; as you rate examples, the human head quickly takes over.
• Robustness: if the AI heuristics drift, the human head keeps us grounded; if human labels are thin, the AI head prevents blindness.

👪 The blend (simple and transparent)

For a candidate, each head outputs a logit (h, a). We convert to probabilities with sigmoid and blend:

• Default α = 0.6 (bias toward human).
• If meta.has_similar_human == True (we’ve seen close human-rated neighbors in this goal/domain/entity neighborhood), α = 1.0 (use the human head only).

This gives us a clear, interpretable score in [0,1] that respects your supervision but doesn’t collapse when it’s missing.

🚏 Training signals

• Human head: trained on contrastive pairs from your RHLF² stars (A > B within the same goal/domain/entities).

• AI head: trained on weaker labels derived from:

  • retrieval fidelity / evidence presence
  • turn position & structure cues
  • text-length norms / density
  • heuristic knowledge scores (v0)

Both heads share the same encoder (CrossFeatureEncoder + AuxProjector). That keeps features aligned while allowing different targets.

🪃 What the API returns

You can ask for a single number or a breakdown of contributions for debugging, dashboards, or audits.

p, details = model.predict(
    goal_text, candidate_text, meta={"has_similar_human": True, "text_len_norm": 0.42},
    return_components=True
)

# details
{
  "probability": 0.91,
  "human_logit": 2.34, 
  "ai_logit": 1.02,
  "human_prob": 0.91,  
  "ai_prob": 0.73,
  "alpha_human_weight": 1.0, 
  "has_similar_human": true,
  "human_component": 0.91, 
  "ai_component": 0.00,
  "human_fraction": 1.0, 
  "ai_fraction": 0.0
}

🧲 Knowledge scoring fields

Notes • Blending: probability ≈ alpha_human_weight * human_prob + (1 − alpha_human_weight) * ai_prob (plus any calibration). • Calibration: If a tuner is present, probability may be post-calibrated. Head components/fractions reflect the pre-calibrated blend logic. • Interpretation tip: Large gaps between human_prob and ai_prob = disagreement; pair with ablation or extra verification.

〰️ Quick recipes

• Flag disagreements for review: if abs(human_prob − ai_prob) > 0.25, queue for human QA.
• Explain a score in dashboards: show probability, alpha_human_weight, human_fraction/ai_fraction, and has_similar_human.
• Tighten promotion rules: require probability ≥ 0.85 and (human_fraction ≥ 0.6 or has_similar_human == True).

📏 How we use it in practice

• Active review queue: sort unlabeled turns by high p (or high ai_prob when α is low), then surface the top-value candidates for quick human rating. Each rating immediately improves the human head and flips α→1.0 for similar items.
• Knowledge-first retrieval: rerank retrieved snippets by p to bias toward knowledge-dense material.
• Agent reward shaping: when training/verifying agents, use p as part of the reward (artifact_quality × knowledge_score), with guardrails from the human head where available.

🧭 Path to your knowledge

The two-head design lets the system learn continuously from your process even when supervision is partial:

• It doesn’t stall in unlabeled regions (AI head carries us).
• It snaps to your taste the moment you label a few exemplars (human head dominates via α).
• It prioritizes your time by proposing the most promising items to review next.

In short: we can operate now and align fast and you always have a faithful path back to the human signal that defines what “knowledge” means for you.

🔀 Calibrating the AI Head to Human Scale

The AI head is trained on weaker, automatically available signals. By default, its probabilities overestimate what a human would mark as “knowledgeful.” To keep the two-head blend honest, we run the AI head through a ScoreCalibrator so its 0–100 confidence maps to a human-equivalent 0–1 curve.

👉 Full Code Here: ScoreCalibrator

🤨 What calibration does (and why)

• Problem: AI head says “95/100,” but humans would call it ~3.5/5 (≈0.75).
• Fix: Fit a monotonic (isotonic) regression that remaps AI→human.
• Outcome: The blend respects your taste even where we don’t have labels yet.

(goal, text, aux) → encoder → aux-proj → ─┬─ human head → σ(h) = h_prob
                                          └─ AI head    → σ(a) = ai_prob (0..1)
                                                           └─ calibrate(100·ai_prob) → ai_cal (0..1)
final score p = α·h_prob + (1−α)·ai_cal

• α (human weight): 1.0 when we’ve seen similar human-rated examples; else 0.6 by default.
• ai_cal: calibrated AI probability; prevents “95 looks like 0.95” inflation.

🧦 How we fit it

We pair human stars and AI scores gathered during training/review:

• Human stars −5..+5 → normalize to 0..1: (stars + 5)/10
• AI scores 0..100 → normalize to 0..1: score/100
• Fit IsotonicRegression (monotonic, non-parametric), store the curve.

When we don’t have enough pairs yet, we use a conservative fallback curve (piecewise linear) that especially down-weights the high end (e.g., 100 → 0.80).

🫶 Where it plugs into the model

# Pseudocode inside model.predict(...)
h_prob = sigmoid(human_logit)
a_prob = sigmoid(ai_logit)

# Calibrate AI head from 0..1 → 0..1 via 0..100 intermediate
ai_cal = calibrator.calibrate(a_prob * 100.0)  # safe if calibrator.unfitted → conservative default

alpha = 1.0 if meta.get("has_similar_human") else 0.6
p = alpha * h_prob + (1 - alpha) * ai_cal

This keeps the ordering the AI head learns (monotonic) while aligning its scale to human reality.

🎥 Practical effects

• Better triage: Unlabeled candidates with inflated AI confidence won’t crowd the review queue; true wins still float to the top.
• Stable rewards: Agent rewards that use knowledge_score won’t spike just because the AI head is over-confident.
• Safer defaults: Before we have enough human pairs, the fallback curve keeps precision high.

⚒️ Maintenance & reliability

• Fit: update the calibrator whenever you add a meaningful batch of new human ratings.
• Persist: save the sampled curve (we store 0–100 → calibrated values) so it’s portable and framework-agnostic.
• Gate: only treat the calibrator as “reliable” once it has ≥50 samples and R² ≥ 0.7; until then, the conservative default remains in force.

Bottom line: calibration lets the AI head contribute coverage without distorting the blend. The human head defines “what good looks like,” and the AI head properly calibrated helps you find more of it faster.

🏋️ Training Knowledge into Stephanie: The KnowledgeTrainer

So far, we’ve shown how to collect and organize conversations into CaseBooks, then distill them into fair pairs (A preferred, B counterfactual). But a pipeline without training is just storage. The KnowledgeTrainer is where those pairs turn into a living signal Stephanie’s ability to recognize and score knowledge.

At its heart, the trainer is a pairwise engine built on the DPO-lite objective:

def dpo_lite_loss(s_pos, s_neg, beta=2.0, margin=0.2):
    return F.softplus(-(beta * (s_pos - s_neg - margin))).mean()

This is just a smooth version of: 👉 “If A is preferred to B, then score(A) should be higher than score(B).” The softplus makes sure we get clean gradients even when A and B are close, and the margin forces the model to really separate the two.

⚖️ Dual-Head Training

The trainer doesn’t just optimize one head, but two in parallel:

• 👤 Human head (predictor_h): learns directly from your star ratings (−5..+5).
• 🤖 AI head (predictor_a): learns from automated judge scores (0–100), but with a calibration step to map them onto the human scale.

During training:

• Human pairs get full weight.
• AI pairs get a reduced weight (≈0.35).
• A small alignment loss keeps both heads roughly consistent.

This way, the model respects human gold signals while still gaining coverage from weaker AI supervision.

📦 What It Trains On

The trainer consumes pairs like this:

• goal_text (the problem we’re solving),
• output_a, output_b (the candidate turns),
• meta_a, meta_b (aux features like length, retrieval fidelity, position),
• label_source (human or AI),
• pair_weight (scales influence).

Behind the scenes, everything is embedded, L2-normalized, and converted into TensorDatasets for PyTorch training.

🧪 Metrics We Track

During training, the trainer evaluates itself on a held-out validation set:

• Pairwise accuracy (did the head correctly rank A over B?).
• Alignment MSE (how far human and AI heads diverge).
• Disagreement rate (fraction of pairs where heads disagree on ordering).

These numbers matter: high disagreement means the model is surfacing examples where human review is most valuable.

🛡️ Guards and Stability

The trainer has a few extra tricks to stay stable:

• Early stopping stop if validation stalls.
• Gradient clipping avoid exploding updates.
• Calibration defaults map raw AI scores (0,40,50,75,100) onto human-equivalent ratings (−5,0,1,3,5).
• Aux features inject retrieval fidelity, text length, and star normalization to prevent the model from drifting.

📊 What Comes Out

At the end, the trainer saves:

• Encoder weights (joint goal-text representation),
• Human head, AI head, and aux projector,
• A calibrator and tuner (for regression-style adjustments),
• Metadata (accuracy, disagreement rate, trained pairs, hyperparameters).

This becomes the knowledge scorer that the rest of Stephanie’s system can call whenever it needs to ask:

“Does this turn actually contain knowledge?”

    flowchart TD
    %% === INPUTS ===
    subgraph PAIRS["⚖️ Knowledge Pairs"]
        A["💬 Turn A (preferred)"]:::good
        B["💬 Turn B (counterfactual)"]:::bad
        M["🛠️ Aux Features<br/>(stars, length, retrieval, position)"]:::meta
    end

    %% === ENCODER ===
    subgraph ENCODER["🔗 CrossFeatureEncoder + AuxProjector"]
        A --> ENC
        B --> ENC
        M --> ENC
        ENC["🧩 Joint Embedding<br/>(goal ⊗ text + aux)"]:::encoder
    end

    %% === PREDICTORS ===
    subgraph HEADS["🧠 Dual Predictors"]
        ENC --> H["👤 Human Head<br/>σ(h)"]:::human
        ENC --> AI["🤖 AI Head<br/>σ(a)"]:::ai
    end

    %% === BLENDING ===
    subgraph BLEND["🔀 Adaptive Blending"]
        H --> BL["⚖️ p = α·σ(h) + (1-α)·calibrate(σ(a))"]:::blend
        AI --> BL
    end

    %% === TRAINING ===
    subgraph TRAIN["📉 Training Signal"]
        BL --> LOSS["📐 DPO-lite Loss<br/>(-log σ(Δ))"]:::loss
        H -.-> ALIGN["🔗 Alignment Loss<br/>(MSE between heads)"]:::align
        AI -.-> ALIGN
        ALIGN --> LOSS
    end

    %% === OUTPUTS ===
    subgraph OUTPUT["📊 Metrics + Model"]
        LOSS --> MET["📈 Metrics:<br/>pairwise acc, disagreement, alignment"]:::metrics
        LOSS --> MODEL["🗂️ Knowledge Model<br/>(encoder + heads + calibrator)"]:::model
    end

    %% Styles
    classDef good fill:#E6F4EA,stroke:#34A853,stroke-width:3px,color:#000,font-weight:bold
    classDef bad fill:#FCE8E6,stroke:#EA4335,stroke-width:3px,color:#000,font-weight:bold
    classDef meta fill:#FFF9C4,stroke:#FBC02D,stroke-width:2px,color:#000,font-weight:bold
    classDef encoder fill:#BBDEFB,stroke:#1565C0,stroke-width:2px,color:#000,font-weight:bold
    classDef human fill:#FFE0B2,stroke:#F57C00,stroke-width:3px,color:#000,font-weight:bold
    classDef ai fill:#E1BEE7,stroke:#7B1FA2,stroke-width:3px,color:#000,font-weight:bold
    classDef blend fill:#FFECB3,stroke:#FFA000,stroke-width:3px,color:#000,font-style:italic,font-weight:bold
    classDef loss fill:#FFCDD2,stroke:#C62828,stroke-width:2px,color:#000,font-weight:bold
    classDef align fill:#D1C4E9,stroke:#512DA8,stroke-width:2px,color:#000,font-weight:bold
    classDef metrics fill:#C8E6C9,stroke:#388E3C,stroke-width:2px,color:#000,font-weight:bold
    classDef model fill:#B2DFDB,stroke:#00796B,stroke-width:3px,color:#000,font-weight:bold
  

🧑‍🏫 KnowledgeTrainer Teaching AI What Counts as Knowledge

Below is the heart of the trainer: a small PyTorch loop that takes in pairs (preferred, counterfactual) and teaches the model to rank the preferred turn higher. Notice how we balance human supervision (stars) and AI supervision (calibrated scores), with a light alignment term to keep them consistent.

This trainer is the engine room of the entire system. Without it, our carefully collected pairs are just data. With it, they become a living model of your judgment that can score thousands of turns in milliseconds.

import torch
import torch.nn.functional as F

# --- Pairwise preference loss (DPO-lite) ---
def dpo_lite_loss(s_pos, s_neg, beta=2.0, margin=0.2):
    """
    Encourage model to score s_pos higher than s_neg.
    Softplus = smooth version of max(0,·).
    """
    return F.softplus(-(beta * (s_pos - s_neg - margin))).mean()


class KnowledgeTrainer:
    """
    Minimal view of the full trainer:
    - Human head learns from human stars
    - AI head learns from calibrated AI scores
    - We combine them with adaptive weights
    """

    def __init__(self, model, optimizer, beta=2.0, margin=0.2,
                 ai_pair_weight=0.35, align_lambda=0.05):
        self.model = model
        self.opt = optimizer
        self.beta = beta
        self.margin = margin
        self.ai_pair_weight = ai_pair_weight
        self.align_lambda = align_lambda

    def train_step(self, batch):
        """
        batch = (goal_emb, pos_emb, neg_emb, aux_pos, aux_neg,
                 source, weight)
        where:
          - source=1 if human label, else 0 (AI label)
          - weight is per-pair importance
        """
        G, A, B, AUXA, AUXB, source, weight = batch

        # Encode pairs under the goal
        z_a = self.model.encoder(G, A)
        z_b = self.model.encoder(G, B)
        z_a = self.model.aux_proj(z_a, AUXA)
        z_b = self.model.aux_proj(z_b, AUXB)

        # Dual-head scores
        s_h_a, s_h_b = self.model.score_h(z_a), self.model.score_h(z_b)
        s_a_a, s_a_b = self.model.score_a(z_a), self.model.score_a(z_b)

        # Human + AI DPO-lite losses
        lh = dpo_lite_loss(s_h_a, s_h_b, self.beta, self.margin)
        la = dpo_lite_loss(s_a_a, s_a_b, self.beta, self.margin)

        # Per-pair weights
        is_human = (source > 0.5).float()
        human_w = weight * is_human
        ai_w = weight * (1.0 - is_human)

        # Alignment loss: keep both heads in sync
        align = F.mse_loss(s_h_a - s_h_b, s_a_a - s_a_b)

        # Final loss = weighted human + weighted AI + alignment
        loss = (
            (lh * human_w).mean()
            + self.ai_pair_weight * (la * ai_w).mean()
            + self.align_lambda * align
        )

        # Backprop
        self.opt.zero_grad()
        loss.backward()
        self.opt.step()

        return loss.item()

🔎 What this shows

• Human stars dominate (our anchor of truth).
• AI scores are discounted but useful when human labels are sparse.
• Alignment loss prevents drift between the two.

👉 The full production version includes calibration, early stopping, disagreement metrics, regression tuner integration, and persistence. You can see it Knowledge Trainer for all the details.

💡 Takeaway: The KnowledgeTrainer is where learning from learning becomes real. It fuses your ratings with AI priors, aligns them in a shared space, and leaves Stephanie with a sharp, measurable signal of knowledge. Every pair you rate, every AI judge score, every aux feature contributes and the result is a model that doesn’t just chat, but understands what’s worth keeping.

👾 Scoring & Telemetry: Seeing the Human vs. AI Contribution

The KnowledgeScorer is the production wrapper around the two-head KnowledgeModel. It takes a (goal_text, candidate_text) pair, injects a small set of aux features (stars, retrieval flags, length, etc.), asks both heads to score, calibrates the AI head (down-weights optimism), and then blends:

final_probability p = α · human_prob  +  (1 − α) · calibrated_ai_prob

• α (alpha) is human-first. If we have neighbor evidence like has_similar_human=True, α=1.0; otherwise α defaults to 0.6. _ The AI head is calibrated into human-equivalent space before blending (see the calibration section).

⚓️ Human first

• We can score everything (AI head) while still anchoring to what humans actually consider “knowledgeful” (human head).
• When the heads disagree, we route examples for review instead of silently trusting inflated AI confidence.

💯 Scoring a summary

In this process we are generating summaries for paper section this is where we evaluate the quality of these summaries.

    def score_summary(
        self,
        text: str,
        paper: Dict[str, Any],
        section: Dict[str, Any],
        context: Dict[str, Any],
    ) -> Dict[str, Any]:
        """Score summary with configurable weights"""
        clarity, grounding = self.rubric_dims(
            text, section.get("section_text", "")
        )
        goal_text = (
            f"{paper.get('title', '')}\n\n{paper.get('abstract', '')}"
        )
        meta = {"text_len_norm": min(1.0, len(text) / 2000.0)}

            # Support different scoring API signatures
        p, comps = self.knowledge.model.predict(
            goal_text, text, meta=meta, return_components=True
        )
        knowledge = float((comps or {}).get("probability", p))

        # Calculate overall score using configurable weights
        overall = (
            self.knowledge_weight * knowledge
            + self.clarity_weight * clarity
            + self.grounding_weight * grounding
        )

        return {
            "overall": overall,
            "knowledge_score": knowledge,
            "clarity": clarity,
            "grounding": grounding,
            "weaknesses": self.weaknesses(
                text, section.get("section_text", "")
            ),
            **(comps or {}),
        }

🔙 What the scorer returns

KnowledgeScorer.score(...) returns a ScoreBundle with a ScoreResult for "knowledge". The result includes rich attributes you can log, chart, or alert on:

{
  "dimension": "knowledge",
  "score": 0.8125,
  "source": "knowledge",
  "rationale": "blended_prob=0.8125",
  "attributes": {
    "probability": 0.8125,          // final blended score
    "human_prob": 0.86,             // σ(human_logit)
    "ai_prob": 0.74,                // σ(ai_logit) BEFORE calibration (for visibility)
    "human_logit": 1.82,            // raw head logit
    "ai_logit": 1.05,               // raw head logit
    "head_gap": 0.12,               // |human_prob - ai_prob|
    "alpha_human_weight": 0.6,      // α used in the blend
    "has_similar_human": false,     // neighbor evidence to bump α to 1.0
    "human_component": 0.516,       // α·human_prob
    "ai_component": 0.2965,         // (1-α)·calibrated_ai_prob
    "human_fraction": 0.635,        // human_component / (human+ai)
    "ai_fraction": 0.365,           // ai_component / (human+ai)
    "aux_used": [
      "human_stars","pseudo_stars","artifact_quality","turn_pos_ratio",
      "has_retrieval","retrieval_fidelity","text_len_norm"
    ]
  }
}

🧮 How to read these fields

• probability / score – the final decision number you feed into ranking, routing, or rewards.
• human_prob, ai_prob, human_logit, ai_logit – transparent head internals for audits & dashboards.
• alpha_human_weight – how much we trusted human priors this time.
• human_component, ai_component – the actual additive pieces of the final score.
• human_fraction, ai_fraction – normalized attribution; great for stacked area charts over time to show your system getting “more human-like.”
• head_gap – big gaps (default > 0.25) trigger uncertainty routing: the scorer can push the candidate into a casebook lane for human review.
• aux_used – documents which aux features were active for this call (good for feature drift checks).

💉 Signals we inject (aux features)

From the scoring context and scorable meta we derive lightweight features:

• human_stars, pseudo_stars – prior supervision signals if present
• artifact_quality – optional content quality heuristic
• turn_pos_ratio – position in conversation (helps de-bias early “draft” turns)
• has_retrieval, retrieval_fidelity – retrieval presence and quality
• text_len_norm – soft length normalization (guards against tiny snippets)

These feed the AuxProjector and nudge the representation without dominating the heads.

🙅‍♂️ Disagreement → Review

When head_gap is high, the scorer can auto-stash the candidate with metadata (human/AI logits, gap size, ids) into your casebook for triage. That gives you a continuous stream of high-value labels where the model is uncertain perfect for improving the human head and tightening the calibrator.

🌅 Optional post-tuning

If you train a light probability tuner (e.g., regression on task-level outcomes), the scorer can apply it to the blended p. This is disabled by default and only used when a trained tuner file is present.

Bottom line: KnowledgeScorer does more than produce a number it explains where that number came from. The telemetry fields let you monitor the human/AI balance, detect optimism, and channel the right samples to humans so the system keeps getting better.

➡️ Next

With training complete, we now have more than just a database of conversations we have a measurable knowledge signal. Each turn is scored by a dual-head model that blends your judgment with AI priors, producing a compact scalar that says: “this is knowledge.”

But scoring alone isn’t the end goal. What matters is application taking that signal and letting it guide retrieval, verification, summarization, and ultimately, new learning events. In the next section we’ll see how these knowledge scores move downstream: reranking what we read, sharpening what we write, and closing the loop so every conversation fuels the next breakthrough.

🚀 Section 4: Knowledge Application

We don’t just retrieve facts we apply prior knowledge to new drafts and prove it with attribution, measurable lift, and ablations.

    flowchart LR
  A[🧩 Knowledge Ingestion] --> B[🧾 Knowledge Processing]
  B --> C[🧪 Knowledge Training & Understanding]
  C --> D[🚀 Knowledge Application]
  D --> E[📊 Measurement of Learning]

  %% Styles
  classDef ingest fill:#e6f7ff,stroke:#1c75bc,stroke-width:3px,color:#000;
  classDef process fill:#d0e7ff,stroke:#1c75bc,stroke-width:2px;
  classDef train  fill:#f0f0f0,stroke:#555,stroke-width:2px,color:#000,color:#000;
  classDef apply  fill:#fff7e6,stroke:#e69500,stroke-width:2px,color:#000,font-weight:bold,stroke-dasharray: 5 2;
  classDef measure fill:#f9f0ff,stroke:#8e44ad,stroke-width:2px,color:#000;

  %% Assign classes
  class A ingest;
  class B process;
  class C train;
  class D apply;
  class E measure;
  

🎓 technē: Applied Knowledge

If this system is really learning from learning, it must do more than write nicer summaries it must apply specific knowledge it has seen before to improve new drafts in measurable, attributable ways. In this section, we show exactly how we verify that.

🔨 What we’ll demonstrate

• Attribution: Final claims are backed by concrete sources. Every non-obvious claim links to a concrete source (retrieval or arena candidate).
• Applied lift: Scores improve at the moment supported knowledge is introduced (iteration-level deltas).
• Necessity (ablation): Removing or shuffling retrieval causes a statistically significant drop in grounding and overall quality.
• Transfer: Patterns and sources that helped in one paper measurably help in later papers (cross-episode reuse).
• Significance: We report t-tests and effect sizes (Cohen’s d) so improvements aren’t chalked up to chance.

How we measure it We log every refinement step, the supporting snippets behind claims, and A/B enrollments of strategy changes. From those breadcrumbs we compute:

• Attribution Rate (AR) share of winning summaries with sentence-level support.

AR = (# winning summaries with ≥1 claim→support link) / (total winning summaries)

• Applied-Knowledge Lift (AKL) score gain on iterations that add supported claims vs those that don’t.

AKL = mean( overall[i] − overall[i−1] | iteration i introduced supported knowledge )

• Retrieval Necessity (RN) performance drop under retrieval ablation.

RN = mean(overall_with) − mean(overall_without) from matched runs where mask_keys hides the exact sources used by the winner.

• Transfer Rate (TR) reuse of winning patterns/sources across papers and its impact.

TR = (# papers where prior winner-origin/strategy reappears and improves mean overall) / (# eligible paper transitions)

Next, we’ll break down the agent’s flow and show where each signal is captured and how it proves the model is truly applying knowledge.

📑 The paper summary process

Here’s the map for what can look like a busy journey: it’s just our section-by-section loop for a paper retrieve the right snippets, draft a candidate, then verify and refine it. The Arena is an optional branch that lets different applications of the same knowledge compete, but everything flows back into the same verify-and-persist cycle. At the end, we roll up the receipts (attribution, lifts, ablations) to produce evidence that the system really applied knowledge and improved.

    flowchart TD
    %% ========== STYLING DEFINITIONS ==========
    classDef start fill:#e1f5fe,stroke:#01579b,stroke-width:2px,color:#01579b
    classDef decision fill:#fff3e0,stroke:#ef6c00,stroke-width:2px,color:#ef6c00
    classDef process fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px,color:#1b5e20
    classDef loop fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c
    classDef corpus fill:#e3f2fd,stroke:#1565c0,stroke-width:2px,color:#0d47a1
    classDef arena fill:#fff8e1,stroke:#ff8f00,stroke-width:2px,color:#e65100
    classDef verify fill:#ffebee,stroke:#c62828,stroke-width:2px,color:#b71c1c
    classDef persist fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px,color:#1b5e20
    classDef evidence fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c

    %% ========== 🚀 START & DOCUMENT RESOLUTION ==========
    A["🚀 Start run(context)"] --> B{📄 documents from context<br/>or single_random_doc?}
    B -->|✅ resolve documents| C[🔄 For each paper]
    C --> D[⚙️ prepare_casebook_goal_sections]
    D --> E[📊 progress.start_paper]

    %% ========== 📑 PAPER PROCESSING LOOP ==========
    E --> F[🔄 sections loop]
    
    %% ========== 🔄 PER SECTION PROCESSING ==========
    subgraph Per_Section_Processing["📑 Per Section Processing"]
        direction TB
        F --> G{📏 section_is_large_enough?}
        G -- ❌ no --> F
        G -- ✅ yes --> H[⚙️ create_section_case]
        H --> I["📋 ctx_case (case_id, pipeline_run_id,<br/>strategy_version, threshold)"]
        I --> J[📊 progress.start_section]

        %% ========== 📚 CORPUS FETCH ==========
        J --> K["📚 corpus.fetch(section_text)"]
        K --> L["📊 progress.stage: corpus:done(items)"]

        %% ========== 🏆 DRAFT BRANCH (ARENA vs BASELINE) ==========
        L --> M{🎯 use_arena?}
        M -- ✅ yes --> N["⚙️ _build_candidates(corpus + seed)"]
        N --> O["⚔️ ArenaService.run(section_text, candidates)"]
        O --> P[🏆 baseline = arena.winner.text]
        O --> Q["💾 persist.persist_arena(... arena ...)"]
        Q --> R["📊 progress.stage: arena:done(winner_overall)"]
        M -- ❌ no --> S["📝 Summarizer.baseline(... corpus ...)"]
        S --> P

        %% ========== 🔍 VERIFY & IMPROVE LOOP ==========
        P --> T[📊 progress.stage: verify:start]
        T --> U["🔍 Summarizer.verify_and_improve(baseline)"]
        U --> V{📈 metrics.overall >= threshold?}
        V -- ✅ yes/early stop --> W[📦 iterations collected]
        V -- ❌ no/iterate --> U
        U --> X["⚙️ StrategyManager.evolve(iterations, ctx_case)"]
        X --> Y["📊 progress.stage: verify:done(overall)"]

        %% ========== 💾 PERSIST ARTIFACTS ==========
        Y --> Z["💾 persist.save_section(... baseline, verify ...)"]
        Z --> ZA["📊 strategy.track_section(..., iterations)"]
        ZA --> ZB["💾 persist.persist_pairs(+/- pairs)"]
        ZB --> ZC["📊 progress.end_section(metrics)"]
        ZC --> ZD["⚙️ StrategyManager.validate_ab(optional)"]

        %% ========== 🔄 NEXT SECTION ==========
        ZD --> F
    end

    %% ========== 📊 PAPER-LEVEL EVIDENCE & REPORTING ==========
    F -->|✅ after all sections| AA["📈 evidence.collect_longitudinal()"]
    AA --> AB["📈 evidence.cross_episode()"]
    AB --> AC["📊 evidence.report(...)"]
    AC --> AD["📋 logger.log(LfL_Paper_Run_Complete)"]
    AD --> AE["✅ Return {context + paper_out}"]

    %% ========== APPLY STYLES ==========
    class A start
    class B decision
    class C,D,E,F process
    class G decision
    class H,I,J process
    class K,L corpus
    class M decision
    class N,O,P,Q,R arena
    class S process
    class T,U,V,W,X,Y verify
    class Z,ZA,ZB,ZC,ZD persist
    class AA,AB,AC,AD,AE evidence
  

🪤 From diagram → code

The flowchart you just saw is the contract; the code below is the implementation. Read it with these waypoints in mind:

• Entry & setup → LearningFromLearningAgent.__init__ Wires up the moving parts the diagram showed: StrategyManager (strategy parameters + AB), CorpusService (retrieval), Summarizer (draft+refine), ArenaService (self-play), Persistence (casebook artifacts), Evidence (metrics), and ProgressAdapter (telemetry).

• Top-level loop → run(context) Mirrors the outer bands of the diagram: resolve papers → iterate sections → return a paper_out bundle with results plus evidence.

• Corpus → Draft path

  • Retrieval: corpus.fetch() (diagram’s blue boxes).

  • Drafting branch: use_arena gate.

    • Arena path: _build_candidates(...) → arena.run(...) → baseline = winner.text.
    • Baseline path: summarizer.baseline(...). This is where knowledge enters the system (retrieved snippets + section seed).

• Verify & improve loop → summarizer.verify_and_improve(...) Iterates until metrics.overall ≥ verification_threshold. Each turn logs an iteration record (score + weaknesses), feeding StrategyManager.evolve(...) exactly as in the diagram’s loop.

• Persistence (breadcrumbs) persist.save_section(...) stores: section text, initial draft, refined draft, verification report, and a compact metrics scorable. persist.persist_arena(...) (if used) writes pool/beam/winner plus per-round summaries these are the attribution handles you’ll use later to prove applied knowledge.

• Strategy telemetry & AB strategy.track_section(...) writes a compact per-section evolution snapshot. strategy.validate_ab(...) periodically aggregates enrollments and returns deltas, t-tests, and effect sizes.

• Evidence reports After all sections: evidence.collect_longitudinal() and evidence.cross_episode() compute the headline stats (Attribution Rate, Applied-Knowledge Lift, Transfer, etc.), and evidence.report(...) renders a human-readable summary.

✔️ How to “spot the proof” in code quickly

• Look for where claims meet sources: persist.persist_arena(...) (winner + citations) and metrics.final_scores in save_section(...).
• Look for when knowledge changes outcomes: iteration deltas inside verify_and_improve(...).
• Look for whether changes generalize: StrategyManager.validate_ab(...) outputs and Evidence.cross_episode() transfer signals.

With that map, the code block below should read like a faithful, line-by-line realization of the mermaid flow plus the breadcrumbs we need to show this is truly applied knowledge.

🕵️‍♀️ The Learning-from-Learning Agent

At the center of Stephanie’s pipeline is the LearningFromLearningAgent the orchestrator that actually turns raw papers into scored, reusable knowledge. Think of it as a conductor managing several specialized players:

• CorpusRetriever → fetches candidate passages from prior chats and documents.
• Arena (KnowledgeArena) → runs head-to-head competitions between candidate summaries to pick the strongest one.
• Summarizer → drafts, verifies, and iteratively improves section summaries.
• Scoring → applies knowledge scorers (human + AI heads, calibrated).
• StrategyManager → tracks policies, runs A/B validation, and adjusts verification thresholds.
• Persistence → stores CaseBooks, pairs, and results so they become training material.
• Evidence → generates longitudinal + cross-episode reports.
• AttributionTracker → tracks what sources supported the winning outputs.

The agent works section by section:

1. Retrieve pull candidate text (from corpus, section seed, or arena-generated).
2. Draft generate a baseline or run an arena match to select the best candidate.
3. Verify & Improve run iterative refinement until the summary passes knowledge and grounding checks.
4. Persist save the case into CaseBooks and generate training pairs for the knowledge trainer.
5. Score & Track log metrics, update strategies, and produce proof-of-learning reports (including ablation tests when enabled).

All of this is wrapped with progress tracking, reporting hooks, and health monitoring, so Stephanie not only learns from each section but also learns how she is learning.

The LearningFromLearningAgent implements a knowledge application loop that transforms retrieved information into verified, improved outputs through structured competition and refinement.

class LearningFromLearningAgent:
    """Applies retrieved knowledge through competitive refinement and verification"""
    
    def __init__(self, cfg, memory, container, logger):
        # Strategy management (evolves verification approach)
        self.strategy = StrategyManager(cfg, memory, container, logger)
        
        # Knowledge retrieval (finds relevant prior conversations)
        self.corpus = CorpusRetriever(cfg, memory, container, logger)
        
        # Candidate generation and improvement
        self.summarizer = Summarizer(...)  # Draft + refine
        self.arena = KnowledgeArena(...)   # Competitive refinement
        
        # Persistence and evidence collection
        self.persist = Persistence(...)
        self.evidence = Evidence(...)

🔁 Key Processing Loop

    flowchart LR
    A[Paper Section] --> B[Retrieve Knowledge]
    B --> C[Generate Candidates]
    C --> D[Arena Competition]
    D --> E[Verify & Improve]
    E --> F[Persist Results]
    F --> G[Collect Evidence]
    G --> H[Evaluate Strategy]
    H --> A
  

🏆 Arena: Where Knowledge Competes and is Measured

Applying knowledge isn’t just about retrieving information; it’s about testing how that information is used. The Arena provides this testbed by forcing multiple candidate applications of knowledge to compete directly.

Here’s how the Arena process applies knowledge within a section:

1. Retrieve: corpus.fetch(section_text) brings in relevant prior conversations related to the specific section_text.
2. Seed Application: cands = self._build_candidates(section, corpus_items) turns retrieved items into potential ways to summarize/apply the knowledge.
3. Compete & Score: arena_res = await self.arena.run(section_text, cands) pits these candidates against each other. Each candidate’s score reflects how well its specific application of knowledge (combined with clarity and grounding) works for the given section.
4. Select Winner: The arena_res["winner"] represents the most successful application of knowledge identified for this specific section in this round.
5. Verify & Refine: verify = await self.summarizer.verify_and_improve(...) takes the Arena winner and further refines it, continuously measuring if new applications of knowledge improve the result, and recording the evidence of this improvement.

This flow ensures that knowledge isn’t just retrieved, but its application is actively tested, measured, and selected based on its effectiveness for the specific task (summarizing the section). The agent uses an Arena tournament to competitively apply knowledge:

📊 Evidence Collection & Strategy Evolution

The agent collects irrefutable proof of learning:

# Track cross-episode improvement
self.strategy.track_section(saved_case, verify["iterations"], context)

# Collect longitudinal metrics
longitudinal = self.evidence.collect_longitudinal(context=context)

# Generate evidence report
report_md = self.evidence.report(longitudinal, cross, context=context)

🎯 Key Features

1. Knowledge Attribution

• Tracks which specific conversations contributed to improvements
• Logs provenance chains for every claim
• Enables ablation testing to prove causal contribution

{
  "winner_claims": [
    {
      "claim": "LoRA adapters can match full fine-tuning at ~1–10% of trainable params.",
      "support": {
        "origin": "chat_corpus",
        "variant": "c14291",
        "turn_id": 14291,
        "text": "LoRA reduces trainable parameters by decomposing weight updates...",
        "tags": ["domain:lora", "paper:2303.XXXX"]
      },
      "similarity": 0.89
    }
  ],
  "attribution_rate": 0.78
}

We can see prior text grounded the claim, its origin, and similarity.

2. Strategy Evolution

• Adapts verification thresholds based on performance
• Evolves skeptic/editor/risk weights over time
• Uses A/B testing to validate strategy changes

3. Ablation Testing

• Masks specific knowledge elements and measures impact
• Proves “learning from learning” through causal evidence
• Shows measurable score drops when key knowledge is removed

Protocol:

• 1. Identify the top-2 supporting items for each winner claim (by similarity).
• 2. Mask those keys (corpus:, arena:chat_corpus#c) and re-run the same section.
• 3. Record deltas on overall, grounding, and knowledge_score.

{
  "with": {"overall": 0.88, "grounding": 0.91},
  "without": {"overall": 0.76, "grounding": 0.80},
  "delta": {"overall": -0.12, "grounding": -0.11},
  "masked_keys": ["corpus:14291","arena:chat_corpus#c14291"]
}

Causal: hiding the exact support predictably drops scores

4. Evidence Reporting

• Generates markdown reports with key metrics
• Tracks verification score improvements over time
• Shows cross-episode knowledge transfer patterns

🚀 Getting better at getting better

This isn’t just another summarization agent - it’s a system that:

• Learns how to verify better across papers
• Applies specific prior knowledge with attribution
• Proves causal contribution through ablation tests
• Compounds improvements across episodes

The agent transforms “incremental improvement” into “learning from learning” by making every knowledge application traceable, measurable, and verifiable.

Key takeaway: This agent doesn’t just get better at tasks - it gets better at getting better by learning from its own verification process across multiple papers.

🧭 Chat Corpus Retriever: what it is, why it exists, and how the agent uses it

What it is. The Chat Corpus Retriever is the agent’s knowledge tap. Given a section of a paper, it pulls the most relevant prior chats/notes/snippets to seed a draft and then justify improvements. In code, this is the CorpusRetriever.fetch(...) you wired into the Learning-from-Learning agent.

Why it exists. If we’re going to claim “applied knowledge,” we need specific, attributable prior text to (a) inspire drafts and (b) be cited/ablate later. The retriever supplies both.

📦 How it’s used inside the agent

1.Before drafting (per section) LearningFromLearningAgent.run → fetch()

• We call fetch(section_text, ...) to get top-K snippets.
• These are used as seeds/candidates (via _build_candidates) and as hints for the baseline summarizer.

2.During self-play (arena) ArenaService.run(...)

• Some candidates are directly drawn from corpus items (“chat_corpus” origin).
• The arena improves/competes them; scoring prefers candidates that show clarity/grounding.

3.For attribution & later ablation fetch(..., attribution_tracker=...)

• Each returned item gets a stable attribution_id (e.g., corpus:123).
• We persist these links; later we can mask the exact IDs and rerun as an ablation to prove causal impact.

🛢️ What the retriever actually does (pipeline)

• Querying: Calls your chat_corpus tool with semantic/entity/domain weights.
• Tag-aware selection: Optional hard filter (tag_mode="require") or soft boost (tag_mode="prefer") over conversation tags (e.g., paper:2310.00001, domain:rlhf, audience:left).
• Corpus scoping: Optionally restricts to a dedicated corpus_id (great for controlled tests).
• Allow/Mask: Supports allow_keys and mask_keys so experiments can whitelist specific items or hide them for ablations.
• Attribution: Records contribution metadata (source, id, score, tags) via AttributionTracker.
• Light post-processing: Optionally annotates/analyzes items (NER, heuristics) for downstream features.

Code anchors:

• CorpusRetriever.fetch (retrieval, tags, masks, attribution)
• LearningFromLearningAgent._build_candidates (turn items into arena seeds)
• LearningFromLearningAgent._run_with_mask (ablation reruns)
• ArenaService.run (competition)
• Persistence.persist_arena / persist_pairs (logging & reuse pairs)

⚙️ Key strategy parameters you can set

• K / weights: chat_corpus_k, and {semantic, entity, domain} weights.
• Tags: tags_any, tags_all, tags_none and tag_mode={"require"|"prefer"}; tag_boost if “prefer”.
• Scope: corpus_id to point at a reduced/curated corpus.
• Ablation: mask_keys / allow_keys to surgically include/exclude items.
• Attribution: attribution_tracker to log contributions automatically.

📡 Typical call sites

Baseline retrieval (production):

items = await self.corpus.fetch(
    section["section_text"],
    attribution_tracker=self.attribution,
    tags_any=["domain:lora", "paper:2310.00001"],
    tag_mode="prefer",        # boost but don't exclude
    tag_boost=0.35
)

Controlled test (isolate to a mini-corpus):

items = await self.corpus.fetch(
    section["section_text"],
    corpus_id="lfl_test_corpus",  # only test items
    tag_mode="require",            # hard filter on tags below
    tags_all=["source:curated"]
)

Ablation rerun (prove causality):

masked = await self._run_with_mask(
    paper, section, ctx_case,
    mask_keys={"corpus:123", "arena:chat_corpus#c123"}  # hide a specific support
)

📳 Signals it creates (the stuff we use as proof)

• Attribution IDs for each used snippet (corpus:<id>), persisted with the case.
• Arena provenance (origin, variant) showing when a corpus seed wins.
• Knowledge pairs (positive/negative) to train reuse and evaluate transfer.
• Ablation deltas: score drop when masked → causal evidence of applied knowledge.

🔖 When to prefer tags vs. corpus_id

• Use corpus_id when you want hard isolation (e.g., paper-specific validation corpus).
• Use tags when you want to steer retrieval inside the full index (audience, domain, campaign).

🎉 Gotchas & best practices

• Drift: Keep section text clean; noisy prompts reduce semantic match quality.
• Over-boosting: With tag_mode="prefer", don’t set an excessive tag_boost; you can starve genuinely relevant non-tagged items.
• Mask both sides: When ablating a corpus item that also appears as an arena candidate, mask both keys (corpus:<id> and arena:chat_corpus#c<id>), as your agent already does.
• Log everything: Always pass the AttributionTracker it’s your audit trail and the foundation for the “Applied Knowledge” proof.

Bottom line: The Chat Corpus Retriever is where knowledge enters the loop. It narrows the world to the most relevant prior conversations, labels them so we can attribute and ablate, and feeds both the baseline and the arena. That’s how “learning from learning” becomes measurable and defensible.

Next we will look at how we use this knowledge to make decisions…

🔄 Why We Started with Conversations

When we looked back at everything we’d built so far, one fact stood out: our conversations were the most valuable knowledge we had.

These weren’t abstract datasets scraped from the web. They were the real back-and-forths where problems got solved, systems got debugged, and ideas turned into working code. Every blog post, every diagram, every agent we designed if you trace it back far enough, it started in a conversation.

In there is knowledge we can see it or measure it yet but we believe that given enough samples the machine can.

That’s why we made chat history the foundation of our system. The chats are the knowledge.

🧠 Human vs. Machine Learning

Machines learn by brute force: weigh one option against another, repeat 10,000 times, slowly edge toward something better. But humans don’t work like that.

When a human learns, they:

1. Start with a conversation talk it through.
2. Pick out what matters “this part is the grocery list I actually need.”
3. Try different applications test one approach, then another.
4. Keep track of mistakes remember what failed and why.

That is the human pattern of learning from learning.

👩‍💻 Where the Humanity Lives in Stephanie

So when we ask, “where’s the humanity in this system?” the answer is simple: it’s everywhere.

1. Conversations first. Our foundation is not raw documents or scraped datasets it’s dialogues between humans and AIs, tied to concrete tasks.
2. Human scoring. Every turn can be starred by a reviewer (RHLF²). This isn’t just about “better or worse” it’s about surfacing the moments of genuine learning.
3. Selective attention. We tag conversations by domains, entities, and goals. Just like a person deciding which part of the discussion matters, the system filters and prioritizes.
4. Tracked trajectories. We don’t just record outputs we track how solutions evolve, what went wrong, and how corrections were made.

⚔️ Why the Arena Matters

All of this feeds into the Arena the place where knowledge isn’t just retrieved but applied deliberately. Instead of letting the AI paraphrase or bluff, the Arena forces multiple candidate applications of the same knowledge to compete. It’s the closest we’ve come to capturing how a human thinks: consider alternatives, test them, keep the best, and remember why.

🎯 The Core Difference

That’s what makes Stephanie different. Most systems are built to make AIs think like AIs optimize weights, iterate endlessly, hope better patterns emerge. We built Stephanie to think like a human: start with conversations, prefer human judgment, test deliberately in the Arena, and track the whole process.

This isn’t just “learning.” It’s learning from learning the deliberate, provable application of knowledge, with humans at the center.

🧭 Why an “Arena”

When we wired the system to ingest thousands to tens of thousands of scorable signals chat corpus hits, casebook artifacts, multi-agent revisions we needed a decision loop that could digest a huge pool fast, apply knowledge on purpose, and produce auditable receipts. The Arena won because it’s the only loop that scales to that density while staying controllable and provable.

💢 What problem the Arena actually solves

• Scale without chaos: Competes many knowledge-bearing candidates, but with bounded compute: O(beam_width × rounds) not O(N).
• Applied knowledge, not vibes: Each candidate carries origin/variant/meta. Winners and beams are persisted, so you can ablate later and watch scores drop.
• Early, cheap convergence: Stops when marginal reward per k-tokens falls below a threshold or the best score plateaus.
• Knowledge diversity: A diversity guard prevents monocultures (e.g., only corpus seeds).
• Tunable & reproducible: Beam width, rounds, plateau epsilon, marginal reward, verification thresholds all strategy parameters you can A/B cleanly.

🏰 How the Arena is designed

Here’s a simple block diagram of the Arena. It shows how candidates are scored, improved, re-scored, and iterated in rounds until progress plateaus ending with the winning, most grounded draft and full telemetry.

    flowchart TD
  %% ===================== Styles (high-contrast) =====================
  classDef start     fill:#001f3f,stroke:#7FDBFF,stroke-width:3px,color:#FFFFFF
  classDef io        fill:#111111,stroke:#FFDC00,stroke-width:2px,color:#FFDC00
  classDef score     fill:#85144b,stroke:#FF4136,stroke-width:2px,color:#FFFFFF
  classDef select    fill:#2ECC40,stroke:#001f3f,stroke-width:2px,color:#001f3f
  classDef loop      fill:#B10DC9,stroke:#F012BE,stroke-width:2px,color:#FFFFFF
  classDef improve   fill:#3D9970,stroke:#2ECC40,stroke-width:2px,color:#FFFFFF
  classDef guard     fill:#FF851B,stroke:#FF4136,stroke-width:2px,color:#111111
  classDef stop      fill:#FF4136,stroke:#85144b,stroke-width:3px,color:#FFFFFF
  classDef win       fill:#39CCCC,stroke:#0074D9,stroke-width:3px,color:#001f3f
  classDef out       fill:#7FDBFF,stroke:#001f3f,stroke-width:2px,color:#001f3f

  %% ===================== Flow =====================
  A["🚀 Start Arena.run(section_text, candidates)"]:::start --> B["📥 Initial candidates<br/>(retrieved + seed)"]:::io
  B --> C["🧮 Score all candidates<br/>(K/C/G + verified)"]:::score
  C --> D["🔢 Stable sort by:<br/>verified ▶ overall ▶ length"]:::select
  D --> E["🎯 Take top N = beam_width<br/>(Beam)"]:::select

  E --> F{🔁 Self-play rounds ≤ max_rounds}:::loop

  subgraph Round["🧪 Round r"]
    direction TB
    F --> G["🛠️ Improve each beam candidate<br/>improve(text, meta+round)"]:::improve
    G --> H["🧮 Re-score improved texts"]:::score
    H --> I["🔢 Re-sort new beam"]:::select
    I --> J{"🧬 Diversity guard?<br/>(avoid monoculture)"}:::guard
    J -- "Yes" --> K["♻️ Swap in alt origin<br/>(from initial pool)"]:::guard
    J -- "No" --> L[" "]:::guard
    K --> M["📈 Compute marginal Δscore / k-tokens"]:::loop
    L --> M
    M --> N{"🛑 Stop if:<br/>Δ/kTok &lt; min_marg<br/>OR plateau &lt; eps"}:::stop
    N -- "Stop" --> O["🏁 Choose current best"]:::win
    N -- "Continue" --> P["➡️ Set beam = top N<br/>prev_best = curr_best"]:::loop
  end

  O --> Q["🏆 Winner"]:::win
  P --> F

  Q --> R["🧾 Outputs:<br/>winner, beam history,<br/>iterations, reasons,<br/>stop_reason"]:::out

  %% ===================== Legends =====================
  %% (Kept compact to avoid syntax issues; labels include emojis)

  %% ===================== Classes =====================
  class A start
  class B io
  class C score
  class D,E select
  class F loop
  class G improve
  class H score
  class I select
  class J,K guard
  class M loop
  class N stop
  class O,Q win
  class R out
  

Legend (quick map):

• 🧮 Score: score_candidate(text, section_text) → K/C/G + overall, verified
• 🛠️ Improve: improve(text, meta) → targeted edits using prior knowledge
• 🧬 Diversity guard: ensures beam includes multiple origins (e.g., corpus vs. seed)
• 🧪 Round r: iterate until low marginal reward per k-tokens or plateau
• 🏆 Winner: highest verified overall; all telemetry returned for persistence/ablation

1. Seed the competition

   • Candidates come from two high-signal sources: (a) retrieved chat-corpus snippets (knowledge-rich), (b) a safe seed from the section text (coverage).

2. Score → Improve → Re-Score (self-play rounds)

   • score_candidate() uses our K/C/G rubric (Knowledge, Clarity, Grounding) to produce overall + verified.
   • improve() calls the Summarizer’s improve_once() which targets explicit weaknesses; provenance is preserved.

3. Early stop

   • If the best score barely improves (plateau) or marginal reward / k-tokens falls below min_marg, we stop.

4. Pick a winner with receipts

   • We persist the initial pool, per-round beams, the winner, and compact round telemetry. That unlocks ablation and attribution after the fact.

📲 Why this fits our data reality

• With the corpus + casebooks + agents, we expect lots of viable contenders. The Arena embraces that: it lets knowledge compete and keeps only what demonstrates lift.
• It’s faster than tree search or RL in our setting, because the search space is mostly “how to apply known facts,” not discovering long latent action sequences.

🔛 Key strategy parameters (defaults you can ship)

🍎 Minimal core (the loop in ~20 lines)

def run(self, section_text: str, initial_candidates: List[Dict]) -> Dict[str, Any]:
    # 1) initial scoring
    scored = [{**c, "score": self._safe_score(lambda: self.score_candidate(c["text"], section_text))}
              for c in initial_candidates]
    scored.sort(key=lambda x: (x["score"].get("verified", False), x["score"]["overall"], len(x["text"])), reverse=True)
    beam = scored[: self._beam_w]; iters = []; best_hist = []; prev_best = beam[0]["score"]["overall"]; prev_toks = self._tok(beam[0]["text"])
    for r in range(self._max_rounds):
        new_beam = []
        for cand in beam:
            improved = self._safe_improve(lambda: self.improve(cand["text"], {**(cand.get("meta") or {}), "round": r}), cand["text"])
            s = self._safe_score(lambda: self.score_candidate(improved, section_text))
            new_beam.append({**cand, "variant": f'{cand.get("variant","v")}+r{r+1}', "text": improved, "score": s})
        new_beam.sort(key=lambda x: (x["score"].get("verified", False), x["score"]["overall"], len(x["text"])), reverse=True)
        if self._enable_diversity_guard and len({b.get("origin") for b in new_beam}) == 1:
            alt = next((c for c in scored if c.get("origin") not in {b.get("origin") for b in new_beam}), None)
            if alt: new_beam[-1] = alt
        curr_best = new_beam[0]["score"]["overall"]; curr_toks = self._tok(new_beam[0]["text"])
        marg = ((curr_best - prev_best) / max(1, curr_toks - prev_toks)) * 1000.0
        iters.append([{"variant": b["variant"], "overall": b["score"]["overall"], "k": b["score"].get("k", 0.0)} for b in new_beam])
        if marg < self._min_marg or (best_hist and (curr_best - best_hist[-1]) < self._plateau_eps): break
        best_hist.append(curr_best); beam, prev_best, prev_toks = new_beam[: self._beam_w], curr_best, curr_toks
    return {"winner": (beam or scored)[0], "beam": beam, "initial_pool": scored, "iterations": iters}

🛒 What this buys us downstream

• Ablation-ready: Because each candidate keeps origin/variant, we can mask a source later and measure the drop (causal proof).
• Evidence pages for the blog: Persistence writes the initial pool, beams, winner, and round summaries perfect to screenshot and explain how knowledge won.
• Strategy evolution: The Summarizer’s verify_and_improve logs iteration deltas; StrategyManager can A/B thresholds and weights without touching Arena internals.

TL;DR: We chose the Arena because our system produces a lot of good ideas. The Arena is a compact, early-stopping tournament that turns that abundance into fast convergence, measurable lift, and auditable attribution the exact ingredients we need to prove learning from learning.

With a candidate winning each round in the Arena, we now ask: did knowledge actually improve the draft and by how much? The Verification & Improvement loop answers this by scoring each revision on Knowledge (K), Clarity (C), and Grounding (G), then iterating only while it’s pulling its weight.

We built a viewer to allow you to review the results.

👁️‍🗨️ Arena Viewer in SIS

⌛ Arena: Live & History

🎠 How to use it

• Start from the Runs list (latest first), then open either Live (to replay with charts) or Details (to inspect raw events).
• In Details/History, filter by run_id or event (e.g., round_end, initial_scored, arena_done) and click rows to see the full payload.

Caption: The Live view replays the run round-by-round. The line chart tracks best overall per round (left tab) and marginal per kTok (right tab), while the timeline shows emitted events (e.g., round_end, arena_done). The Top-K Preview table summarizes leading candidates with their overall and k-scores and whether they were verified. The “sawtooth” shape you see is typical as the arena explores candidates: scores fluctuate as worse/better variants are sampled, with occasional jumps when a stronger candidate surfaces late in the run.

🎡 What this is showing / why it matters

• Each dot = one arena round; connecting lines make progress trends obvious.
• Small dips are expected exploration; upward steps signal real improvements.
• The final point/summary (winner score, rounds run, reason) is the run’s outcome.
• Use this view to sanity-check behavior as it happens or when replaying a single run from history.

⏺️ Arena History (post-hoc auditing)

Caption: The History view loads all events from the database for a given run_id. On the left you can filter and scan events (subject, event, time); on the right you get a prettified, unwrapped JSON payload for the selected event. This is ideal for auditing, debugging, and traceability: you can reconstruct how the final winner emerged, confirm which rounds moved the needle, and inspect any metadata emitted along the way.

🧪 Reproduce the proof (reader checklist)

1. Locate the claim in the final draft → open its attribution record (claim, source excerpt, similarity, iteration).
2. Check the lift at that iteration (e.g., +0.09 overall; grounding ↑).
3. Oh yeah I knowMask the source and re-run; a consistent drop confirms causation, not correlation.

📊 Section 5: Measurement of Learning

    flowchart LR
  A[🧩 Knowledge Ingestion] --> B[🧾 Knowledge Processing]
  B --> C[🧪 Knowledge Training & Understanding]
  C --> D[🚀 Knowledge Application]
  D --> E[📊 Measurement of Learning]

  %% Styles
  classDef ingest fill:#e6f7ff,stroke:#1c75bc,stroke-width:3px,color:#000;
  classDef process fill:#d0e7ff,stroke:#1c75bc,stroke-width:2px;
  classDef train  fill:#f0f0f0,stroke:#555,stroke-width:2px,color:#000,color:#000;
  classDef apply  fill:#fff7e6,stroke:#e69500,stroke-width:2px;
  classDef measure fill:#f9f0ff,stroke:#8e44ad,stroke-width:2px,color:#000,color:#000,font-weight:bold,stroke-dasharray: 5 2;

  %% Assign classes
  class A ingest;
  class B process;
  class C train;
  class D apply;
  class E measure;
  

🔍 phronēsis: Attribution & Traceability: Following the Knowledge Trail

Having shown how knowledge is applied within a task (Section 4), we now measure how it flows across tasks and whether the system learns to apply it more effectively over time. This section deepens two things: (1) traceable causation from source → claim → lift, and (2) strategy evolution validated by A/B tests.

After verification proves knowledge was applied, we need to show exactly how it flowed from source to output. This is where most systems fail they verify outputs but can’t trace which specific knowledge caused which specific improvement.

Without traceability, you have correlation but not causation. Our system closes this gap with auditable knowledge trails that let anyone inspect the causal chain.

🗺️ The Knowledge Flow: From Source to Verified Output

    flowchart LR
    %% === Circular Nodes Version ===
    A([📚 Knowledge Sources]) --> B([🔍 Retrieval Pool])
    B --> C([🏆 Arena Candidates])
    C --> D([🔄 Verification Loop])
    D --> E([📝 Attribution Records])
    E --> F([🛤️ Knowledge Trail])
    F --> G([🚀 Cross-Paper Transfer])
    
    %% === Example Flow ===
    A -.->|📄 LoRA Paper| B
    B -.->|🔖 c1287: Attention...| C
    C -.->|🥇 Candidate 3: 0.81| D
    D -.->|🔄 Iteration 2| E
    E -.->|📌 claim + support| F
    F -.->|📈 +0.08 lift| G
    
    %% === Styling ===
    classDef source fill:#dbeafe,stroke:#3b82f6,stroke-width:3px,color:#1e3a8a
    classDef pool fill:#e0e7ff,stroke:#6366f1,stroke-width:3px,color:#3730a3
    classDef arena fill:#dcfce7,stroke:#22c55e,stroke-width:3px,color:#166534
    classDef verify fill:#fef3c7,stroke:#f59e0b,stroke-width:3px,color:#92400e
    classDef attr fill:#f3e8ff,stroke:#a855f7,stroke-width:3px,color:#6b21a8
    classDef trail fill:#ffe4e6,stroke:#f43f5e,stroke-width:3px,color:#9f1239
    classDef transfer fill:#ccfbf1,stroke:#14b8a6,stroke-width:3px,color:#0f766e
    
    class A source
    class B pool
    class C arena
    class D verify
    class E attr
    class F trail
    class G transfer
  

👣 How to read the diagram (the knowledge trail)

• Knowledge Sources → Retrieval Pool: Our system retrieves specific knowledge snippets (like LoRA paper’s attention mechanism description).
• Retrieval Pool → Arena Candidates: These snippets become candidate sources in the Arena competition.
• Arena Candidates → Verification Loop: The winning candidate enters verification where we target weaknesses.
• Verification Loop → Attribution Records: When improving the draft, we match new/changed sentences to knowledge sources.
• Attribution Records → Knowledge Trail: Each claim is linked to its source with similarity score, creating an auditable trail.
• Knowledge Trail → Cross-Paper Transfer: These trails enable knowledge transfer between papers (LoRA → QLoRA).

The smoking gun: For any sentence in the final output, you can trace it back to its knowledge source and see exactly how much it improved the score.

🧪 The Traceability Engine: How We Build the Knowledge Trail

📊 1. Building the Evidence Pool (Where knowledge lives)

# In LearningFromLearningAgent.run()
ctx_case["retrieval_items"] = [
    {"id": it.get("id"), "text": (it.get("assistant_text") or it.get("text") or "")}
    for it in (corpus_items or [])
]

ctx_case["arena_initial_pool"] = [
    {"origin": c.get("origin"), "variant": c.get("variant"), "text": c.get("text", "")}
    for c in (arena_res.get("initial_pool") or [])
]

Why this matters: Before verification even begins, we create a unified evidence pool combining:

• Retrieved knowledge snippets (retrieval_items)
• Arena candidate sources (arena_initial_pool)

This pool becomes our “source of truth” for attribution.

📚 2. Claim Attribution: Linking Sentences to Sources

Attribution threshold. We treat a claim as “supported” only if cosine similarity with a source ≥ τ (default 0.82). We set τ by sweeping 0.70–0.95 on a held-out set and picking the knee where precision stops improving materially.

ATTR_THRESHOLD = float(self.cfg.get("attr_threshold", 0.82))
matches = list(self._attribute_claims(claims, sources, threshold=ATTR_THRESHOLD))


# In Summarizer.improve_once(..., return_attribution=True)
def _attribute_claims(self, claims: List[str], sources: List[Dict[str, Any]], threshold: float) -> List[Dict[str, Any]]:
    # Pre-embed knowledge sources for fast matching
    S = [{"meta": s, "v": self.embedding.get_or_create(s["text"][:2000])} 
         for s in sources[:50]]
    
    for c in claims:
        # Find best matching knowledge source
        cv = self.embedding.get_or_create(c)
        best, best_sim = None, 0.0
        for s in S:
            sim = self._cos_sim(cv, s["v"])
            if sim > best_sim:
                best_sim, best = sim, s["meta"]
        
        # Only count as "applied knowledge" if match is strong
        if best and best_sim >= threshold:
            yield {
                "claim": c,
                "support": {
                    "text": best["text"][:220],
                    "origin": best["origin"],
                    "variant": best["variant"]
                },
                "similarity": round(best_sim, 3)
            }

Why this proves traceability: We don’t just store knowledge we actively match specific sentences to specific knowledge sources. Each attribution record contains:

• The claim sentence
• The supporting source (with excerpt)
• The similarity score (proving it’s not a random match)

✅ 3. Persistence: Creating the Audit Trail

# In Persistence.save_section() (your uploaded code)
if context.get("case_id") and matches:
    payload = {"claims": matches, "threshold": th, "timestamp": time.time()}
    self.memory.casebooks.add_scorable(
        case_id=context["case_id"],
        role="improve_attribution",
        text=dumps_safe(payload),
        pipeline_run_id=context.get("pipeline_run_id"),
        meta={"iteration": context.get("iteration")}
    )

Why this creates an audit trail: Every attribution record is persisted as a scorable with:

• Precise timestamp
• Iteration number
• Knowledge threshold used
• Full claim→source mapping

This creates a complete timeline of how knowledge flowed through the verification process.

🔍 Real Traceability Example: Following a Claim to Its Source

Let’s trace a specific claim from the LoRA paper through to the QLoRA paper:

The traceability proof:

1. In the LoRA paper, we identified this claim as important knowledge
2. When processing QLoRA, our system retrieved this snippet
3. During verification, the claim was applied in iteration 2
4. The score jumped from 0.75 → 0.84 (+0.09) at that exact moment
5. When we masked this knowledge source, the score dropped to 0.76

This isn’t correlation it’s a complete causal chain from knowledge source to applied improvement.

📊 What a Reader Can Verify (The Traceability Checklist)

For any section in your paper, a reader should be able to:

1. Find the attribution record for any claim

   {
     "claim": "Attention scales quadratically with sequence length",
     "support": {
       "text": "The attention mechanism computes pairwise relationships between all tokens...",
       "origin": "chat_corpus",
       "variant": "c1287"
     },
     "similarity": 0.87
   }
   

2. Follow the knowledge trail across papers

   {
     "source_paper": "LoRA",
     "source_section": "Methods",
     "target_paper": "QLoRA",
     "target_section": "Methods",
     "similarity": 0.84,
     "score_lift": 0.09
   }
   

3. See the verification timeline where knowledge was applied

   {
     "iteration": 2,
     "score": 0.84,
     "knowledge_applied": true,
     "first_knowledge_lift": 0.09
   }
   

4. Confirm with ablation that removing the source causes a drop

   {
     "with": {"overall": 0.84, "knowledge_score": 0.87},
     "without": {"overall": 0.75, "knowledge_score": 0.78},
     "delta": {"overall": 0.09, "knowledge": 0.09}
   }
   

💡 Why This Matters for “Learning from Learning”

Traceability transforms our system from:

• “The system got better over time” → correlation
• To “This specific knowledge from Paper A caused improvement in Paper B” → causation

Without this traceability, we couldn’t prove true “learning from learning” only incremental improvement within tasks.

➡️ What’s Next: Strategy Evolution & A/B Testing

Now that we’ve proven knowledge is being traced and transferred, the next section shows how the system learns which verification strategies work best through A/B testing. We’ll see how the system:

• Evolves its skeptic/editor/risk weights based on verification outcomes
• Validates strategy changes through controlled experiments
• Gets better at getting better through self-audited learning

This creates the complete “learning from learning” loop: from knowledge retrieval → application → verification → attribution → strategy evolution.

🧪 Strategy Evolution & A/B Testing: Getting Better at Getting Better

After proving knowledge was applied and traced, the final piece of our “learning from learning” puzzle is strategy evolution. This is where our system doesn’t just improve within tasks it gets better at getting better through self-audited learning.

🔍 Why Strategy Evolution Matters

Most systems stop at verification they ensure outputs meet quality standards but never ask: “How can we get better at ensuring quality?”

Our system closes this gap by:

• Continuously adapting its verification strategy based on performance
• Testing changes scientifically through A/B testing
• Validating improvements before committing to them
• Learning which approaches work best across different paper types

This creates the complete “learning from learning” loop: from knowledge retrieval → application → verification → attribution → strategy evolution.

⚙️ The Strategy Parameters: What We’re Evolving

Our verification strategy is controlled by four key parameters:

@dataclass(frozen=True)
class Strategy:
    """Immutable strategy configuration for verification and improvement."""
    verification_threshold: float = 0.85  # Minimum overall score to accept draft
    skeptic_weight: float = 0.34           # Weight for knowledge checks
    editor_weight: float = 0.33            # Weight for clarity improvements
    risk_weight: float = 0.33              # Weight for grounding checks
    version: int = 1                       # Version counter for tracking evolution

The three weights are normalized to sum to 1.0 at runtime. This keeps their relative emphasis stable even if configs are edited.Hello

def _normalize_strategy(self):
    s = self.strategy
    total = max(1e-9, s.skeptic_weight + s.editor_weight + s.risk_weight)
    self.strategy.skeptic_weight /= total
    self.strategy.editor_weight  /= total
    self.strategy.risk_weight    /= total

Why these specific knobs?

• verification_threshold: Controls how strict we are about accepting drafts
• skeptic_weight: How much we prioritize knowledge accuracy
• editor_weight: How much we prioritize clarity and structure
• risk_weight: How much we prioritize grounding in sources

These weights sum to 1.0 and represent how we allocate “attention budget” during verification.

🧪 The Strategy Evolution Process

We call _evolve_strategy(...) after each section’s verify-and-improve loop, using that section’s per-iteration gains as the signal.

Here’s how our system evolves its strategy:

    flowchart LR
    A[Track Verification Outcomes] --> B{Avg Gain < Min?}
    B -- Yes --> C[Propose Larger Changes]
    B -- No --> D[Propose Smaller Changes]
    C --> E[Randomly Assign A/B Groups]
    D --> E
    E --> F[Run with New Strategy]
    F --> G{Validate Performance}
    G -- B better --> H[Commit Changes]
    G -- A better --> I[Revert Changes]
    H --> A
    I --> A
    
    classDef process fill:#e0f2fe,stroke:#0ea5e9;
    classDef decision fill:#fef2f2,stroke:#ef4444;
    classDef outcome fill:#f0fdf4,stroke:#10b981;
    
    class A,B,C,D,E,F,G process;
    class H,I outcome;
  

🤷 How to read the diagram

• Track Verification Outcomes: We monitor how much each verification iteration improves scores.
• Propose Changes: If improvements are small, we make larger strategy adjustments.
• A/B Testing: We randomly assign between current strategy (A) and proposed (B).
• Validate Performance: After enough data, we compare performance between groups.
• Commit or Revert: If B performs better, we keep changes; otherwise, we revert.

This creates a closed learning loop where strategy changes are only kept if they’re proven to improve performance.

🔬 The A/B Testing Engine: Scientific Strategy Evolution

🔤 1. Strategy Evolution & A/B Enrollment

def _evolve_strategy(self, iters: List[Dict[str, Any]], context: Optional[Dict[str, Any]]):
    if len(iters) < 2:
        self._record_strategy_state(context, tag="pre_change")
        return
    
    # Calculate average improvement per iteration
    gains = [iters[i]["score"] - iters[i-1]["score"] for i in range(1, len(iters))]
    avg_gain = sum(gains) / len(gains) if gains else 0.0
    
    # Record current state for comparison
    old_strategy = {
        "verification_threshold": self.strategy.verification_threshold,
        "skeptic_weight": self.strategy.skeptic_weight,
        "editor_weight": self.strategy.editor_weight,
        "risk_weight": self.strategy.risk_weight,
        "version": self.strategy.version,
    }
    
    # Propose strategy changes based on performance
    proposed = self._propose_strategy_changes(avg_gain)
    
    # A/B enroll for *next* work unit
    # - A keeps current strategy parameters
    # - B uses proposed strategy parameters
    if random.random() < 0.5:
        # switch to proposed for the next work unit
        self.strategy = proposed
        group = "B"
        # bump version only when actually switching
        self.strategy.version += 1
    else:
        group = "A"
    
    # Record the assignment for later validation
    self._record_strategy_test(
        context, 
        old_strategy=old_strategy, 
        new_strategy=proposed, 
        test_group=group, 
        avg_gain=avg_gain
    )

Why this works:

• We measure actual performance (average gain per iteration)
• We propose changes proportional to need (bigger changes when progress is slow)
• We randomly assign between strategies to avoid bias
• We record all assignments for later validation

🧪 2. Strategy Validation: Proving What Works

def _validate_strategy_effectiveness(self) -> Optional[Dict[str, Any]]:
    """Validate which strategy performed better and commit or revert"""
    test_results = self._get_strategy_test_results()
    
    if not test_results or len(test_results) < 10:  # Need enough data
        return None
    
    # Calculate performance difference between A and B groups
    perf_a = [r["performance"] for r in test_results if r["group"] == "A"]
    perf_b = [r["performance"] for r in test_results if r["group"] == "B"]

    
    if not perf_a or not perf_b:
        return None
    
    avg_perf_a = sum(perf_a) / len(perf_a)
    avg_perf_b = sum(perf_b) / len(perf_b)
    improvement = (avg_perf_b - avg_perf_a) / avg_perf_a * 100
    
    # Log the validation result
    validation_result = {
        "samples_A": len(perf_a),
        "samples_B": len(perf_b),
        "mean_A": avg_perf_a,
        "mean_B": avg_perf_b,
        "delta_B_minus_A": avg_perf_b - avg_perf_a,
        "improvement_pct": improvement,
        "timestamp": time.time()
    }
    
    self.logger.log("StrategyAB_Validation", validation_result)
    
    # improvement is a percentage
    min_improvement_pct = float(self.cfg.get("min_strategy_improvement_pct", 2.0))
    if improvement > min_improvement_pct:
        # Save the validated strategy
        self._save_strategy_version()
    else:
        # Revert to the better-performing strategy
        better_strategy = self._determine_better_strategy(test_results)
        self.strategy = better_strategy
        
        self.logger.log("StrategyReverted", {
            "reason": "insufficient_improvement",
            "improvement_pct": improvement,
            "reverted_to": vars(better_strategy),
            "timestamp": time.time()
        })
    
    return validation_result

Why this proves causation:

• We compare actual performance between strategy variants
• We require statistically significant improvement before committing changes
• We revert changes when they don’t deliver promised improvements
• We log all validation results for auditing

💾 3. Strategy Persistence: Creating an Audit Trail

def _record_strategy_test(
    self, 
    context: Dict[str, Any], 
    old_strategy: Dict[str, Any], 
    new_strategy: Dict[str, Any], 
    test_group: str, 
    avg_gain: float
) -> None:
    """Record A/B test assignment for later validation"""
    payload = {
        "test_group": test_group,
        "old_strategy": old_strategy,
        "new_strategy": new_strategy,
        "avg_gain": avg_gain,
        "timestamp": time.time()
    }
    
    try:
        case_id = context.get("case_id")
        if case_id:
            self.memory.casebooks.add_scorable(
                case_id=case_id,
                role="strategy_ab_enroll",
                text=dumps_safe(payload),
                pipeline_run_id=context.get("pipeline_run_id"),
                meta={"group": test_group}
            )
    except Exception:
        pass

Why this creates an audit trail:

• Every A/B assignment is persisted with metadata
• We can reconstruct the evolution timeline later
• We can correlate strategy changes with performance metrics
• This provides irrefutable evidence of learning from learning

📊 Real Strategy Evolution Example

# Get all strategy versions
versions = memory.models.list_versions("learning_strategy")

# Find the best-performing version
best_version = max(versions, key=lambda v: v.meta.get("performance", 0.0))

# Load and apply the best strategy
strategy_data = memory.models.load("learning_strategy", best_version.version)

Here’s what strategy evolution looks like with actual numbers:

The evolution story:

1. Initial strategy (v1) showed modest gains (~0.055)
2. We proposed increasing skeptic_weight (more focus on knowledge)
3. A/B test showed B group (higher skeptic_weight) outperformed A by 60%
4. We committed the change (v2) and saw consistent gains (~0.095)
5. Further refinement led to v3 with even better performance

Validation result:

{
  "samples_A": 12,
  "samples_B": 15,
  "mean_A": 0.82,
  "mean_B": 0.91,
  "delta_B_minus_A": 0.09,
  "improvement_pct": 10.98,
  "timestamp": 1717984320.456
}

💡 Why This Completes the “Learning from Learning” Loop

Strategy evolution transforms our system from:

• “The system got better over time” → correlation
• To “The system evolved its verification strategy based on proven performance improvements” → causation

Without this final piece, we’d only have incremental improvement. With it, we have self-audited learning the system gets better at getting better through scientific experimentation.

📌 What a Reader Can Verify (The Strategy Evolution Checklist)

For any strategy change, a reader should be able to:

• Attribution: find claim → see {claim, support, similarity, iter}.
• Lift: see lift at that iter and overall/grounding deltas.
• Ablation: confirm with/without and negative delta.
• Transfer: verify from→to edges and impact in the Arena Learning table.
• Strategy: locate strategy_ab_enroll (A/B), then StrategyAB_Validation with sample sizes and delta_B_minus_A.

🎒 Arena Learning

The Arena Learning screens are the operational readout of this section: they aggregate the traceability events and cross-paper edges into a single place where you can audit causal transfer at a glance.

📝 Arena Learning Details

🎢 What you’re seeing

This page focuses on a single pipeline run and surfaces evidence of learning i.e., moments where knowledge from one paper appears to be applied to another. It does this by reading the run’s event stream, sorting it chronologically, and flagging every cross-paper boundary as a candidate knowledge transfer.

🛜 Where the data comes from

• Events are pulled with BusEventStore.payloads_by_run(run_id), which gives ordered rows with paper_id, section_name, agent, event, and timestamps.

• Evidence extraction is done by build_transfer_matrix_from_rows(...) (in stephanie/tools/evidence_extractor.py). For each consecutive event where paper_id changes, we emit a transfer edge:

  • from_paper → to_paper
  • section (the section where the target paper received knowledge)
  • agent, event, timestamp
  • transfer_type (conceptual / procedural / metacognitive) inferred from section/agent hints
  • confidence (0.50–0.95) based on recency, same agent, and section continuity
  • learning_score weighted by type and confidence

• KPIs are aggregated across all edges (count of papers/sections/agents, cross-paper transfers, and an overall Learning Effectiveness Score, which is the average of per-transfer scores, 0–10).

📙 How to read the page

• Header + Profile Context for the run (tag, start time, duration, paper/section hints) and a Learning Effectiveness Score with a progress bar (0–100). This score is a quick proxy for how convincingly knowledge moved between papers during the run.

• Knowledge Transfer Evidence (table) Each row is a transfer event:

  • From / To show the paper IDs involved.

  • Section is where the knowledge was applied in the target paper.

  • Type:

    • 🔦 Conceptual (theory/ideas)
    • ⚙️ Procedural (methods/steps)
    • 🧠 Metacognitive (strategy/approach)

  • Confidence visualizes our certainty that the handoff reflects learning (bar + %).

  • Timestamp uses your |timestamp Jinja filter to render human time.

  Use the chip filters (All / Conceptual / Procedural / Metacognitive) to focus on a single transfer type.

• Learning Insights (cards) Four compact, data-driven observations derived from the evidence:

  • Overall learning strength (via the run’s learning score)
  • Transfer type distribution (e.g., conceptual dominance)
  • Hot sections (e.g., most activity in Methods)
  • Top contributing agent(s) Each card includes a short recommendation you can action in future runs.

• Pipeline Timeline (right column) The first ~50 events for quick provenance event name, agent, paper/section, timestamp so you can correlate transfers against what the pipeline was doing.

• Top Paper→Paper Transfers (right column) A small rollup of the most frequent from → to pairs observed in this run.

🤝🏼 Interactions

• Filter by transfer type with the chip group in the table header (client-side row filtering).
• Export report with the “Export Learning Report” button (stubbed in the template; wire to your report endpoint when ready).
• (Optional) Deep links If you want table cells to open this same Learning view focused on a specific transfer (e.g., to scroll to the timestamp or highlight a pair), add a tiny link wrapper around the badges, or attach a row click handler:

<!-- inside the table row in your Jinja template -->
<td>
  <a class="badge text-bg-secondary text-decoration-none"
     href="/learning/run/{{ run.id }}?focus_from={{ e.from_paper }}&focus_to={{ e.to_paper }}&t={{ e.timestamp|int }}">
     {{ e.from_paper or " " }}
  </a>
</td>
<td>
  <a class="badge text-bg-primary text-decoration-none"
     href="/learning/run/{{ run.id }}?focus_from={{ e.from_paper }}&focus_to={{ e.to_paper }}&t={{ e.timestamp|int }}">
     {{ e.to_paper or " " }}
  </a>
</td>

…and (optionally) in your page script, read those query params to auto-filter/highlight the matching row on load.

🎨 Design notes (why it looks this way)

• The type palette uses stable Bootstrap utility classes (*-subtle, text-*, border-*) rather than string-replace color hacks; this keeps the UI consistent across themes and Bootstrap updates.
• Table rows are kept compact and keyboard-navigable; badges and progress bars include aria-* to improve screen-reader output.
• Everything degrades gracefully when there’s no evidence: the table shows a friendly empty state and the insights panel displays a guidance message.

🗡️ Common edge cases

• No transfers found (e.g., a run that never switches papers) → score near 0, empty table, “Initial/No insights” messaging.
• Sparse metadata (missing section_name or agent) → cells show “ ”, confidence may be lower.
• Timestamp quirks → extractor falls back to extras.publisher_ts if ts is missing.

📝 Troubleshooting checklist

• Seeing empty evidence but you’re sure there were paper switches? Confirm events have paper_id s-et and are arriving in order; the extractor only emits edges when the global chronological stream changes paper_id.
• Confidence looks uniformly low? That typically means sections differ and/or long gaps between events; it’s expected behavior, not an error.

What we measured: where knowledge came from, when it helped, how much it mattered, whether it transfers, and which strategy makes it happen more reliably. That’s not just “better outputs” it’s provable learning from learning.

➡️ The Complete “Learning from Learning” System

We’ve now shown the full “learning from learning” loop in action:

1. Knowledge Retrieval: Pulling relevant information from our corpus
2. Arena Competition: Testing multiple ways to apply knowledge
3. Verification: Proving knowledge was causally applied, not just present
4. Attribution: Tracing claims back to their knowledge sources
5. Strategy Evolution: Getting better at getting better through A/B testing

This creates a system that doesn’t just improve within tasks it learns from the process of learning itself. When we remove specific knowledge elements, scores drop. When we apply unproven strategy changes, we revert them. Every improvement is measurable, attributable, and validated.

This isn’t just AI that writes better we’ve built AI that learns how to learn better, with irrefutable evidence of the causal chain from knowledge source to improved output.

# The complete "learning from learning" loop (compact, faithful)
async def run_learning_loop(paper, section, ctx):
    # 1) Retrieve knowledge
    corpus_items = await corpus.fetch(section["section_text"])

    # 2) Build candidates (retrieved + safe seed)
    candidates = build_candidates(section, corpus_items)  # origin/variant/meta preserved

    # 3) Arena competition (pick best *application* of knowledge)
    arena_res   = await arena.run(section["section_text"], candidates)
    baseline    = arena_res["winner"]["text"]

    # 4) Verify & improve (scored K/C/G; iteration deltas tracked)
    verify = summarizer.verify_and_improve(
        baseline,
        paper=paper,
        section=section,
        context=ctx
    )

    # 5) Persist receipts (for attribution/ablation/evidence)
    case = persist.save_section(
        casebook=ctx["casebook"],
        goal_id=ctx["goal_id"],
        doc_id=str(paper["id"]),
        section_name=section["section_name"],
        section_text=section["section_text"],
        result={
            "initial_draft": {"title": section["section_name"], "body": baseline},
            "refined_draft": {"title": section["section_name"], "body": verify["summary"]},
            "verification_report": {"scores": verify["metrics"], "iterations": verify["iterations"]},
            "final_validation": {"scores": verify["metrics"], "passed": verify["metrics"]["overall"] >= ctx["strategy"].verification_threshold},
            "passed": verify["metrics"]["overall"] >= ctx["strategy"].verification_threshold,
            "refinement_iterations": len(verify["iterations"]),
        },
        context=ctx,
    )
    persist.persist_arena(case, paper, section, arena_res, ctx)  # pool/beam/winner for attribution+ablation

    # 6) Strategy evolution & validation (A/B-ready)
    strategy.track_section(case, verify["iterations"], ctx)
    ab_validation = strategy.validate_ab(ctx)  # optional; logs deltas/effect sizes

    return {
        "summary": verify["summary"],
        "metrics": verify["metrics"],
        "arena_winner": arena_res["winner"],
        "ab_validation": ab_validation,
    }

🏁 Conclusion: The Active Partner in Learning

We’ve built a system that can measure knowledge, ingest it, apply it, and demonstrate learning in action. We’ve shown how to capture breakthrough conversations, train models to recognize knowledge signals, and apply them across agents and tasks. We’ve proven that knowledge isn’t just present but causally applied through verification, attribution, and ablation.

This isn’t the summit it’s a ridge line with a clearer view of the peak.

The real challenge isn’t measuring knowledge; it’s filtering it. It’s not knowing what exists; it’s knowing what matters. The world is overflowing with information, and wisdom is the art of applying the right knowledge to the right problem at the right time.

Yes, the process we’ve built still takes work: ingesting, measuring, applying, verifying, tracing, and evolving strategies are all complex. It hasn’t made knowledge work easy. It’s changed the work from effort to leverage. We’re no longer facing the vastness of information alone we’ve built an active partner.

This system learns from how we learn. It captures judgment, carries insights forward, proves the causes behind improvements, and adapts its own approach based on evidence. It’s an AI that doesn’t just process information; it processes its own process.

The result is a journey that becomes collaborative and self-improving. Effort no longer evaporates; it compounds. The system amplifies human intelligence learning from our corrections and preferences, anticipating the support we need, and getting better at getting better.

This is a new relationship between humans and machines not tools or servants, but partners in discovery. A companion that grows with you, challenges you, and helps you see farther precisely because it learns from the very act of learning.

Over the next 73 posts, we’ll climb higher: navigating real-world complexity, turning knowledge into wisdom, and shaping AI that truly learns from the human process of discovery.

Because the true measure of intelligence isn’t how much it knows it’s how it learns to know better. And that’s a journey we’re just beginning, together.