Prompt Optimization

Document Intelligence: Turning Documents into Structured Knowledge

Document Intelligence: Turning Documents into Structured Knowledge

AI Research, LLM Systems, RAG, Agent Architectures, Reasoning Engines, Prompt Optimization, Self-Improving Systems, Knowledge Ingestion, Document Scoring

📖 Summary

Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.

In this post we combine insights from Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers and Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.

Programming Intelligence: Using Symbolic Rules to Steer and Evolve AI

Programming Intelligence: Using Symbolic Rules to Steer and Evolve AI

AI Research, Symbolic Reasoning, Self-Tuning Systems, LLM Architecture, Agent Design, Prompt Optimization, AI Evaluation Methods, Co AI Framework

🧪 Summary

“What if AI systems could learn how to improve themselves not just at the level of weights or prompts, but at the level of strategy itself? In this post, we show how to build such a system, powered by symbolic rules and reflection.

The paper Symbolic Agents: Symbolic Learning Enables Self-Evolving Agents introduces a framework where symbolic rules guide, evaluate, and evolve agent behavior.

Adaptive Reasoning with ARM: Teaching AI the Right Way to Think

Adaptive Reasoning with ARM: Teaching AI the Right Way to Think

AI Research, LLM Systems, Agent Architectures, Reasoning Engines, Prompt Optimization

Summary

Chain-of-thought is powerful, but which chain? Short explanations work for easy tasks, long reflections help on hard ones, and code sometimes beats them both. What if your model could adaptively pick the best strategy, per task, and improve as it learns?

The Adaptive Reasoning Model (ARM) is a framework for teaching language models how to choose the right reasoning format direct answers, chain-of-thoughts, or code depending on the task. It works by evaluating responses, scoring them based on rarity, conciseness, and difficulty alignment, and then updating model behavior over time.