Use with AI · Chapter Application

Audit Recursive Compute in a Small Model

Apply Models From First Principles Step 05 to determine whether repeated application of a compact learned model creates useful iterative computation or just repeated cost.

Chapter Application Models From First Principles Recursive and iterative neural models Advanced
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How to use this

  1. Open a repository-aware AI assistant.
  2. Give it access to the repository or files you want reviewed.
  3. Copy the prompt below and run it unchanged first.
  4. Use the evidence it finds to decide what to inspect or change next.
PromptCopy and run against your own project
Review this repository for recursive or iterative use of a compact learned model.

Do not treat repeated inference as reasoning by default. Determine what changes from one iteration to the next and whether additional iterations improve the target behavior under an equal compute budget.

Trace the recursive loop and identify:
- initial state/input;
- model applied at each step;
- state passed forward;
- stopping rule;
- maximum iteration budget;
- supervision applied per step or only at the end;
- gradient path through iterations;
- inference-time iteration policy.

Measure or derive, where repository evidence allows:
- output/state delta per iteration;
- prediction quality by iteration;
- compute cost by iteration;
- convergence or oscillation behavior;
- sensitivity to initialization;
- whether later iterations merely repeat the first result.

Check for:
- fixed iteration counts with no empirical justification;
- recurrence that adds latency but no measurable gain;
- unstable state growth;
- accidental detach preventing intended learning;
- exploding memory from full unrolled graphs;
- stopping decisions made by the same unverified signal being optimized;
- a larger one-pass model outperforming recursion at equal FLOPs/latency;
- a smaller iteration budget matching full performance.

Cite exact file/symbol evidence and separate observations from hypotheses.

Output:
1. Recursive execution trace
2. Per-iteration state/output contract
3. Compute-versus-quality curve or instrumentation plan
4. Convergence/stability risks
5. Equal-budget baselines
6. Smallest ablations
7. Recommended stopping policy
8. Recommendation: Keep / Reduce / Replace recursion

Recursive computation earns its place only when later steps add measurable information or correction rather than merely repeating work.