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convergence-check

Evaluate whether a hypothesis newly entered the current top-k set and update the convergence counter deterministically.

personAuthor: TashanworldhubOpenAPI
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convergence-check

Goal:

  • Evaluate whether a hypothesis newly entered the current top-k set and update the convergence counter deterministically.

Inputs:

  • hypothesis_id
  • previous_top_k_ids
  • current_top_k_ids
  • current convergence count
  • caller-owned state/EVOLUTION_STATE.json

Outputs:

  • ConvergenceCheckResult
  • updated convergence count
  • when consumed by the evolution loop, updated state/EVOLUTION_STATE.json

Context Loading:

  • Open skills/shared-references/schema-index.md.
  • Read packages/agent_contracts/pipeline_control.py and confirm the exact EvolutionStateContract shape before writing state/EVOLUTION_STATE.json.
  • Treat the top-k sets as caller-supplied frontier inputs. This skill only evaluates the rule and updates the counter.

Execution Contract:

  • This skill is deterministic and must not call an LLM.
  • Use from tools import evaluate_convergence as the stable invocation surface.
  • The exported helper is implemented in packages/agent_mechanics/convergence_check.py.
  • The helper signature is evaluate_convergence(hypothesis_id, previous_top_k_ids, current_top_k_ids, current_convergence_count) -> ConvergenceCheckResult.

Execution Steps:

  1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/pipeline_control.py before writing state/EVOLUTION_STATE.json.
  2. Read the candidate hypothesis_id, the previous and current top-k sets, and the current convergence count.
  3. Call tools.evaluate_convergence(hypothesis_id, previous_top_k_ids, current_top_k_ids, current_convergence_count).
  4. Return the ConvergenceCheckResult to the caller.
  5. When used by the evolution loop, persist the returned entered_top_k and convergenceCount values into state/EVOLUTION_STATE.json.
  6. Validate any updated state/EVOLUTION_STATE.json artifact before declaring completion.

Artifact Rules:

  • The convergence rule is fixed: entering the top-k frontier resets the counter to zero; otherwise the counter increments by one.
  • Do not fold additional stopping logic into this skill. Stop decisions belong to evolution state management and completion verification.

Completion Rule:

  • This skill is complete only when the deterministic result has been produced and any caller-owned state/EVOLUTION_STATE.json update matches that result exactly.