Result To Claim

Assemble the key information:

• What experiments were run (method, dataset, config)
• Main metrics and baseline comparisons (deltas)
• The intended claim these experiments were designed to test
• Any known confounds or caveats

Step 2: Codex Judgment

Send the collected results to Codex for objective evaluation:

mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    RESULT-TO-CLAIM EVALUATION

    I need you to judge whether experimental results support the intended claim.

    Intended claim: [the claim these experiments test]

    Experiments run:
    [list experiments with method, dataset, metrics]

    Results:
    [paste key numbers, comparison deltas, significance]

    Baselines:
    [baseline numbers and sources — reproduced or from paper]

    Known caveats:
    [any confounding factors, limited datasets, missing comparisons]

    Please evaluate:
    1. claim_supported: yes | partial | no
    2. what_results_support: what the data actually shows
    3. what_results_dont_support: where the data falls short of the claim
    4. missing_evidence: specific evidence gaps
    5. suggested_claim_revision: if the claim should be strengthened, weakened, or reframed
    6. next_experiments_needed: specific experiments to fill gaps (if any)
    7. confidence: high | medium | low

    Be honest. Do not inflate claims beyond what the data supports.
    A single positive result on one dataset does not support a general claim.

Step 3: Parse and Normalize

Extract structured fields from Codex response:

- claim_supported: yes | partial | no
- what_results_support: "..."
- what_results_dont_support: "..."
- missing_evidence: "..."
- suggested_claim_revision: "..."
- next_experiments_needed: "..."
- confidence: high | medium | low

Step 4: Route Based on Verdict

no — Claim not supported

1. Record postmortem in findings.md (Research Findings section):
   • What was tested, what failed, hypotheses for why
   • Constraints for future attempts (what NOT to try again)
2. Update CLAUDE.md Pipeline Status
3. Decide whether to pivot to next idea from IDEA_CANDIDATES.md or try an alternative approach

partial — Claim partially supported

1. Update the working claim to reflect what IS supported
2. Record the gap in findings.md
3. Design and run supplementary experiments to fill evidence gaps
4. Re-run result-to-claim after supplementary experiments complete
5. Multiple rounds of partial on the same claim → record analysis in findings.md, consider whether to narrow the claim scope or switch ideas

yes — Claim supported

1. Record confirmed claim in project notes
2. If ablation studies are incomplete → trigger /ablation-planner
3. If all evidence is in → ready for paper writing

Rules

• Codex is the judge, not CC. CC collects evidence and routes; Codex evaluates. This prevents post-hoc rationalization.
• Do not inflate claims beyond what the data supports. If Codex says "partial", do not round up to "yes".
• A single positive result on one dataset does not support a general claim. Be honest about scope.
• If confidence is low, treat the judgment as inconclusive and add experiments rather than committing to a claim.
• If Codex MCP is unavailable (call fails), CC makes its own judgment and marks it [pending Codex review] — do not block the pipeline.
• Always record the verdict and reasoning in findings.md, regardless of outcome.