Result To Claim

---
name: result-to-claim
description: "Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing before paper planning."
argument-hint: [experiment-description-or-wandb-run]
allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit
---

# Result-to-Claim Gate

Experiments produce numbers; this gate decides what those numbers *mean*. Collect results from available sources, 获取独立评审模型判定（经评审桥），再自动路由 based on the verdict.

## Context: $ARGUMENTS

## When to Use

- After a set of experiments completes (main results, not just sanity checks)
- Before committing to claims in a paper or review response
- When results are ambiguous and you need an objective second opinion

## Workflow

### Step 1: Collect Results

Gather experiment data from whatever sources are available in the project:

1. **W&B** (preferred): `wandb.Api().run("<entity>/<project>/<run_id>").history()` — metrics, training curves, comparisons
2. **EXPERIMENT_LOG.md**: full results table with baselines and verdicts
3. **EXPERIMENT_TRACKER.md**: check which experiments are DONE vs still running
4. **Log files**: `ssh server "tail -100 /path/to/training.log"` if no other source
5. **docs/research_contract.md**: intended claims and experiment design

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: 独立评审模型判定

将收集到的结果送独立评审模型（经评审桥）做客观评估:

```
review_bridge.invoke:  # 经评审桥调用独立评审模型（工具名随宿主；缺席时降级为当前 Agent 自审）
  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

从评审桥返回中提取结构化字段:

```markdown
- 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 AGENTS.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

- **独立评审模型是裁判，不是执行 Agent。** 当前 Agent 收集证据并路由，评审桥负责评估。 This prevents post-hoc rationalization.
- Do not inflate claims beyond what the data supports. 若评审模型判 "partial"，不得上调 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.
- 若评审桥不可用（调用失败），由当前 Agent 自行判定并标注 `[pending independent review]` — do not block the pipeline.
- Always record the verdict and reasoning in findings.md, regardless of outcome.