{slug}: {one-line title}

Metadata

Date: YYYY-MM-DD
Author: {agent or human name}
Status: draft | validated | in-progress | done | rejected

Hypothesis

Question: What happens if we {specific change}?

Prediction: We expect {metric} to change by {amount} because {mechanism}.

Falsification: If {metric} does NOT change by at least {threshold}, the hypothesis is wrong.

Approach

Brief description of the implementation. What files change? What's the core modification?

# pseudocode or key snippet showing the idea

Why this is novel

How does this differ from what's already in RESEARCH_JOURNAL.md? What paper/idea is it based on?

Expected implementation effort

Small (< 1 hour) / Medium (1-4 hours) / Large (> 4 hours)

Pueue label (for queuing)

Q: does {change} improve {metric}? H: expect {delta}

Subagent Critique

Rating: 1-5 (1=bad, 5=excellent)

Strengths:

Failure modes:

Verdict: proceed / revise / reject

Result (fill in after experiment)

Commit: SHORT_SHA Report: 9_reports/YYYY-MM-DD_{slug}.md Observed: {metric} = {value} (expected {expected}) Verdict: confirmed / partially confirmed / refuted / inconclusive

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