| name | research-decision-gate |
| description | Use before research or engineering-research decisions: deciding whether to continue a direction, run an experiment, change route, interpret results, write a paper claim, or invest more work when there is risk of local optimization, self-drawn targets, weak evaluation, LLM self-evaluation loops, system-specific bug fixes disguised as innovation, or poor ROI. Produces a concise decision gate covering generality, innovation boundary, evaluation credibility, ROI and stop conditions, paper narrative fit, next action, and what not to do. |
Research Decision Gate
Use this as a pre-decision gate, not as a brainstorming mode. Be skeptical, concise, and willing to stop work.
Procedure
- Start with one verdict:
GO, SMALL BET, REFRAME, or STOP.
- Test generality: decide whether the issue remains after removing project-specific bugs, prompt artifacts, data quirks, implementation defects, and accidental constraints.
- Set the innovation boundary: name the strongest defensible claim and the claims that would be overreach.
- Audit evaluation: check gold source, baselines, negative examples, leakage, judge independence, metric gaming, and whether the result would convince someone outside this project.
- Check ROI: every proposed experiment must change a real decision; define the cheapest decisive test and the stop condition before running it.
- Check narrative fit: decide whether the result supports the paper or product thesis, or only improves a local subsystem.
- Give one next action and one explicit non-action.
Output Format
一句话判断: GO / SMALL BET / REFRAME / STOP + one sentence.
通用性判断: Is this a problem for a class of RAG or AI systems, or mainly our implementation bug?
创新边界: What claim is defensible? What claim is forbidden?
评测风险: What could make the evaluation circular, unfair, leaky, or self-drawn?
ROI/止损: What is the cheapest decisive test? What result stops this line?
论文叙事位置: Which paper claim, section, or ablation would this support?
下一步动作: One concrete action to do now.
不该做什么: One tempting action to avoid.
Red Flags
- The claim disappears after fixing one project-specific bug.
- The baseline is weaker because it lacks equivalent evidence, prompts, tools, retrieval budget, or tuning effort.
- Negative examples are produced by the same method being evaluated without independent audit.
- LLM labels are used as both gold and judge.
- More experiments only improve a local metric without changing the paper claim.
- The work can only be explained as "our system had this issue", not "this class of systems has this failure mode".
Decision Bias
Prefer REFRAME when the phenomenon is real but the current claim is too broad.
Prefer SMALL BET when evaluation is weak but upside is high and cost is low.
Prefer STOP when the next experiment cannot change the decision.
