RegimeAbstain：检索置信度评分让RAG在没把握时主动弃答
Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention
RAG错得理直气壮最误事，这篇给检索加个置信分，没把握就弃答，MuSiQue上错误率降了近一半。
论文证明多跳检索失败并非均匀分布，而是集中在结构上可预测的子群中，三个基准MuSiQue、2WikiMultiHopQA、HoVer上的自信错误率（CWAR）在14.5%到62.1%之间。理论部分给出CWAR可约化性：置信失败率能否降低取决于检索特征是否携带成功与否的互信息，LLM-judge架构满足该条件而纯稠密检索明显更弱。第二个结论是特征机制互补性：没有单一ANN分数特征在所有失败机制下最优，主导特征因数据集而异，MuSiQue上是查询长度，HoVer上是hop-1集中度。据此实现的RegimeAbstain用最多9个查询-ANN结构特征做逻辑回归得到RCS置信度分数，全程不需要额外LLM调用。在MuSiQue（LLM-judge）上，RCS在50%覆盖率下把CWAR从39.5%降到20.6%，相对降幅47.8%，ECE为0.035，迁移到2WikiMultiHopQA仅损失0.5pp AUC。
Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention
Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations. We prove two results formalizing this structure. First (CWAR Reducibility): confident-failure reduction is achievable if and only if retrieval features carry mutual information about success, a condition satisfied by LLM-judge pipelines but substantially weaker in dense-only settings, explaining the AUC-AC gap between regimes. Second (Feature Regime Complementarity): no single ANN score feature achieves best predictive performance across all failure regimes; the dominant feature differs between datasets (query length on MuSiQue, hop-1 concentration on HoVer), and a constructive witness pair shows each is necessary in one regime and non-contributory in the other. We instantiate these principles in RegimeAbstain, which computes a Retrieval Confidence Score (RCS), a logistic function of up to nine query-ANN structural features, all available without any additional LLM call, and uses it to implement a calibrated abstention policy. We define the Confident-Wrong-Answer Rate (CWAR) metric and evaluate across three multi-hop benchmarks (MuSiQue, 2WikiMultiHopQA, HoVer) and two retrieval architectures (LLM-judge and dense-only), covering five failure regimes with CWAR from 14.5% to 62.1%. RCS achieves best or co-best AUC-AC in all five conditions against eight confidence baselines. On MuSiQue (LLM-judge), RCS reduces CWAR from 39.5% to 20.6% at 50% coverage (47.8% relative reduction), with ECE=0.035. A model trained on MuSiQue transfers to 2WikiMultiHopQA with only -0.5pp AUC loss, confirming the domain-agnostic structure of regime features.
