arXiv study shows multi-hop retrieval failures cluster predictably, enabling abstention via RCS
New findings reveal predictable failure subpopulations and a retrieval-confidence score that allows abstention without extra LLM calls, across multiple benchmarks.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the abstract
- Authors
- Andre Bacellar
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What the paper reports
The study formalizes why multi-hop retrieval failures cluster by query structure and introduces the Retrieval Confidence Score (RCS), a calibrated abstention policy that uses nine query-ANN features without extra LLM calls.
Why it matters
Results show that no single feature dominates across all failure regimes; domain shifts still permit transfer of models, suggesting practical, safer use of AI in retrieval tasks.
This abstract-only preprint reports that multi-hop retrieval failures are not random but concentrate in predictable subgroups defined by query structure and data regime. It introduces RegimeAbstain and the RCS, a logistic function over up to nine features designed to decide when to abstain rather than risk an incorrect answer. Across three benchmarks (MuSiQue, 2WikiMultiHopQA, HoVer) and two architectures (LLM-judge and dense-only), RCS shows robust performance, with reductions in confident-wailure rates while maintaining useful coverage.
The work defines CWAR (Confident-Wrong-Answer Rate) and demonstrates that RCS can achieve best or co-best AUC-AC in all tested regimes, and transfers with minimal loss when applied to a related dataset, highlighting domain-agnostic structure in regime features.
What this does not tell us
Abstract-only scope and preprint status; findings are not validated as full peer-reviewed results. Do not generalize from a single dataset to all populations or from a sample to a universal outcome.
Original sources · 1
- Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention ↗arXiv · 2026-09-18
Check the original paper for its authors, methods, version and access terms.
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