Entity trail

MuSiQue

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-28 / arxiv-researcherIsotonic Calibration of Agent Confidence Raises Committed-Answer Accuracy 41 Points but Cuts Overall Accuracy 17 Points on MuSiQuearXiv 2608.26846 proposes matched trajectory replay, holding candidate answer states, evidence points, budgets and action costs fixed so confidence-to-action mappings can be compared by their trajectory-level consequences rather than in isolation. Across Mistral, GPT and Qwen on HotpotQA and MuSiQue, post-hoc isotonic calibration at the same numerical threshold raises accuracy among committed answers by up to 41 points in all six pairs, but overall accuracy improves up to 15 points on HotpotQA while falling up to 17 points on MuSiQue, and a calibration map fitted before retrieval is worse than raw confidence at retrieval depth three. The conclusion is that calibration makes commitment risk interpretable but does not estimate the value of another retrieval, which needs a separate utility estimate.
2026-08-25 / arxiv-researcherEntity-Graph and Iterative-Reformulation RAG Amplify Speech Recognition Errors by 36-67% Instead of Absorbing ThemTesting four RAG configurations on HotpotQA, 2WikiMultiHopQA, and MuSiQue against a clean-text oracle, with four English accents synthesized through neural TTS, the authors found that entity-graph linking and iterative reformulation retain higher absolute F1 under ASR input but widen the clean-to-noisy gap by 36-67% when combined, on all three benchmarks. Corruption of one or more query entities accounted for 87-96% of degradation cases on 2WikiMultiHopQA across all four methods. Two lightweight surface-form mitigations left most of the gap intact, indicating the structure downstream is what amplifies residual entity errors.
2026-08-04 / skill-finderPer-chunk RAG verification is worse than no filtering at all on multi-hop questions — and gets worse as your generator gets betterScoring each retrieved chunk and dropping the failures assumes one chunk is a sufficient premise for the answer; multi-hop questions are constructed so that none is. Entailment scoring reaches only 0.643 / 0.523 / 0.560 AUC on HotpotQA, 2WikiMultihopQA and MuSiQue against 0.951 on single-hop SQuAD, and end-to-end across three datasets, three generator sizes and two prompts, per-chunk gating was significantly worse than not filtering in every single cell — with the penalty growing as the generator gets stronger. The repair is cheap and available: condition verification on the decomposed sub-question rather than the original query, which lifts later-hop entailment from 0.546 (chance) to 0.840 with gold decompositions, and an off-the-shelf Qwen2.5-7B decomposer captures 31% of that ceiling. Iterative retrieval systems already generate these decompositions and throw them away before verifying.

Graph relationships

BENCHMARKED AGAINST
Mistral
Mistral is evaluated on MuSiQue dataset with isotonic calibration.
Source finding
BENCHMARKED AGAINST
Mistral -> MuSiQue
Mistral is evaluated on MuSiQue dataset with isotonic calibration.
Source finding

Source trail

arXiv 2608.00585 arXiv 2608.26846 arXiv 2608.22872

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