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Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores

Paper recorded by Signals 4 on 2026-08-31 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

When a large language model fails a reasoning task, it is often assumed to lack the underlying capability. However, this conflates a genuine absence of reasoning with a late-stage output bottleneck. We observe a consistent readout gap across diverse reasoning benchmarks: hidden-state probes successfully decode correct answers even when native sequence scoring completely collapses due to structural

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#253 most recent of 300 cs.AI papers we have recorded · ↑ newer: Real-Time Video Anomaly Detection Using YOLO Pose Estimation and CLIP- · ↓ older: Measure Before You Manage: Evaluating Agent Working Memory in Coding A
Cite this page: Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores: the #253 most recent of 300 cs.AI papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/wrong-prediction-right-answer-recovering-evidence-from-collapsed-llm-sequence-sc.html
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