The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies
On the map: LLM reasoning → Prompting and chain-of-thought reasoning
Abstract (arXiv)
Corruption studies, the standard tool for evaluating chain-of-thought (CoT) faithfulness, infer which steps are ``computationally important'' from accuracy loss when steps are corrupted. We show that when benchmark chains end with an explicit terminal answer line, as in GSM8K and MATH, these tests largely measure answer placement rather than where intermediate computation is carried out. Using matched GSM8K examples, removing only the final answer statement while preserving all reasoning collapses suffix sensitivity by about 19× for Qwen~2.5-3B (N=300, p=0.022). Conflicting-answer prompts, which contain correct reasoning but a wrong explicit final answer, drive accuracy to zero or near-zero at 7B across five open-weight model families; wrong-answer following is strong at 3B--7B and attenuates sharply at larger scales. Replications on MATH, within-stable comparisons at 7B, and suffix-free chains show the same pattern in different guises: corruption sensitivity tracks the location of explicit answer text, not a fixed computational depth in the reasoning. Generation-time probes indicate that final answers are rarely early-determined during generation (<5% early commitment), yet consumption-time behavior systematically follows explicit answer text. The confound is therefore largely a readout effect when the chain is consumed. We propose a three-prerequisite protocol (question-only control, format characterization, and an all-position sweep) as a practical minimum for future corruption-based faithfulness studies.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 16 references that are in the map. First posted on arXiv 11 May 2026.
Builds on
15 of 16 references.
Closest ancestors · 7
Same direction, newest first
- 2024Let's Think Dot by Dot: Hidden Computation in Transformer Language Models24 Apr
- 2023Measuring Faithfulness in Chain-of-Thought Reasoning17 Jul
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting7 May
- 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango16 Sep
- 2022Large Language Models are Zero-Shot Reasoners24 May
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan
Other references · 8
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
- 2023Let's Verify Step by Stepcited by 3,598
- 2023Towards Understanding Sycophancy in Language Modelscited by 988
- 2022Solving math word problems with process- and outcome-based feedbackcited by 871
- 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
- 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughtcited by 334
- 2023Think before you speak: Training Language Models With Pause Tokenscited by 254
- 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026When Reasoning Traces Become Performative: Step-Level Evidence that Chain-of-Thought Is an Imperfect Oversight Channel
- 2026Lie to Me: How Faithful Is Chain-of-Thought Reasoning in Reasoning Models?
- 2026When Chain-of-Thought Fails, the Solution Hides in the Hidden States
- 2026Two Regimes of Chain-of-Thought Unfaithfulness: Metric-Based Detection Fails Where Models Are Wrong
- 2026Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning
- 2024Chain-of-Thought Unfaithfulness as Disguised Accuracy
- 2024Dissociation of Faithful and Unfaithful Reasoning in LLMs
- 2026A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models