Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models

• URL: http://arxiv.org/abs/2606.13603v1
• Date: Thu, 11 Jun 2026 17:21:16 GMT
• Title: Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
• Abstract summary: Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models.<n>We study how answers form across the reasoning traces of several model families.
• Score: 16.372524974789492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models, yet the causal influence of individual steps on the final answer poorly understood. We estimate each step's causal importance via early exit and use this measure to study how answers form across the reasoning traces of several model families. Across diverse tasks, we find that reasoning typically crosses a \emph{commitment boundary} -- a sharp transition from transient intermediate guesses to a stable, high-confidence answer. This transition often happens in a single step, well before the model's reasoning block ends, and is followed by \emph{epiphenomenal} CoT steps that leave the final answer probability unaltered. Using attention probes, we show that answer-formation stages can be linearly decoded from intermediate reasoning steps with high accuracy and generalize robustly to unseen reasoning tasks. We exploit this signal to early-exit reasoning blocks at the commitment boundary, reducing the length of CoTs up to 55\% on average with negligible impact on model performance.

Related papers

• Large Language Models Decide Early and Explain Later [47.20810161393936]
  We study the evolution of predicted answers over reasoning steps using forced answer completion.<n>We show that simples, including probe-based stopping, can reduce reasoning token usage by 500 tokens per query.
  arXiv  Detail & Related papers  (2026-04-24T06:26:24Z)
• Dissecting Failure Dynamics in Large Language Model Reasoning [9.525092839502433]
  Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation.<n>We find that errors are not uniformly distributed but often originate from a small number of early transition points.<n>We introduce GUARD, a targeted inference-time framework that probes and redirects critical transitions.
  arXiv  Detail & Related papers  (2026-04-16T01:49:12Z)
• Early Stopping for Large Reasoning Models via Confidence Dynamics [55.67938134245981]
  Large reasoning models rely on long chain-of-thought generation to solve complex problems.<n>Key challenge is determining when the model should stop reasoning and produce the final answer.<n>We propose CoDE-Stop, an early stopping method that leverages the dynamics of intermediate answer confidence to decide when to terminate reasoning.
  arXiv  Detail & Related papers  (2026-04-06T17:59:45Z)
• Mitigating Overthinking in Large Reasoning Language Models via Reasoning Path Deviation Monitoring [35.58177960646011]
  We propose an early-exit method to mitigate overthinking in LRLMs.<n>Our method delivers the largest performance improvement over vanilla CoT compared to existing early-exit methods.
  arXiv  Detail & Related papers  (2026-03-15T07:00:47Z)
• Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure [58.89643769707751]
  We study latent chain-of-thought as a manipulable causal process in representation space.<n>We find that latent-step budgets behave less like homogeneous extra depth and more like staged functionality with non-local routing.<n>These results motivate mode-conditional and stability-aware analyses as more reliable tools for interpreting and improving latent reasoning systems.
  arXiv  Detail & Related papers  (2026-02-09T15:25:12Z)
• Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization [9.193078163792427]
  Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems.<n>Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states.<n>We introduce PLaT, a framework that reformulates latent reasoning as planning by fundamentally decouple reasoning from verbalization.
  arXiv  Detail & Related papers  (2026-01-29T07:38:18Z)
• Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks [54.31998314008198]
  Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks.<n>We attribute this limitation to textbfreasoning overconfidence: a tendency to express undue certainty in an incomplete solution set.<n>We propose the textbfcognitive-rigidity hypothesis, which posits that overconfidence arises when the reasoning process prematurely converges on a narrow set of thought paths.
  arXiv  Detail & Related papers  (2025-12-01T14:35:06Z)
• Temporal Predictors of Outcome in Reasoning Language Models [0.0]
  Chain-of-thought (CoT) paradigm uses the elicitation of step-by-step rationales as a proxy for reasoning.<n>We show that, for harder questions, a drop in predictive accuracy highlights a selection artifact.<n>Overall, our results imply that for reasoning models, internal self-assessment of success tends to emerge after only a few tokens.
  arXiv  Detail & Related papers  (2025-11-03T08:57:18Z)
• MixReasoning: Switching Modes to Think [79.70845484191543]
  Reasoning models enhance performance by tackling problems in a step-by-step manner.<n>Applying extended reasoning to every step introduces substantial redundancy.<n>We propose MixReasoning, a framework that dynamically adjusts the depth of reasoning within a single response.
  arXiv  Detail & Related papers  (2025-10-07T15:46:34Z)
• Stop Spinning Wheels: Mitigating LLM Overthinking via Mining Patterns for Early Reasoning Exit [114.83867400179354]
  Overthinking can degrade overall performance of large language models.<n>We categorize reasoning into three stages: insufficient exploration stage, compensatory reasoning stage, and reasoning convergence stage.<n>We develop a lightweight thresholding strategy based on rules to improve reasoning accuracy.
  arXiv  Detail & Related papers  (2025-08-25T03:17:17Z)
• Fractured Chain-of-Thought Reasoning [61.647243580650446]
  We introduce Fractured Sampling, a unified inference-time strategy that interpolates between full CoT and solution-only sampling.<n>We show that Fractured Sampling consistently achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling gains in Pass@k versus token budget.
  arXiv  Detail & Related papers  (2025-05-19T11:30:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.