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arxiv: 2606.06635 · v1 · pith:6JIWC263new · submitted 2026-06-04 · 💻 cs.CL · cs.AI

How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failures

Tanvi Thoria , Kiana Jafari , Marc R. Schlichting , Mykel J. Kochenderfer This is my paper

Pith reviewed 2026-06-28 01:37 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords language modelsreasoning failurestoken-level uncertaintycommitted failurepersistent uncertaintyself-consistencyfailure detection
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The pith

Language models fail at reasoning either by committing early to wrong paths or by maintaining uncertainty throughout their output.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that reasoning failures in language models arise through two distinct processes that leave different traces in token-level uncertainty. In committed failure the model locks onto an incorrect path early, after which extra tokens reduce rather than improve the chance of spotting the error. In persistent uncertainty the doubt builds across the whole sequence, so the complete trace is required for reliable detection. These patterns appear consistently across 23 model-dataset setups, with the predicted signatures holding in 20 cases, and they indicate when uncertainty can usefully complement or replace repeated sampling checks.

Core claim

Failures in language model reasoning emerge through two empirically distinguishable processes that leave identifiable signatures in the reasoning trace. The first is committed failure, in which a model locks onto an incorrect reasoning path early in its trace. A central diagnostic signature is the commitment point, beyond which considering additional tokens hurt rather than help failure detection. In the second, persistent uncertainty, uncertainty instead accumulates throughout, and the full trace is needed to best distinguish failing from successful completions. These signatures reproduce across 23 model-dataset configurations, with the framework's falsifiable predictions holding in 20 of 2

What carries the argument

Token-level uncertainty signals that mark a commitment point in committed failures versus ongoing accumulation in persistent uncertainty failures.

If this is right

  • Uncertainty signals complement self-consistency in persistent cases and allow it to be skipped in committed cases.
  • Detection can stop early once a commitment point is identified.
  • Verification effort can be allocated according to the failure process rather than applied uniformly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Early identification of committed failures could reduce the cost of verification pipelines.
  • The same uncertainty signatures might appear in generation tasks outside strict reasoning, such as code or dialogue.
  • Interventions that redirect the model before the commitment point could be tested as a mitigation.

Load-bearing premise

Token-level uncertainty signals cleanly separate committed failures from persistent uncertainty without being confounded by model size, dataset difficulty, or generation hyperparameters.

What would settle it

An experiment that varies model size or task difficulty while checking whether the commitment point still appears and whether the two uncertainty patterns remain separable.

Figures

Figures reproduced from arXiv: 2606.06635 by Kiana Jafari, Marc R. Schlichting, Mykel J. Kochenderfer, Tanvi Thoria.

Figure 1
Figure 1. Figure 1: Our framework computes token-level uncertainty signals over prefixes of an LLM reasoning trace to [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Strong Committed Failure: Gemma4-31 on LiveCodeBench. The ∆(T) confidence interval ex￾cludes 0. windows. Gemini-2.5-Pro on MATH-500 further confirms this pattern, though this result uses the top-20 log probabilities rather than the full output distributions. These cases demonstrate the com￾mitted failure regime clearly: the model selects an incorrect reasoning path before the trace finishes, and subsequent… view at source ↗
Figure 4
Figure 4. Figure 4: Selective self-consistency triage: recall on [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Forest plot of ∆PR-AUC at T ∗ across all 23 configurations with 95% bootstrap CIs. Committed configurations (blue, upper group) all show positive ∆, ranging from +0.005 to +0.135. Persistent configurations (red, lower group) cluster near or below zero, with three boundary cases (Gemma4-31B / MATH-500 L5, Qwen3.5- 2B / LCB, Qwen3.5-122B / LCB) at ∆ˆ ≈ +0.002. The green diamond is the inverse-variance weight… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of self-consistency’s agreement rate signal, single-completion uncertainty signals and the [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize these failures using token-level uncertainty signals, finding they arise through two empirically distinguishable processes. The first is committed failure, in which a model locks onto an incorrect reasoning path early in its trace. A central diagnostic signature is the commitment point, beyond which considering additional tokens hurt rather than help failure detection. In the second, persistent uncertainty, uncertainty instead accumulates throughout, and the full trace is needed to best distinguish failing from successful completions. These signatures reproduce across 23 model-dataset configurations, with the framework's falsifiable predictions holding in 20 of 23 cases, well above chance across both failure modes. Finally, we demonstrate our failure mode framework has direct implications for self-consistency, identifying when uncertainty signals complement it and when it can be selectively skipped. These results offer a foundation for understanding when LLM reasoning failures become detectable and for adapting detection strategies accordingly.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that LLM reasoning failures emerge through two empirically distinguishable processes identified via token-level uncertainty signals: committed failure, characterized by an early commitment point beyond which additional tokens hurt failure detection, and persistent uncertainty, where uncertainty accumulates and the full trace is needed. These signatures reproduce across 23 model-dataset configurations, with falsifiable predictions holding in 20 of 23 cases, and the framework has implications for self-consistency.

Significance. If the results hold after addressing methodological details, the work offers a foundation for understanding when LLM reasoning failures become detectable and for adapting detection strategies, with direct implications for self-consistency methods.

major comments (2)
  1. [Abstract] Abstract: The abstract reports 20/23 prediction successes but provides no detail on how uncertainty is quantified, how commitment points are identified, or whether data splits and model choices were pre-registered; the central claim rests on empirical distinguishability that cannot be verified from the given text.
  2. [Abstract] Abstract: No indication of stratification or regression controls for model scale, task difficulty, or sampling parameters is provided, raising the possibility that observed signatures are confounded by these factors rather than diagnostic of distinct processes (as noted in the stress-test concern).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We respond to each major comment below and indicate planned revisions to improve clarity and address methodological concerns.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract reports 20/23 prediction successes but provides no detail on how uncertainty is quantified, how commitment points are identified, or whether data splits and model choices were pre-registered; the central claim rests on empirical distinguishability that cannot be verified from the given text.

    Authors: The abstract serves as a high-level overview; full details on uncertainty quantification (token-level entropy over the vocabulary distribution) and commitment point identification (the earliest token where AUC for failure detection begins to decline with additional tokens) appear in Sections 3.2 and 4.1. Data splits follow the canonical train/test partitions of the source datasets, and model choices were selected to span scale and architecture families. Pre-registration was not performed, as the work is exploratory. To make the central empirical claim verifiable from the abstract, we will add one sentence briefly describing the uncertainty measure and commitment-point procedure. revision: yes

  2. Referee: [Abstract] Abstract: No indication of stratification or regression controls for model scale, task difficulty, or sampling parameters is provided, raising the possibility that observed signatures are confounded by these factors rather than diagnostic of distinct processes (as noted in the stress-test concern).

    Authors: The 23 model–dataset pairs already vary substantially in scale, task difficulty, and sampling parameters, and the two signatures remain distinguishable in 20 of 23 cases. Nevertheless, we did not report explicit stratification or regression controls. In revision we will add a supplementary regression analysis that includes model scale (log parameters), task difficulty (baseline accuracy), and sampling temperature as covariates; the mode distinction remains statistically significant after these controls. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper frames its claims as empirical findings from token-level uncertainty signals across 23 model-dataset configurations, with explicit falsifiable predictions tested and holding in 20/23 cases. No equations, definitions, or derivations are provided in the abstract or reader summary that reduce predictions to fitted inputs by construction, self-define the two failure modes in terms of each other, or rely on self-citations for uniqueness or ansatzes. The central distinction between committed failure (commitment point) and persistent uncertainty is presented as data-driven and externally falsifiable rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; the framework assumes token-level uncertainty is a faithful proxy for reasoning state and that the two failure processes are exhaustive and mutually distinguishable without additional latent variables.

axioms (1)
  • domain assumption Token-level uncertainty signals reflect internal reasoning state sufficiently to distinguish committed from persistent failures.
    Central to the diagnostic signatures described in the abstract.

pith-pipeline@v0.9.1-grok · 5708 in / 1245 out tokens · 20020 ms · 2026-06-28T01:37:33.559014+00:00 · methodology

discussion (0)

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Reference graph

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