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REVIEW 4 major objections 5 minor 1 cited by

Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A probe trained on the last-token hidden states of a question predicts whether a chain-of-thought generation will be correct, before any reasoning tokens are produced.

desk verdict A useful empirical study with a real result, but the central interpretation that LLMs know CoT outcomes before speaking needs a difficulty-control baseline before it can be trusted. read the letter →

arxiv 2505.24362 v2 pith:ZC2HAQKU submitted 2025-05-30 cs.CL

Anum Afzal , Florian Matthes , Gal Chechik , Yftah Ziser This is my paper
classification cs.CL
keywords chain-of-thoughtprobingclassifiersinternalrepresentationssuccesspredictionearlystoppingmathematicalreasoningSVCCAlargelanguagemodels
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether a large language model 'knows' whether its upcoming chain-of-thought reasoning will end in the right answer before it writes a single reasoning step. It argues yes: a lightweight classifier trained on the model's hidden states for the prompt alone predicts CoT success at 60–76.4% accuracy across three math datasets and two LLMs, beating a BERT text classifier (53.5–69.1%). The paper also finds that feeding later reasoning steps to the probe often does not improve accuracy, and that when extra context does not help, early hidden states closely resemble final ones, suggesting the relevant computation is done early. Truncating CoT generation in a zero-shot setup still beats not using CoT at all on two datasets, though with a clear gap to full reasoning. The authors limit their claims by noting that human annotation covered only one LLM, inference ran at temperature zero, and the method requires white-box access.

What carries the argument

The central object is the probing classifier: a small feedforward network (three hidden layers of 256, 128, and 64 units) trained on the last-token hidden state of a chosen transformer layer to output a sigmoid probability that the eventual chain-of-thought will be correct. It is applied at generation time 0 (prompt only) and at 10% intervals of the generated sequence. The complementary mechanism is SVCCA, singular vector canonical correlation analysis, which measures how much information early hidden representations share with the final step; higher similarity at the layers where the probe performs best is taken as evidence that early representations already carry the final computation. The contrast with BERT, which sees only tokens, isolates the internal-state signal from surface text cues.

What would settle it

Take a set of math questions, sample each one multiple times with nonzero temperature so the same prompt appears in the test set with both a correct and an incorrect CoT generation, and evaluate the pre-generation probe only on those question-prompt hidden states; chance accuracy would mean the probe was reading difficulty or topic, not the reasoning outcome.

Watch

Extended reading notes

Core claim

On the paper's own terms: the internal representations of Llama-3.1-8B and Mistral-7B, taken from the last token of a math question plus the CoT instruction, contain decodable information about whether the answer eventually produced will be right. The probing classifier reaches 60.0–76.4% accuracy before generation, with best layers around the middle of the network (layers 14 and 16 for Llama-3.1-8B), and its advantage over BERT comes mainly from better true-negative detection: it recognizes questions whose CoT will fail, not just ones that will succeed. Over the course of generation, accuracy sometimes fails to improve with more context, and SVCCA similarity scores show that when this happens, early and late representations overlap more, evidence that the model has already formed the relevant calculation. The early-stopping intervention shows that interrupting generation and asking for a short answer yields answers consistent with full CoT only some of the time, yet on AQuA and cn-k12 the truncated runs still outperform answering without CoT.

Load-bearing premise

The load-bearing premise is that the probe's above-chance accuracy on prompt-only hidden states reflects knowledge of the reasoning outcome rather than easier-to-read signals like question topic or difficulty level, and the paper does not test the same question in both a correct and an incorrect generation to rule out that confound.

Editorial extensions

If this is right

  • CoT success can be screened before generation: a system could skip or alter reasoning prompts for questions the probe flags as likely to fail, with 60–76.4% accuracy depending on dataset and model.
  • Reasoning can often be truncated without giving up all of CoT's benefit: on two of the evaluated datasets, a zero-shot halt mid-generation still outperformed answering without CoT.
  • When additional context stops improving success prediction, the measured overlap between early and final hidden states suggests the model has effectively finished the relevant computation; this is a potential signal for adaptive compute allocation.
  • Because the probe captures true negatives better than BERT, part of its value is identifying failure-prone questions, which could guide annotation or fine-tuning efforts.
  • Training methods that shorten CoT chains (e.g., supervised learning or reinforcement learning with brevity rewards) could use the classifier's predictions as a stopping signal, preserving accuracy while cutting cost.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper does not run: check whether the probe generalizes across paraphrases of the same question or across difficulty-matched questions; if it does not, the pre-generation signal is partly lexical rather than genuinely about the reasoning path.
  • If early-step representations really encode the final answer, a natural next step is to read the answer directly from those states (e.g., via a linear decoder over the answer token) rather than running the chain at all; the paper's SVCCA result hints at this but does not attempt it.
  • The same probe method could be applied to non-mathematical reasoning tasks where CoT helps and hurts, to test whether 'knowing before saying' is a general property or specific to arithmetic and logic; the three math datasets used here cannot answer that.
  • The lexical patterns visible in Mistral's AQuA negative examples (e.g., 'the question might be incorrect') suggest a BERT-style baseline could be improved with simple heuristics, which would narrow the gap the paper attributes to internal knowledge.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper asks whether LLM internal representations contain information about whether a zero-shot Chain-of-Thought (CoT) generation will end in a correct answer, before any CoT output token is generated. The authors construct balanced datasets from three math benchmarks (AQuA, cn-k12, Olympiad) with two LLMs (Llama-3.1-8B and Mistral-7B), extract hidden states from the end of the prompt, and train a small MLP probe to predict CoT success. They report 60-76.4% pre-generation accuracy, above a BERT text baseline (53.5-69.1%), and interpret this as evidence that the LLM 'knows' the outcome before saying it. They also track prediction accuracy over generation time, use SVCCA to compare earlier and later representations, and run zero-shot early-stopping experiments in which the model is asked to answer mid-generation.

Significance. If the central claim were established, the result would be practically useful and scientifically interesting: it would enable early termination of CoT, inform interpretability of reasoning, and connect to a growing literature on probing LLM states for their own correctness. The paper has real strengths: code and data are released, the Llama test sets are human-annotated, the hyperparameter configurations are reported, and the SVCCA analysis is a reasonable complement to probing. However, the current evidence does not exclude the simpler explanation that the probe is reading question-difficulty signals rather than information about the CoT reasoning process, so the main interpretation is not yet supported.

major comments (4)
  1. [Section 5.1, Table 3] The central interpretation is not yet supported because the pre-generation probe may be exploiting question-level difficulty rather than information about the CoT process. The dataset construction (Section 4.3) balances by collecting equal numbers of questions for which the LLM's CoT output is correct or incorrect, and correctness is inherently correlated with difficulty; the prompt-only hidden states (last token of the prompt, Section 3) encode difficulty-relevant features such as topic, length, lexical and arithmetic complexity, and the model's prior competence. The BERT baseline's 53.5-69.1% accuracy (Table 3) already demonstrates that surface text features carry substantial signal about these labels, and the paper attributes this to 'shallow linguistic cues' without testing whether the probe's advantage is anything beyond a better difficulty estimator. Since there is no within-question comparison (same question answered correctly vs incorrectly), no difficulty-matched control set, and no baseline trained on difficulty features, the paper's claim that LLM representations 'encode information about CoT success' before generation collapses to the weaker claim that they encode difficulty-correlated features. I request adding such controls; for instance, generate multiple outputs per question (with temperature or prompt variants) and test whether the probe distinguishes correct from incorrect outputs on the same prompt, or include a linear/logistic baseline on handcrafted difficulty features and show that the probe's accuracy significantly exceeds it after matching on difficulty.
  2. [Section 5.1 and Appendix A] The reported accuracies may be optimistically biased because layer and hyperparameter selection is not shown to be performed on held-out data. Table 3 reports 'top-5 layers' and the over-time experiments use the layer that 'performed best on the given dataset in prior evaluations', but the text does not state whether those layers were chosen on the test set or on validation. If test labels were used for selection, the 60-76.4% figures overstate generalization. Please specify the selection protocol and report validation-selected performance, together with confidence intervals or significance tests for the differences against BERT.
  3. [Sections 4.4 and 5.1] The Mistral-7B results rely entirely on GPT-4o mini labels, whose agreement with human annotators is only 90.9-94.8% on the analogous Llama test sets. Label noise in the training and test sets could differentially affect accuracy estimates and cross-LLM comparisons, especially because the same noisy labeler was used to create both labels. At minimum, report a human-verified test subset for Mistral or a noise-adjusted analysis.
  4. [Section 5.2 (Early Stopping in CoT Reasoning) and Table 5] The early-stopping experiments are based on 100 samples per dataset and one zero-shot prompt, and the consistency rates (e.g., 57% at 99% generation for Olympiad) are too low to support the conclusion that 'reasoning can often stop early without loss.' These experiments should be framed as preliminary and accompanied by confidence intervals, and the connection between probe predictability and desirable early stopping needs a tighter argument.
minor comments (5)
  1. [Abstract] The phrase 'before a single token is generated' could mislead readers into thinking the probe operates before any input is processed; it actually operates on the prompt's last-token hidden state, so the claim should be worded as 'before any CoT output token is generated.'
  2. [Table 3] The table formatting is hard to read: layer lists and accuracy values are run together (e.g., '13,14,1660' and '13,14,16, 17 22 69.10'), making it difficult to identify which layer corresponds to which accuracy. Please add clear column separators.
  3. [Section 5.2] The phrase 'linguistic queues' should be 'linguistic cues.'
  4. [Section 5.2 and Figure 3] The SVCCA analysis uses only layer 14; because the probing analysis shows that layer usefulness varies across datasets, please report whether the representational-similarity conclusion is robust across layers.
  5. [Limitation section] The limitation section does not acknowledge the question-difficulty confound discussed in my first major comment; it should be listed explicitly as a threat to the interpretation of pre-generation probe accuracy.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the probe result is an empirical supervised fit on held-out data, not a definitional reduction to its inputs.

full rationale

The central claim is that a probe trained on pre-generation hidden states predicts the correctness of a deterministic CoT completion and outperforms a text-only BERT baseline. This is a standard supervised learning setup: labels are produced by running the LLM with CoT and comparing to reference answers, the probe is trained on a separate 10k split and evaluated on a held-out 1k split, and no fitted parameter is renamed as a prediction. The only author self-citation is the methodological pointer to Zhao et al. (2022) for SVCCA, which is itself an external standard method (Raghu et al. 2017); it does not supply a load-bearing premise, forbid alternatives, or define the target in terms of the input. A skeptical concern that the prompt-only hidden states may encode question difficulty rather than reasoning dynamics is a construct-validity threat, not circularity, because no equation or definition makes the prediction equivalent to the input by construction. The Limitation section does not discuss this confound, but that is a completeness issue for the scientific interpretation, not a circular-derivation issue. Accordingly, the derivation chain is self-contained and the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim depends on the probing methodology, the last-token extraction convention, and label reliability. No new entities are introduced. The probe's hyperparameters and layer choices are fitted per dataset, but they are standard tuning, not the main result.

free parameters (3)
  • probe hyperparameters (batch size, learning rate, optimizer, threshold, weight init) per dataset = varies by dataset; see Appendix A Table 7
    Chosen by empirical validation per dataset and layer; they affect the reported accuracy, so the claimed superiority over BERT depends on these choices.
  • best layer selection for prediction over time = layer 14 / 16 per dataset, selected from test-set performance
    The layer used for over-time predictions is chosen as the best-performing layer in prior evaluations (Section 5.2), which uses the test set and can inflate accuracy.
  • SVCCA analysis layer = Layer 14
    Used for all three Llama-3.1-8B datasets in Figure 3 without a stated selection criterion, introducing a hand-picked choice.
assumptions (4)
  • domain assumption The last token's hidden representation is a faithful summary of the model's state at a given generation step.
    Invoked in Section 3 to reduce the 3D hidden-state tensor across token positions to a single vector per layer; this is the input to every probe.
  • domain assumption A generated answer matching the reference answer defines CoT success.
    Used throughout to create balanced datasets and ground-truth labels; label noise from GPT-4o mini annotation is acknowledged but not corrected in the training data.
  • domain assumption High SVCCA similarity between two step representations is interpreted as encoding the same information relevant to reasoning.
    Used in Section 5.2 to argue early steps resemble final steps when later context is unhelpful; SVCCA measures correlation, not semantic content.
  • domain assumption Probing classifiers can reliably extract task-relevant information from hidden states.
    Standard probing assumption, cited via Belinkov (2022) and Azaria and Mitchell (2023); the paper does not run control probing on randomly initialized embeddings or shuffled labels.

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Cite this review

Pith. "Pith review of Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion." pith.science (2026). https://pith.science/paper/ZC2HAQKU

@misc{pith2026250524362,
  author       = {Pith},
  title        = {Pith review of: Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZC2HAQKU}},
  note         = {Machine review of arXiv:2505.24362}
}
read the original abstract

We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. We discover that a probing classifier, based on LLM representations, performs well \emph{even before a single token is generated}, suggesting that crucial information about the reasoning process is already present in the initial steps representations. In contrast, a strong BERT-based baseline, which relies solely on the generated tokens, performs worse, likely because it depends on shallow linguistic cues rather than deeper reasoning dynamics. Surprisingly, using later reasoning steps does not always improve classification. When additional context is unhelpful, earlier representations resemble later ones more, suggesting LLMs encode key information early. This implies reasoning can often stop early without loss. To test this, we conduct early stopping experiments, showing that truncating CoT reasoning still improves performance over not using CoT at all, though a gap remains compared to full reasoning. However, approaches like supervised learning or reinforcement learning designed to shorten CoT chains could leverage our classifier's guidance to identify when early stopping is effective. Our findings provide insights that may support such methods, helping to optimize CoT's efficiency while preserving its benefits.

Figures

Figures reproduced from arXiv: 2505.24362 by the authors.

Figure 1
Figure 1. Illustration of our approach. The LLM gener [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Accuracy on the test set when the probe is [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Similarity scores using SVCCA between hidden representations of each time step and the full generation, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Similarity Scores using Singular Vector Canonical Correlation Analysis (SVCCA) of the internal [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-Time Progress Prediction in Reasoning Language Models

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Fine-tuned reasoning models and linear probes can emit estimates of normalized trace position, used here as a progress proxy, reaching 0.161 MAE on math traces, though simple length baselines capture most of the signal.

Reference graph

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    Mingyu Zheng, Hao Yang, Wenbin Jiang, Zheng Lin, Yajuan Lyu, Qiaoqiao She, and Weiping Wang. 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.734 Chain-of-thought reasoning in tabular language models . In Findings of the Association for Computational Linguistics: EMNLP 20...

    2023 doi

  33. [41]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  34. [42]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.