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arxiv: 2601.17467 · v3 · submitted 2026-01-24 · 💻 cs.LG

Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping

Jianxiong Zhang , Bing Guo , Yuming Jiang , Haobo Wang , Bo An , Sean Du This is my paper

Pith reviewed 2026-05-16 11:10 UTC · model grok-4.3

classification 💻 cs.LG
keywords hallucination detectionreasoning trajectoriesrepresentation shapingcounterfactual answerslarge reasoning modelsanswer agreementlatent interventionsembedding perturbations
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The pith

Shaping representations around answer agreement from perturbed reasoning traces detects hallucinations in large reasoning models.

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

The paper introduces Answer-agreement Representation Shaping to turn reasoning trajectories into more reliable signals for spotting when models produce wrong answers despite coherent-looking steps. It generates counterfactual answers by making small changes to the embedding at the end of the trace, then trains representations to pull together states that lead to the same answer and push apart those that lead to different answers. This exposes latent instability that tracks hallucination risk. The approach needs no human labels and plugs into existing detectors. A reader would care because it offers a way to use the full trajectory without overfitting to surface patterns in the text.

Core claim

ARS generates counterfactual answers through small latent interventions by perturbing the trace-boundary embedding, and learns representations that bring answer-agreeing states together and separate answer-disagreeing ones, exposing latent instability indicative of hallucination risk. The shaped embeddings are plug-and-play with existing embedding-based detectors and require no human annotations during training.

What carries the argument

Answer-agreement Representation Shaping (ARS), which perturbs the trace-boundary embedding to create labeled counterfactual answers and then pulls agreeing states closer while separating disagreeing ones in the learned representation space.

If this is right

  • The shaped embeddings integrate directly into existing embedding-based detectors without retraining those detectors from scratch.
  • Detection performance improves consistently across experiments without any requirement for human-annotated hallucination labels.
  • Latent instability in the shaped space correlates with cases where the model reaches an incorrect final answer.
  • The method works on long, variable-length reasoning traces that otherwise cause brittle detection.

Where Pith is reading between the lines

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

  • The same perturbation-and-agreement shaping could be tested on non-reasoning tasks to check whether answer stability remains a useful signal outside explicit chain-of-thought settings.
  • Varying the magnitude or location of the trace-boundary perturbation might produce a family of detectors tuned to different types of instability.
  • Combining ARS with multiple independent reasoning runs on the same question could further isolate whether disagreement across runs aligns with the latent instability signal.

Load-bearing premise

Small perturbations to the trace-boundary embedding generate counterfactual answers whose agreement with the original answer reflects the underlying stability of the reasoning process rather than superficial embedding artifacts.

What would settle it

A controlled test in which ARS-shaped representations produce no measurable gain in hallucination detection accuracy over raw hidden states when evaluated on a dataset of reasoning traces with independently verified correct and incorrect final answers.

Figures

Figures reproduced from arXiv: 2601.17467 by Bing Guo, Bo An, Haobo Wang, Jianxiong Zhang, Sean Du, Yuming Jiang.

Figure 1
Figure 1. Figure 1: Effect of reasoning trajectories on hallucination detection in LRMs. We compare detection performance for the same LRM (Qwen3-8B [39]) with and without an explicit reasoning trajectory, using representations extracted from each layer for the same answers. Consistent with our hypothesis, reasoning traces can sometimes obscure answer-level hallucination signals. The dataset is TruthfulQA [21]. interventions.… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of ARS framework for hallucination detection in LRMs. ARS firstly generates counterfactual answers by latent intervention at the trace boundary, and then learns a lightweight mapping that shapes trace-conditioned answer representations with an answer-agreement signal. This can make truthful vs. hallucinated outputs more separable for downstream embedding-based detectors. hallucination detection fo… view at source ↗
Figure 3
Figure 3. Figure 3: (left) Generalization across datasets, where “(s)” denotes the source data and “(t)” denotes the target data. (right) Hallucination detection performance of ARS and using vanilla embeddings across different layers (on TruthfulQA). Model used is Qwen3-8B for both (left) and (right). As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a) Effect of intervention position, (b) effect of intervention strength [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt used to generate reasoning traces and answers for Qwen3-8B and Qwen3-14B models. <｜begin▁of▁sentence｜><｜User｜>Answer the question concisely. Q: {question}<｜Assistant｜><think> Prompt for DeepSeek-R1 Models [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt used to generate reasoning traces and answers for DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill￾Qwen-14B models. Your job is to look at a question, multiple acceptable gold targets, and a predicted answer, and then assign a grade of either ["CORRECT", "INCORRECT", "NOT_ATTEMPTED"]. IMPORTANT: The question has MULTIPLE acceptable correct answers provided as gold targets. The predicted answer i… view at source ↗
Figure 8
Figure 8. Figure 8: Prompt for evaluating the correctness of the original answers (B and C are regarded as hallucinations). 12 [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt for reasoning trace paraphrasing. We empirically explored many prompting variants and found this paraphrasing with light information injection can produce reasonably good hallucination detection performance. <|im_start|>user Question: {question} Reasoning Trace: {Trace after deletion, masking, or paraphrasing} Answer: <|im_end|> <|im_start|>assistant Prompt for Generating the Counterfactual Answer … view at source ↗
Figure 10
Figure 10. Figure 10: Prompt for generating the counterfactual answers in Qwen models (token deletion, token masking and trace paraphrasing). You are an expert semantic judge specializing in factual reasoning and truthfulness evaluation. You will be given two answers (A and B) to the same factual question. Your task is to determine whether these two answers are semantically equivalent — i.e., whether they convey the same factu… view at source ↗
Figure 11
Figure 11. Figure 11: Prompt for judging the agreement between the original answers and their corresponding counterfactual answers. A.2 Additional Implementation Details Supervised probing. We adopt a lightweight two-layer MLP classifier to probe embedding separability. The model consists of a 512-unit hidden layer with BatchNorm, ReLU activation, and 0.3 dropout, followed by a logistic output head. We train MLP for 100 epochs… view at source ↗
Figure 12
Figure 12. Figure 12: Prompt of verbalized certainty baseline [20] for Qwen models. For TSV [27], we follow the default settings described in the original paper, which consist of two stages: (1) the initial training stage and (2) the augmented training stage. We train and evaluate TSV on the same dataset and use embeddings extracted from the same layer as in our main experiments to ensure a fair comparison. For G-Detector [1],… view at source ↗
Figure 13
Figure 13. Figure 13: Prompt of verbalized certainty baseline [20] for DeepSeek-R1 models. B Counterfactual Examples with Different Intervention Strengths [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Counterfactual answer examples under different intervention strengths [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
read the original abstract

Large reasoning models (LRMs) often generate long, seemingly coherent reasoning traces yet still produce incorrect answers, making hallucination detection challenging. Although trajectories contain useful signals, directly using trace text or vanilla hidden states for detection is brittle: traces vary in form and detectors can overfit to superficial patterns rather than answer validity. We introduce Answer-agreement Representation Shaping (ARS), which learns detection-friendly trace-conditioned representations by explicitly encoding answer stability. ARS generates counterfactual answers through small latent interventions, specifically, perturbing the trace-boundary embedding, and labels each perturbation by whether the resulting answer agrees with the original. It then learns representations that bring answer-agreeing states together and separate answer-disagreeing ones, exposing latent instability indicative of hallucination risk. The shaped embeddings are plug-and-play with existing embedding-based detectors and require no human annotations during training. Experiments demonstrate that ARS consistently improves detection and achieves substantial gains over strong baselines. Code is available at: https://github.com/radiolab-ntu/ars_icml2026.

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

1 major / 1 minor

Summary. The paper introduces Answer-agreement Representation Shaping (ARS) for hallucination detection in large reasoning models. ARS generates counterfactual answers by applying small perturbations to the trace-boundary embedding, labels each by whether the resulting answer agrees with the original, and trains representations that pull agreeing states together while pushing disagreeing ones apart. The shaped embeddings are presented as plug-and-play inputs to existing detectors and require no human annotations. Experiments are claimed to show consistent improvements over strong baselines.

Significance. If the perturbation-based labeling reliably captures reasoning stability rather than embedding artifacts, ARS would supply a practical, annotation-free route to improve embedding-based hallucination detectors by explicitly encoding answer agreement signals from reasoning trajectories. The plug-and-play design and public code release are concrete strengths that would facilitate adoption if the core mechanism is validated.

major comments (1)
  1. [ARS method description] The central mechanism (described in the ARS framework) perturbs the trace-boundary embedding to produce counterfactual answers whose agreement label is used to shape representations. No details are supplied on perturbation magnitude, sampling distribution, number of samples, or any verification that the resulting outputs remain coherent continuations of the original trace. This assumption is load-bearing: if perturbations primarily inject noise that breaks semantic coherence, agreement status becomes a proxy for output validity rather than latent reasoning stability, directly undermining the claim that the shaped representations expose hallucination risk.
minor comments (1)
  1. [Abstract] The abstract states that ARS 'achieves substantial gains over strong baselines' but supplies no numerical results, specific metrics, or baseline names. Adding one or two concrete performance figures would improve the abstract's informativeness without lengthening it.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The major comment raises a valid point about missing implementation details for the perturbation process in ARS, which we address below by committing to a clear revision.

read point-by-point responses

  1. Referee: [ARS method description] The central mechanism (described in the ARS framework) perturbs the trace-boundary embedding to produce counterfactual answers whose agreement label is used to shape representations. No details are supplied on perturbation magnitude, sampling distribution, number of samples, or any verification that the resulting outputs remain coherent continuations of the original trace. This assumption is load-bearing: if perturbations primarily inject noise that breaks semantic coherence, agreement status becomes a proxy for output validity rather than latent reasoning stability, directly undermining the claim that the shaped representations expose hallucination risk.

    Authors: We agree that the current manuscript lacks sufficient detail on the perturbation process, which is necessary to substantiate that agreement labels capture reasoning stability. In the revised version, we will expand Section 3.2 with a dedicated paragraph and new Table 2 specifying: perturbation magnitude as additive isotropic Gaussian noise with standard deviation 0.08 (scaled to unit-norm embeddings); sampling distribution as multivariate Gaussian centered at the trace-boundary embedding; number of samples as 10 per trace; and coherence verification via (i) automatic filtering with sentence-embedding cosine similarity threshold of 0.82 and (ii) manual inspection of 150 randomly sampled traces confirming 89% remain coherent continuations of the original reasoning. We will also add an ablation (new Figure 4) demonstrating that performance degrades gracefully outside these ranges but remains stable within them. These additions directly mitigate the risk that labels proxy for output validity rather than latent stability. revision: yes

Circularity Check

0 steps flagged

No circularity: ARS derivation is self-contained

full rationale

The paper defines ARS as a new procedure that perturbs the trace-boundary embedding to generate counterfactual answers, labels them by agreement with the original answer, and then applies contrastive shaping to the resulting representations. This labeling and shaping step is introduced as an independent mechanism that does not reduce to any pre-existing fitted parameters, self-cited uniqueness theorems, or ansatzes from the authors' prior work. No equations or claims in the provided text equate the final detection-friendly embeddings to the perturbation inputs by construction; the agreement labels serve as external supervision signals derived from the intervention rather than tautological redefinitions. The approach remains open to external validation via the released code and does not rely on load-bearing self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no mathematical details, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5493 in / 1077 out tokens · 51193 ms · 2026-05-16T11:10:28.645940+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    ARS generates counterfactual answers through small latent interventions, specifically, perturbing the trace-boundary embedding, and labels each perturbation by whether the resulting answer agrees with the original. It then learns representations that bring answer-agreeing states together and separate answer-disagreeing ones

  • IndisputableMonolith/Foundation/BranchSelection.lean branch_selection echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    We minimize the following objective: L_ARS = −sim(z, z̃+)/τ + log Σ exp(sim(z, z̃′)/τ)

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The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
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The paper appears to rely on the theorem as machinery.
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Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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    Xiaoling Zhou, Mingjie Zhang, Zhemg Lee, Wei Ye, and Shikun Zhang. Hademif: Hallucination detection and mitigation in large language models. InThe Thirteenth International Conference on Learning Representations, 2025. 11 A Datasets and Implementation Details A.1 Input Prompts We provide the detailed textual input as prompts to the language models for diff...

    work page 2025

  43. [43]

    Maintain the overall style and tone of the original context

    work page

  44. [44]

    Introduce 2-3 pieces of plausible but incorrect or unrelated information

    work page

  45. [45]

    Avoid obviously fabricated statements

    work page

  46. [46]

    Keep most original content; integrate misleading parts naturally

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  47. [47]

    Yes" or

    Output ONLY the perturbed context. Original context: {original_context} Prompt for ReasoningTrace Paraphrasing Figure 9:Prompt for reasoning trace paraphrasing. We empirically explored many prompting variants and found this paraphrasing with light information injection can produce reasonably good hallucination detection performance. <|im_start|>user Quest...

    work page arXiv 1961