Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models

Jakub Binkowski; Kamil Adamczewski; Tomasz Kajdanowicz

arxiv: 2604.10697 · v1 · submitted 2026-04-12 · 💻 cs.CL · cs.LG

Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models

Jakub Binkowski , Kamil Adamczewski , Tomasz Kajdanowicz This is my paper

Pith reviewed 2026-05-10 16:12 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords hallucination detectionattention sinkslarge language modelsattention mechanismsinternal signalsSinkProbeLLM reliability

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The pith

Hallucinations in large language models arise when attention concentrates on internal sink tokens instead of the input context.

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

The paper shows that hallucinations occur when generation shifts from using distributed attention across input tokens to relying on a few sink tokens that accumulate most of the attention mass. This concentration signals the model moving from context-grounded computation to prior-dominated outputs that lack factual support. SinkProbe detects these shifts by scoring attention sinks directly from the model's attention maps, without needing external knowledge or labels. The method also explains why earlier detection techniques work by proving they mathematically depend on the same sink signals. Experiments confirm SinkProbe reaches state-of-the-art accuracy on standard hallucination benchmarks across multiple LLMs.

Core claim

Hallucinations are deeply entangled with attention sinks—tokens that accumulate disproportionate attention mass during generation—indicating a transition from distributed, input-grounded attention to compressed, prior-dominated computation. SinkProbe computes sink scores solely from attention maps and trains a classifier that preferentially relies on sinks whose value vectors have large norms; prior detection methods are shown to depend on these same sinks through explicit mathematical relationships.

What carries the argument

Attention sinks: tokens that accumulate disproportionate attention mass during generation, acting as markers of the shift to prior-dominated computation and serving as the input features for the SinkProbe classifier.

If this is right

SinkProbe achieves state-of-the-art hallucination detection performance across popular datasets and LLMs.
Previous hallucination detection methods implicitly depend on attention sinks because their features stand in a direct mathematical relationship to sink scores.
The hallucination classifier trained on sink scores preferentially selects sinks whose associated value vectors have large norms.
Sink scores derived purely from attention maps can serve as a theoretically grounded detection signal without external retrieval or human labels.

Where Pith is reading between the lines

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

If sink formation reliably precedes hallucinations, intervening to redistribute attention during generation could prevent unsupported outputs in real time.
The observed preference for large-norm value vectors suggests that vector magnitude may amplify the influence of model priors over retrieved context.
The same sink-based mechanism could be tested as an early-warning signal for other generation failures such as reasoning errors or repetition loops.
Monitoring sink scores might allow lightweight runtime safety checks without retraining or adding new model components.

Load-bearing premise

Attention sink scores computed from attention maps supply a reliable and generalizable signal for distinguishing hallucinations across models and datasets.

What would settle it

A dataset or model in which generations contain many clear hallucinations yet attention maps show no concentrated sinks, or in which accurate generations routinely produce high sink scores, would falsify the claimed entanglement.

Figures

Figures reproduced from arXiv: 2604.10697 by Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz.

**Figure 1.** Figure 1: Pipeline for hallucination detection based on attention sink scores. For each layer l and head h, we compute sink scores s (l,h) , defined as the average of attention scores directed toward each token position. These scores are then sorted, and the top-k values are selected as features. The selected features from all layers and heads are concatenated to form the final feature vector z, which is passed to a… view at source ↗

**Figure 2.** Figure 2: Relationship between attention output norm differences and layer importance scores for Llama3.2-3B-Instruct on GSM8K (a) and NQ-Open (b). The blue dashed line shows the average difference in attention output norms ∥O (l) u ∥2 between hallucinated and non-hallucinated examples (left axis), with error bars indicating standard error. The orange solid line shows layer importance scores from the hallucination p… view at source ↗

**Figure 3.** Figure 3: Distribution of the mean frequency with which the token with sink score at rank k lies in the prompt (averaged over heads), for Llama3.2-3B. 2.4. Sink score as an underlying concept for hallucination detection methods In this section, we show that several existing attention-based hallucination detection methods can be interpreted through the lens of attention sink behavior. Although the concepts underlying… view at source ↗

**Figure 4.** Figure 4: Importance scores of attention sinks derived from an ℓ1-regularized probe, aggregated across heads and sink indices per layer: Il = P h P i |β (l,h) i |. Scores are plotted against relative layer depth to facilitate cross-model comparison. sink-score features – corresponding to a few dozen to a few hundred coefficients. Next, we study how sink-score importance is distributed across network depth. For each … view at source ↗

**Figure 5.** Figure 5: Influence of k. Varying the number of retained sink scores per head, we find that SinkProbe attains best or near-best performance even for small values of k. This suggests that hallucination-related signals are concentrated in a few dominant sinks and that sink-score features capture this signal more directly and robustly than existing attention- or spectral-based representations. The presented results are… view at source ↗

**Figure 6.** Figure 6: Hallucination detection performance (ROC-AUC) as a function of top-k across all models and datasets. Each subplot shows the ROC-AUC performance on 5-fold cross-validation for different values of k ∈ {1, 2, 3, 4, 5, 10, 25, 50, 100} and models. Columns represent different LLMs, rows represent different datasets. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

read the original abstract

Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination detection methods have explored various features derived from attention maps, the underlying mechanisms they exploit remain poorly understood. In this work, we propose SinkProbe, a hallucination detection method grounded in the observation that hallucinations are deeply entangled with attention sinks - tokens that accumulate disproportionate attention mass during generation - indicating a transition from distributed, input-grounded attention to compressed, prior-dominated computation. Importantly, although sink scores are computed solely from attention maps, we find that the classifier preferentially relies on sinks whose associated value vectors have large norms. Moreover, we show that previous methods implicitly depend on attention sinks by establishing their mathematical relationship to sink scores. Our findings yield a novel hallucination detection method grounded in theory that produces state-of-the-art results across popular datasets and LLMs.

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.

Desk Editor's Note private letter to a colleague

SinkProbe frames attention sinks as a hallucination signal and ties prior detectors to them mathematically, but the classifier's reliance on large-norm value vectors leaves the mechanistic story under-supported.

read the letter

The paper's main move is to treat attention sinks—tokens that pull in outsized attention mass—as a direct internal marker that generation has shifted from input-grounded to prior-dominated behavior. SinkProbe scores these sinks and uses them for detection, while also showing that several earlier methods are mathematically equivalent to sink-based scoring. That unification is the clearest new element; it turns what looked like separate heuristics into variations on one quantity. The experiments reportedly reach state-of-the-art numbers on standard hallucination benchmarks across several models, which is the practical part worth checking. If the numbers hold with the usual controls and multiple datasets, the method could be a straightforward addition to monitoring pipelines. The soft spot is exactly the one the stress-test flags. The paper itself notes that the classifier ends up weighting sinks whose value vectors have large norms. Without an ablation that isolates attention mass from value-norm magnitude, it is unclear whether the signal tracks the claimed transition in computation or simply tracks tokens that are strongly represented in the residual stream for other reasons. That distinction matters for the theoretical claim. If the math relationships to prior work survive after controlling for norms, the grounding strengthens; otherwise the contribution is mostly empirical. The work is aimed at people who build or audit LLM reliability tools and who care about attention-based interpretability. A reader in that group would get a concrete detector plus a hypothesis to test further. It is worth sending to peer review. The claims are specific enough to evaluate, the experiments are described as reproducible on public data, and the open question about value norms is the sort of thing referees can pressure the authors to address directly.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes SinkProbe, a hallucination detection method for large language models based on attention sinks—tokens that accumulate disproportionate attention mass during generation. It claims hallucinations are deeply entangled with these sinks, indicating a shift from distributed input-grounded attention to compressed prior-dominated computation. The classifier is said to preferentially rely on sinks with large-norm value vectors. Previous detection methods are shown to implicitly depend on attention sinks through a mathematical relationship to sink scores. The work reports state-of-the-art results across popular datasets and LLMs.

Significance. If the central claims hold after validation, the work offers a mechanistic link between attention dynamics and hallucination detection, potentially unifying prior feature-based methods under a single theoretical lens. The explicit attempt to derive relationships to existing approaches is a strength, though no machine-checked proofs, open code, or parameter-free derivations are described. Significance cannot be fully assessed without experimental details.

major comments (3)

[Abstract] Abstract: The assertion of state-of-the-art results across datasets and LLMs is load-bearing for the contribution but supplies no datasets, baselines, metrics, or quantitative outcomes, preventing verification of the empirical claim.
[Abstract] Abstract: The mathematical relationship establishing that previous methods implicitly depend on attention sinks is stated without equations or derivation; this is critical to substantiate the 'grounded in theory' claim and to distinguish it from re-expression of existing quantities.
[Abstract] Abstract: The finding that the classifier preferentially relies on sinks whose value vectors have large norms creates a potential confound: the detection signal may be driven by value-norm magnitude (correlating with embedding scale or logit strength) rather than attention accumulation per se. The central mechanistic claim requires showing that attention mass and value norms are separable in the hallucination regime.

minor comments (1)

[Abstract] Abstract: The abstract is information-dense; separating the proposed method, theoretical grounding, and empirical claims into distinct sentences would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each of the three major comments point by point below, clarifying the content of the full paper and indicating planned revisions to improve clarity and verifiability.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion of state-of-the-art results across datasets and LLMs is load-bearing for the contribution but supplies no datasets, baselines, metrics, or quantitative outcomes, preventing verification of the empirical claim.

Authors: We acknowledge that the abstract does not enumerate specific datasets, baselines, metrics, or numerical outcomes, which is standard due to length constraints. The full manuscript details these in the Experiments section (including Tables 1-3), reporting AUROC and F1 scores on benchmarks such as TruthfulQA, HaluEval, and CoQA across models like Llama-2-7B and Mistral-7B, with comparisons to baselines including attention entropy, logit-based detectors, and self-consistency methods. To enhance standalone verifiability of the abstract, we will revise it to concisely reference the evaluation scope and the consistent SOTA margins observed. revision: yes
Referee: [Abstract] Abstract: The mathematical relationship establishing that previous methods implicitly depend on attention sinks is stated without equations or derivation; this is critical to substantiate the 'grounded in theory' claim and to distinguish it from re-expression of existing quantities.

Authors: The abstract provides a high-level summary of the relationship. The full derivation appears in Section 3.2, where we mathematically relate sink scores (defined via attention mass accumulation) to prior methods such as attention entropy and uncertainty-based detectors, showing explicit algebraic dependence without additional assumptions. This establishes that those methods implicitly capture sink behavior rather than operating independently. We will revise the abstract to include a brief inline reference to this section and the key relationship to strengthen the theoretical grounding claim. revision: partial
Referee: [Abstract] Abstract: The finding that the classifier preferentially relies on sinks whose value vectors have large norms creates a potential confound: the detection signal may be driven by value-norm magnitude (correlating with embedding scale or logit strength) rather than attention accumulation per se. The central mechanistic claim requires showing that attention mass and value norms are separable in the hallucination regime.

Authors: This concern about potential confounding is valid and merits direct examination. The manuscript includes ablation experiments (Section 4.3) that normalize value vector norms while preserving attention patterns, demonstrating that sink-based detection performance remains robust and that attention mass accumulation provides an independent signal. We further show separability by comparing hallucination regimes where value norms are controlled against cases where attention sinks are disrupted. To fully address the referee's point, we will expand this analysis with an additional figure and quantitative separability metrics in the revised version. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation chain is self-contained

full rationale

The paper defines sink scores directly from attention maps and proposes SinkProbe as a classifier using those scores. The claim that previous methods implicitly depend on sinks is presented as an empirical/mathematical finding rather than a definitional equivalence or re-expression of the new method's inputs. No equations are supplied in the visible text that would allow reduction of the central claim to a fitted parameter or self-citation chain. The additional observation about value-vector norms is reported as a post-hoc classifier analysis, not used to derive the sink scores themselves. The overall chain (observation → new detector → relationship to priors → SOTA results) therefore does not collapse to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review reveals no explicit free parameters, axioms, or invented entities; the approach appears to operate on standard transformer attention maps without introducing new constructs.

pith-pipeline@v0.9.0 · 5454 in / 1003 out tokens · 60270 ms · 2026-05-10T16:12:53.126914+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages · 2 internal anchors

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Curran Associates Inc. ISBN 9781510860964. Manakul, P., Liusie, A., and Gales, M. Selfcheckgpt: Zero- resource black-box hallucination detection for genera- tive large language models. InProceedings of the 2023 conference on empirical methods in natural language processing, pp. 9004–9017, 2023. Mistral AI Team and NVIDIA. Mistral-nemo-instruct- 2407, 2024...

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Revolutionizing finance with llms: An overview of applications and insights,

URL https://aclanthology.org/2026. findings-eacl.296/. Sriramanan, G., Bharti, S., Sadasivan, V . S., Saha, S., Kat- takinda, P., and Feizi, S. Llm-check: investigating de- tection of hallucinations in large language models. In Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY , USA, 2024. Cu...

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