arxiv: 2604.10027 · v1 · submitted 2026-04-11 · 💻 cs.CV

Recognition: unknown

SinkTrack: Attention Sink based Context Anchoring for Large Language Models

Xu Liu , Guikun Chen , Wenguan Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:43 UTC · model grok-4.3

classification 💻 cs.CV

keywords attention sinkcontext anchoringhallucination mitigationlarge language modelsmulti-modal modelstraining-free

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The pith

By injecting contextual features into the attention sink token, SinkTrack keeps large language models focused on the original input throughout generation, reducing hallucination and forgetting.

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

Large language models tend to shift their attention to newly generated tokens, causing them to forget initial context and produce hallucinations. This paper identifies the attention sink phenomenon, where models consistently focus high attention on the first token of a sequence. SinkTrack leverages this by treating the first token as a fixed anchor and embedding important input information, such as from text or images, into it. This simple modification ensures the model stays connected to the starting context during the entire output process. The approach needs no training and shows gains on both text-only and vision-language tasks.

Core claim

SinkTrack turns the <BOS> token into an information anchor by injecting key contextual features derived from the input into its representation. This prevents attention drift away from the initial context, thereby mitigating hallucination and context forgetting in LLMs. The method is training-free and demonstrates consistent improvements across textual and multi-modal benchmarks.

What carries the argument

The attention sink at the first token (<BOS>), repurposed as a context anchor through feature injection into its hidden representation.

Load-bearing premise

That consistently high attention to the BOS token can be turned into an effective context anchor by injecting input-derived features into its representation without side effects or per-model tuning.

What would settle it

A controlled experiment showing no reduction in attention drift or hallucination rates when using the modified first token representation would disprove the effectiveness of this anchoring method.

Figures

Figures reproduced from arXiv: 2604.10027 by Guikun Chen, Wenguan Wang, Xu Liu.

**Figure 2.** Figure 2: Attention drift and sink (the darker the color, the greater the weight). tains a high degree of attention throughout generation, even though it is semantically sparse (cf. 1st column in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: An overview of our methodology exploration. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Figure 4: Soft injection achieves considerable performance improvements (§2.3). learned attention patterns are sensitive to direct manipulation. Abruptly overwriting values in the KV cache disrupts this finely-tuned structure, leading to a severe degradation. This finding establishes a core principle: any effective information injection must preserve the integrity of the LLM’s native computational flow, motivating … view at source ↗

**Figure 5.** Figure 5: SINKTRACK further improves performance. To overcome the limitations of soft injection, we introduce SINKTRACK, an adaptive mechanism centered on a dualtrack attention design. SINKTRACK achieves dynamic information infusion by separating the attention computation for ⟨BOS⟩ from that of all subsequent tokens (cf. Fig. 3c). This separation allows SINKTRACK to inject context adaptively while preserving… view at source ↗

**Figure 6.** Figure 6: Effect of mean-pooling in information injection (Eq. 4). (a) Using the mean-pooled infor [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison between CoT and SINKTRACK. tion Strength. A decaying schedule, which applies stronger injection in earlier layers, yields better performance (60.7% vs 59.9%). This indicates that early contextual anchoring is more beneficial than later-stage interventions. In addition, the sensitivity to injection strength motivates us to develop an adaptive mechanism (§2.4). 3.4.2 HIERARCHICAL ATTE… view at source ↗

**Figure 8.** Figure 8: The illustration of information flow with and without S [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

read the original abstract

Large language models (LLMs) suffer from hallucination and context forgetting. Prior studies suggest that attention drift is a primary cause of these problems, where LLMs' focus shifts towards newly generated tokens and away from the initial input context. To counteract this, we make use of a related, intrinsic characteristic of LLMs: attention sink -- the tendency to consistently allocate high attention to the very first token (i.e., <BOS>) of a sequence. Concretely, we propose an advanced context anchoring method, SinkTrack, which treats <BOS> as an information anchor and injects key contextual features (such as those derived from the input image or instruction) into its representation. As such, LLM remains anchored to the initial input context throughout the entire generation process. SinkTrack is training-free, plug-and-play, and introduces negligible inference overhead. Experiments demonstrate that SinkTrack mitigates hallucination and context forgetting across both textual (e.g., +21.6% on SQuAD2.0 with Llama3.1-8B-Instruct) and multi-modal (e.g., +22.8% on M3CoT with Qwen2.5-VL-7B-Instruct) tasks. Its consistent gains across different architectures and scales underscore the robustness and generalizability. We also analyze its underlying working mechanism from the perspective of information delivery. Our source code is available at https://github.com/67L1/SinkTrack.

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

3 major / 2 minor

Summary. The paper proposes SinkTrack, a training-free, plug-and-play technique that exploits the attention sink phenomenon (high attention to the <BOS> token) by injecting input-derived contextual features (e.g., from images or instructions) directly into the <BOS> hidden-state representation. This is claimed to anchor the model to the initial context throughout generation, thereby reducing hallucination and context forgetting. Empirical results are reported on textual tasks (e.g., +21.6% on SQuAD2.0 with Llama-3.1-8B-Instruct) and multi-modal tasks (e.g., +22.8% on M3CoT with Qwen2.5-VL-7B-Instruct), with an accompanying information-delivery analysis.

Significance. If the core mechanism is shown to preserve the sink property while delivering context, the approach would constitute a lightweight, architecture-agnostic intervention with negligible overhead that could be adopted across many LLMs and VLMs. The reported gains on both text-only and vision-language benchmarks, together with the claim of generalizability across scales, would be of practical interest to the community.

major comments (3)

[Method description and mechanism analysis] The central premise—that feature injection into the <BOS> representation preserves the attention-sink property—is load-bearing yet unverified. The skeptic correctly notes that altering the key/value vector at the BOS position necessarily changes query-key dot products for all subsequent tokens; without before/after attention-weight statistics (e.g., mean attention mass on position 0 across layers and generation steps), it is impossible to confirm that the sink remains intact rather than being an incidental side-effect of richer context.
[Experiments] The experimental section reports absolute gains but provides no ablation on the injection operator itself (additive vs. replacement, scaling factor, which features are selected) nor statistical significance tests across multiple runs. Consequently the link between the BOS modification and the observed improvements on SQuAD2.0 and M3CoT cannot be isolated from simple context enrichment.
[Mechanism analysis] The information-delivery analysis (abstract and §4) does not address the potential feedback loop: once the BOS key is modified, attention scores are recomputed at every step, so any claim that the modified BOS continues to serve as a stable anchor requires explicit verification that attention mass on the BOS position does not decay or become position-dependent after injection.

minor comments (2)

[Abstract] The abstract states that SinkTrack “introduces negligible inference overhead” but does not quantify the added cost (e.g., extra forward-pass operations or memory) relative to the baseline.
[Introduction / Method] Notation for the injected feature vector and the precise update rule applied to the BOS hidden state is not introduced until the method section; a compact equation in the abstract or introduction would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below with clarifications and commitments to strengthen the manuscript where the concerns are valid.

read point-by-point responses

Referee: [Method description and mechanism analysis] The central premise—that feature injection into the <BOS> representation preserves the attention-sink property—is load-bearing yet unverified. The skeptic correctly notes that altering the key/value vector at the BOS position necessarily changes query-key dot products for all subsequent tokens; without before/after attention-weight statistics (e.g., mean attention mass on position 0 across layers and generation steps), it is impossible to confirm that the sink remains intact rather than being an incidental side-effect of richer context.

Authors: We agree that direct before-and-after attention statistics are necessary to rigorously confirm preservation of the sink property rather than incidental enrichment. Our current information-delivery analysis in §4 demonstrates feature propagation but does not include explicit attention-mass comparisons. We will revise the paper to add these statistics: mean attention weights on the BOS token across layers and generation steps, computed on the same prompts with and without injection. This addition will directly address the concern. revision: yes
Referee: [Experiments] The experimental section reports absolute gains but provides no ablation on the injection operator itself (additive vs. replacement, scaling factor, which features are selected) nor statistical significance tests across multiple runs. Consequently the link between the BOS modification and the observed improvements on SQuAD2.0 and M3CoT cannot be isolated from simple context enrichment.

Authors: The referee is correct that the present experiments lack operator ablations and multi-run statistics, limiting causal isolation. We will add a dedicated ablation subsection varying the injection operator (additive vs. replacement), scaling factors, and feature selection, together with mean ± std results over at least three independent runs for the main benchmarks. These revisions will strengthen the link to the BOS modification. revision: yes
Referee: [Mechanism analysis] The information-delivery analysis (abstract and §4) does not address the potential feedback loop: once the BOS key is modified, attention scores are recomputed at every step, so any claim that the modified BOS continues to serve as a stable anchor requires explicit verification that attention mass on the BOS position does not decay or become position-dependent after injection.

Authors: We acknowledge that the existing analysis does not explicitly track attention-mass stability over generation steps to rule out decay or position dependence. We will extend §4 with additional plots and tables showing BOS attention mass across successive decoding steps (with and without SinkTrack) on representative sequences. This will verify the anchor remains stable and address the feedback-loop concern directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity; SinkTrack applies an observed property without self-referential reduction

full rationale

The paper begins from the documented attention sink phenomenon (high attention to the BOS token), which is treated as an external empirical observation drawn from prior literature rather than derived within the work. It then introduces SinkTrack as a plug-and-play injection of input-derived features into the BOS hidden state to maintain context anchoring. No equations are shown that define the anchoring effect in terms of its own outputs, no parameters are fitted to a data subset and then relabeled as predictions, and no load-bearing self-citations or uniqueness theorems close the loop. Performance improvements are measured on external benchmarks (SQuAD2.0, M3CoT) and the information-delivery analysis is interpretive, leaving the central claim independent of its own results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain observation that LLMs allocate high attention to the first token and that this can be exploited by direct representation modification.

axioms (1)

domain assumption LLMs exhibit an intrinsic attention sink to the first token of a sequence
Cited as a known characteristic from prior studies that the method leverages.

pith-pipeline@v0.9.0 · 5565 in / 1169 out tokens · 60216 ms · 2026-05-10T15:43:49.317116+00:00 · methodology

discussion (0)

Reference graph

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"" h_ori: hidden states of the original sequence (Lori ×D h)) h_info: hidden states of the external information (L inf o ×D h)) cfg: configuration for injection rules

14 Published as a conference paper at ICLR 2026 SUMMARY OF THE APPENDIX • §A details the pseudo-code implementation of SINKTRACK. • §B provides in-depth analyses concerning the stability of the hierarchical attention pattern, resis- tance to attention drift, and computational overhead. • §C discusses the limitations of the current approach. A PSEUDO-CODE ...

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Drift Test

Apply final output projection h_out =Output Projection(o_combined) returnh_out B IN-DEPTHANALYSIS: MECHANISM ANDEFFICIENCY This section provides a deeper analysis of the underlying mechanism of SINKTRACKand its com- putational implications. B.1 STABILITY OFHIERARCHICALATTENTIONPATTERN To analyze the structural impact of our injection, we quantify its effe...

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