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arxiv: 2604.10027 · v2 · pith:AOA5CRYTnew · submitted 2026-04-11 · 💻 cs.CV

SinkTrack: Attention Sink based Context Anchoring for Large Language Models

Xu Liu , Guikun Chen , Wenguan Wang This is my paper

Pith reviewed 2026-05-21 01:34 UTC · model grok-4.3

classification 💻 cs.CV
keywords attention sinkcontext anchoringhallucination mitigationcontext forgettinglarge language modelstraining-freeplug-and-playmulti-modal reasoning
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The pith

Treating the BOS token as an information anchor by injecting key contextual features into its representation keeps LLMs focused on the original input throughout generation.

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

The paper seeks to establish that attention drift away from initial context causes hallucination and forgetting in LLMs, and that this can be countered by exploiting the built-in attention sink on the first token. SinkTrack modifies the BOS representation with features from the input image or instruction so that high attention to that token carries the context forward. A reader would care because the method requires no training, adds almost no cost, and produces large gains on both text-only question answering and multi-modal reasoning. If the anchoring works as described, existing models could become more reliable on long or complex inputs without retraining or architectural changes.

Core claim

SinkTrack is a training-free method that treats the <BOS> token as an information anchor and injects key contextual features into its representation. This leverages the intrinsic attention sink behavior so the model remains anchored to the initial input context for the entire generation process, reducing hallucination and context forgetting.

What carries the argument

Attention sink on the <BOS> token, turned into a persistent context carrier by direct feature injection into its hidden state.

If this is right

  • Substantial accuracy gains appear on textual QA benchmarks such as SQuAD2.0 without any parameter updates.
  • Similar gains occur on multi-modal chain-of-thought tasks such as M3CoT.
  • The approach adds negligible inference overhead and works across model families and sizes.
  • The same injection can be analyzed as an explicit information-delivery pathway from early to late tokens.

Where Pith is reading between the lines

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

  • If other attention sinks exist at different positions, similar targeted injections could be tested there.
  • The method implies that context length limits might be partially relaxed by strengthening early-token signals rather than extending windows.
  • Dynamic selection of which features to inject could further improve results on tasks with varying context needs.

Load-bearing premise

The method assumes that high attention to the modified first token will reliably deliver the injected features to later tokens without the model overriding or ignoring the change.

What would settle it

Measure attention weights on the BOS token across generation steps after injection; if those weights drop sharply while task accuracy stays flat or declines, the anchoring mechanism is not operating as claimed.

Figures

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

Figure 1
Figure 1. Figure 1: Hallucination and context forgetting are [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
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. Figure 3: An overview of our methodology exploration. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
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 estab￾lishes 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. Figure 5: SINKTRACK further improves performance. To overcome the limitations of soft injection, we intro￾duce SINKTRACK, an adaptive mechanism centered on a dual￾track attention design. SINK￾TRACK achieves dynamic in￾formation infusion by separat￾ing the attention computation for ⟨BOS⟩ from that of all sub￾sequent tokens (cf. Fig. 3c). This separation allows SINK￾TRACK to inject context adap￾tively while preserving… view at source ↗
Figure 6
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. 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 de￾velop an adaptive mechanism (§2.4). 3.4.2 HIERARCHICAL ATTE… view at source ↗
Figure 8
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

2 major / 1 minor

Summary. The manuscript proposes SinkTrack, a training-free, plug-and-play method that leverages the attention sink phenomenon in LLMs by injecting key contextual features (derived from input image or instruction) into the representation of the <BOS> token. This is intended to anchor the model to the initial context throughout generation, thereby mitigating hallucination and context forgetting. Empirical results report gains of +21.6% on SQuAD2.0 using Llama3.1-8B-Instruct and +22.8% on M3CoT using Qwen2.5-VL-7B-Instruct, with analysis of the information delivery mechanism.

Significance. If the core mechanism is substantiated, the approach offers a lightweight, architecture-agnostic intervention for improving context retention and reducing hallucinations in both text-only and multi-modal LLMs. The reported cross-scale and cross-architecture gains, combined with negligible overhead, could have practical impact on deployment if the attention-sink anchoring is shown to be the causal driver rather than incidental effects.

major comments (2)
  1. [Abstract] The central claim that injecting features into the <BOS> representation ensures the LLM 'remains anchored to the initial input context throughout the entire generation process' (Abstract) rests on two unverified assumptions: (1) that high attention to the modified <BOS> token persists across layers and generation steps, and (2) that downstream attention heads actually read and propagate the injected features. No attention maps, layer-wise activation analysis, or ablation on injection location/layer are described to confirm information flow rather than dilution or override.
  2. [Experiments] The reported performance gains on SQuAD2.0 and M3CoT could arise from prompt-length regularization or incidental effects of feature injection rather than attention-sink anchoring. Without controls that isolate the contribution of the <BOS> modification (e.g., random injection baselines or comparison to non-sink tokens), the link between the intervention and the +21.6% / +22.8% improvements remains unestablished.
minor comments (1)
  1. [Abstract] The abstract states that source code is available at https://github.com/67L1/SinkTrack, but no implementation details, hyper-parameters for feature injection, or exact formulation of the injected contextual features are provided in the visible text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the positive assessment of SinkTrack's potential impact. We address each major comment below with clarifications from the manuscript and indicate where revisions have been made to strengthen the evidence.

read point-by-point responses

  1. Referee: [Abstract] The central claim that injecting features into the <BOS> representation ensures the LLM 'remains anchored to the initial input context throughout the entire generation process' (Abstract) rests on two unverified assumptions: (1) that high attention to the modified <BOS> token persists across layers and generation steps, and (2) that downstream attention heads actually read and propagate the injected features. No attention maps, layer-wise activation analysis, or ablation on injection location/layer are described to confirm information flow rather than dilution or override.

    Authors: We appreciate the referee's emphasis on verifying the mechanism. The manuscript already includes an analysis of the information delivery mechanism to support how injected features influence generation. However, to more directly address the persistence of attention and feature propagation, we have added attention map visualizations across layers and steps, layer-wise activation analyses, and ablations varying the injection location and layer in the revised manuscript. These additions confirm that the modified <BOS> maintains high attention and that downstream heads utilize the injected features without dilution. revision: yes

  2. Referee: [Experiments] The reported performance gains on SQuAD2.0 and M3CoT could arise from prompt-length regularization or incidental effects of feature injection rather than attention-sink anchoring. Without controls that isolate the contribution of the <BOS> modification (e.g., random injection baselines or comparison to non-sink tokens), the link between the intervention and the +21.6% / +22.8% improvements remains unestablished.

    Authors: We agree that stronger isolation of the sink-anchoring effect is valuable. The original experiments demonstrate consistent gains across architectures and tasks, supporting the role of the <BOS> modification. To further rule out alternative explanations, we have incorporated additional baselines in the revision: random feature injection (to control for incidental effects) and feature injection into non-sink tokens (to isolate the attention-sink property). These controls show that gains are specifically tied to anchoring at the sink token rather than prompt regularization or generic injection. revision: yes

Circularity Check

0 steps flagged

No circularity: SinkTrack is an empirical intervention without self-referential derivation

full rationale

The paper presents SinkTrack as a training-free, plug-and-play engineering technique that injects contextual features into the BOS token to leverage the pre-existing attention sink phenomenon for anchoring context during generation. No mathematical derivation chain, fitted parameters renamed as predictions, or load-bearing self-citations appear in the method; the core logic rests on the independently observed attention sink behavior and is validated directly via benchmark experiments rather than reducing to its own inputs by construction. The approach is self-contained as a practical modification with reported empirical gains.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the empirical observation that attention sinks exist and can be repurposed; no new mathematical axioms or invented entities are introduced.

axioms (1)
  • domain assumption LLMs exhibit a consistent attention sink on the first token across architectures and tasks.
    Invoked in the abstract as the foundation for using <BOS> as an anchor.

pith-pipeline@v0.9.0 · 5796 in / 1104 out tokens · 19957 ms · 2026-05-21T01:34:28.829285+00:00 · methodology

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    Drift Test

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