Attention Sink Phenomenon in Transformers

• Attention sink phenomenon is a structural pattern in transformer models where early or shallow tokens attract a disproportionate share of attention regardless of their semantic importance.
• It emerges from softmax normalization and geometric anchoring in high-dimensional spaces, with empirical studies confirming its universal presence across various model scales and architectures.
• This mechanism influences model efficiency, stability, and security, driving innovative approaches in streaming inference, quantization, and defending against backdoor attacks.

The attention sink phenomenon is a structural pattern in transformer-based neural architectures—especially LLMs—where specific tokens, typically early or “shallow” tokens in a sequence, consistently attract a disproportionately high amount of attention from other tokens. This concentration of attention is independent of the semantic importance of the tokens in question and emerges from fundamental mathematical, geometric, and optimization properties of transformer attention mechanisms. Attention sinks have direct implications for model efficiency, streaming inference, quantization, security, continual learning, and multi-modal grounding.

1. Mathematical Foundations and Universal Emergence

The emergence of attention sinks in autoregressive LLMs fundamentally arises from the normalization constraints of the softmax function used in attention layers. For a query vector $q$ and key vectors $k_j$, the attention distribution is computed as:

$\mathrm{Softmax}(q^{\top}k_j/\sqrt{d})$

where all scores must sum to 1. When a queried token is not semantically aligned with most of its context, the normalization constraint forces the model to assign residual attention weights somewhere; the universal visibility of initial or special tokens in causal decoding makes these tokens natural attractors for this surplus. If an initial token $j=1$ frequently exhibits $x_1 \gg x_j$ for $j>1$, then $\mathrm{Softmax}(x)_1 \approx 1$, and these initial tokens act as consistent “attention sinks.”

Empirical and theoretical studies (Xiao et al., 2023, Gu et al., 14 Oct 2024, Barbero et al., 3 Apr 2025) show that attention sinks emerge universally in both large and small-scale LLMs, across natural and synthetic input domains, and even in models trained with a variety of positional encodings and architectural modifications. Their emergence is robust to factors such as data distribution, optimization rate, and even significant changes to the architecture, provided that a softmax-based normalization is retained in the attention calculation.

2. Geometric and Spectral Perspectives

A geometric interpretation reveals that the presence of attention sinks is a manifestation of the need to establish stable reference frames in high-dimensional representational spaces (Ruscio et al., 4 Aug 2025). The mapping of attention via the softmax confines all attention distributions to a probability simplex with significant curvature (Fisher–Rao geometry). Within this simplex, it is efficient for the network to select one or a few anchor tokens as “reference points,” resulting in the observed attention sink effect.

This geometric imperative is not an artifact of design but a mathematical solution to the challenge of orienting, organizing, and “anchoring” representations as they propagate through depth. Multiple forms of these reference frames appear, shaped by architecture and position encoding:

• Centralized (star-like, usually the first token or [BOS])
• Distributed (multiple local anchors depending on position encoding scaling)
• Bidirectional (dual anchors at the start and end in encoders)

A complementary spectral (SVD-based) perspective (Cancedda, 14 Feb 2024) finds that attention sinks are implemented by low-rank or “dark” subspaces in the embedding/unembedding matrices. Signals trafficking in these low-spectral-value (“dark”) bands are critical for the absorption of excess attention and lossless transmission of attention sink signals through many layers; models remain performant if these components are preserved even as larger, higher-rank portions of the spectrum are pruned.

3. Dynamic Behavior and Mechanistic Explanations

Mechanistically, attention sinks can be traced to at least two interacting phenomena: (a) active–dormant attention head switching and (b) mutual reinforcement between attention logits and value-state suppression (Guo et al., 17 Oct 2024). When a head is “inactive” for a given input pattern, it diverges almost all its attention to the sink token. The corresponding value states for the sink token are actively “drained”—i.e., their norm is minimized—so the excess attention does not pollute the model’s computation.

In contrast, when contextual features demand, the same head may “activate” and allocate attention elsewhere. This switching is observed both in simple synthetic tasks (such as the Bigram-Backcopy) and full-scale LLM pretraining.

The “catch, tag, and release” mechanism (Zhang et al., 2 Feb 2025) further clarifies that attention sinks act as organizational points: they absorb tokens’ attention (“catch”), tag them via coupled outlier features (i.e., large-norm feature activations), and later release the tokens back into the network to enable global operations such as averaging. This process is naturally realized through low-rank structures in the QKV parameter matrices.

4. Functional Implications and Optimization Consequences

Attention sinks are functionally linked to the regulation of information mixing, rank preservation, and model stability. In deep transformers, repeated self-attention can lead to over-mixing, rank collapse, and over-squashing—phenomena where token representations become too similar and important distinctions are lost. By allocating substantial attention to a designated sink (such as the first token), the model introduces an effective “no-op” channel that restricts mixing and preserves the local sensitivity of representations (Barbero et al., 3 Apr 2025). The magnitude of attention sink behavior scales with context length, model depth, and data packing scheme, and removing or misplacing sink tokens during inference (when the model was trained with them fixed) leads to severe performance degradation.

In continual and multi-task learning settings, excessive reliance on prominent sink tokens can induce over-smoothing, whereby cross-task interference is exacerbated and discriminative features are lost (Bai et al., 8 Oct 2024). Mitigation strategies such as pre-scaling encourage attention diversity and non-sink token focus, improving continual learning retention.

From a compression and quantization standpoint, attention sink positions display unusual outlier activations, which complicate naive quantization routines. Strategies such as KVSink (Su et al., 6 Aug 2025) for dynamic detection and preservation of sink tokens during key-value cache quantization demonstrate empirically superior perplexity and quantization efficiency compared to static heuristics.

5. Methodological Applications and Modifications

Optimal handling of attention sinks enables significant advances in model deployment and efficiency:

• In streaming or long-context inference, StreamingLLM (Xiao et al., 2023) retains a small number of sink tokens and a rolling cache of recent tokens, avoiding performance collapse and reducing memory requirements by 22.2× over baseline recomputation methods.
• In vision transformers, excessive attention to the [CLS] token acts as an attention sink that can undermine spatial feature integration. Encoder-decoder architectures with separated [CLS] and patch processing (e.g., EDIT (Feng et al., 9 Apr 2025)) mitigate this, refining aggregation layer by layer and improving top-1 accuracy on benchmark datasets.
• In structured state-space models (SSMs), attention sinks are implemented via learnable prompt initializations that anchor recurrent state updates and prevent numerical instability during long-sequence processing (Meng et al., 1 Aug 2024).
• Multi-modal and vision–LLMs develop visual attention sinks in which irrelevant visual tokens (e.g., background regions with massive activation in fixed “sink dimensions”) attract much of the attention budget. Targeted redistribution, as in the VAR method (Kang et al., 5 Mar 2025), reallocates this surplus to more salient tokens and improves task performance across vision–language and hallucination benchmarks.

6. Manipulation, Vulnerabilities, and Security Implications

Attention sinks are not just an optimization curiosity; their universal presence and amplification effect make them prime targets for both desirable and undesirable model interventions. The attention sink’s role as an amplifier of prefix tokens underpins a new class of backdoor attacks on LLM unlearning (Shang et al., 19 Oct 2025). When a hidden trigger is placed at an attention sink position (typically a shallow prefix), and its value-norms are aligned, the model can be made to “forget” information in normal operation but recover “forgotten” knowledge on demand with the trigger. This duality defeats traditional forget/retain/unlearning benchmarks.

These insights also call for robust auditing mechanisms: detection of unusual sink patterns may reveal tampering or backdoors, while attention calibration techniques (such as ACT (Yu et al., 22 Jun 2024)) can calibrate or redistribute sink attention dynamically during inference and enhance output accuracy with no retraining.

7. Modulation, Generality, and Future Directions

Intervention strategies include adjusting the attention kernel: softmax induces competitive, sum-one normalization and is responsible for enforcing the winner-takes-all property characteristic of attention sinks (Gu et al., 14 Oct 2024). Replacing softmax with non-normalizing kernels (e.g., sigmoid) or designing learnable or explicit “sink tokens” can entirely remove or redirect the sink phenomenon. Additional modifications, such as replacing Adam with SGD (Guo et al., 17 Oct 2024), reduce downstream drift in the residual stream and mitigate quantization bottlenecks.

A geometric approach suggests that architectural choices such as position encoding, initialization, or anchor token specification guide the emergent type (centralized, distributed, bidirectional) and location of attention sinks (Ruscio et al., 4 Aug 2025). Recognizing these as geometric artifacts rather than architectural “bugs” invites future models to embed explicit anchor management, enabling more principled representation learning, interpretability, and potentially improving training efficiency and transfer.

In sum, attention sinks represent a fundamental, mechanism-driven phenomenon in transformer-based models, reflecting both mathematical necessity and emergent optimization. They offer both an essential tool for efficient and robust sequence modeling and a vector for vulnerabilities necessitating careful theoretical and practical consideration.