CLAA: Cross-Layer Attention Aggregation for Accelerating LLM Prefill
arXiv:2602.16054v1 Announce Type: new Abstract: The prefill stage in long-context LLM inference remains a computational bottleneck. Recent token-ranking heuristics accelerate inference by selectively processing a subset of semantically relevant tokens. However, existing methods suffer from unstable token importance estimation, often varying between layers. Evaluating token-ranking quality independently from heuristic-specific architectures is challenging. To address this, we introduce an Answer-Informed Oracle, which defines ground-truth token importance by measuring attention from generated answers back to the prompt. This oracle reveals that existing heuristics exhibit high variance across layers: rankings can degrade sharply at specific layers, a failure mode invisible to end-to-end benchmarks. The diagnosis suggests a simple fix: aggregate scores across layers rather than relying on any single one. We implement this as Cross-Layer Attention Aggregation (CLAA), which closes the gap
arXiv:2602.16054v1 Announce Type: new Abstract: The prefill stage in long-context LLM inference remains a computational bottleneck. Recent token-ranking heuristics accelerate inference by selectively processing a subset of semantically relevant tokens. However, existing methods suffer from unstable token importance estimation, often varying between layers. Evaluating token-ranking quality independently from heuristic-specific architectures is challenging. To address this, we introduce an Answer-Informed Oracle, which defines ground-truth token importance by measuring attention from generated answers back to the prompt. This oracle reveals that existing heuristics exhibit high variance across layers: rankings can degrade sharply at specific layers, a failure mode invisible to end-to-end benchmarks. The diagnosis suggests a simple fix: aggregate scores across layers rather than relying on any single one. We implement this as Cross-Layer Attention Aggregation (CLAA), which closes the gap to the oracle upper bound and reduces Time-to-First-Token (TTFT) by up to 39\% compared to the Full KV Cache baseline.
Executive Summary
This article introduces Cross-Layer Attention Aggregation (CLAA), a novel approach to accelerate long-context Large Language Model (LLM) inference. CLAA addresses the computational bottleneck in LLM prefill by aggregating token importance scores across layers, rather than relying on a single layer. The proposed Answer-Informed Oracle provides ground-truth token importance, revealing high variance in existing token-ranking heuristics across layers. CLAA significantly improves Time-to-First-Token (TTFT) by up to 39% compared to the Full KV Cache baseline, closing the gap to the oracle upper bound. This research has significant implications for LLM inference, particularly in applications requiring high-speed processing of long contexts.
Key Points
- ▸ CLAA is a novel approach to accelerate LLM inference by aggregating token importance scores across layers.
- ▸ The Answer-Informed Oracle provides ground-truth token importance, revealing high variance in existing token-ranking heuristics.
- ▸ CLAA improves TTFT by up to 39% compared to the Full KV Cache baseline, closing the gap to the oracle upper bound.
Merits
Effective Solution to Computational Bottleneck
CLAA addresses the computational bottleneck in LLM prefill, enabling faster processing of long contexts.
Improved Accuracy and Consistency
CLAA's aggregation of token importance scores across layers improves accuracy and reduces variance in existing heuristics.
High Potential for Real-World Applications
CLAA's significant improvement in TTFT has major implications for LLM inference in applications requiring high-speed processing of long contexts.
Demerits
Limited Generalizability
The proposed solution may not be directly applicable to all LLM architectures, requiring further adaptation and fine-tuning.
Potential Over-Reliance on Oracle
The Answer-Informed Oracle may not be directly applicable to all scenarios, potentially limiting the generalizability of CLAA.
Expert Commentary
The proposed CLAA approach is a significant contribution to the field of efficient LLM inference, leveraging attention mechanisms to improve token importance estimation and reduce variance in existing heuristics. While the work has several merits, including its effectiveness in addressing the computational bottleneck and improving accuracy and consistency, it also has limitations, such as limited generalizability and potential over-reliance on the oracle. Further research is needed to adapt and fine-tune CLAA for broader applicability and to explore its potential in various LLM architectures.
Recommendations
- ✓ Further research is needed to adapt and fine-tune CLAA for broader applicability and to explore its potential in various LLM architectures.
- ✓ Investigate the potential of CLAA in conjunction with other efficient LLM inference techniques to further improve performance and reduce computational costs.
