Papers with InfiniteBench
Adaptive Layer Selection for Layer-Wise Token Pruning in LLM Inference (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in processing long contexts. |
| Approach: | They propose a training-free method that adaptively chooses the selection layer for KV cache reduction . they exploit the variance of token ranks ordered by attention score to optimize decoding . |
| Outcome: | The proposed method outperforms state-of-the-art token pruning methods on InfiniteBench, RULER, and NIAH benchmarks. |
LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation (2025.findings-emnlp)
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| Challenge: | Existing methods for cache compression are heuristic and lack dynamic budget allocation . cnn's john mccartney and johnny mccain present a new approach for cache eviction and dynamic budgets . |
| Approach: | They propose a unified framework for cache compression that minimizes information loss in transformer residual streams. |
| Outcome: | The proposed method consistently maintains top performance across task types. |