OBCache: Optimal Brain KV Cache Pruning for Efficient Long-Context LLM Inference

• URL: http://arxiv.org/abs/2510.07651v1
• Date: Thu, 09 Oct 2025 00:58:28 GMT
• Title: OBCache: Optimal Brain KV Cache Pruning for Efficient Long-Context LLM Inference
• Abstract summary: We propose a principled framework that formulates cache eviction as a layer-wise structured pruning problem.<n>We measure the perturbation in attention outputs induced by pruning tokens, with closed-form scores derived for isolated keys, isolated values, and joint key-value pairs.<n>Our scores account not only for attention weights but also for information from value states and attention outputs, thereby enhancing existing eviction strategies with output-aware signals.
• Score: 11.315090790312041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) with extended context windows enable powerful downstream applications but impose significant memory overhead, as caching all key-value (KV) states scales linearly with sequence length and batch size. Existing cache eviction methods address this by exploiting attention sparsity, yet they typically rank tokens heuristically using accumulated attention weights without considering their true impact on attention outputs. We propose Optimal Brain Cache (OBCache), a principled framework that formulates cache eviction as a layer-wise structured pruning problem. Building upon the Optimal Brain Damage (OBD) theory, OBCache quantifies token saliency by measuring the perturbation in attention outputs induced by pruning tokens, with closed-form scores derived for isolated keys, isolated values, and joint key-value pairs. Our scores account not only for attention weights but also for information from value states and attention outputs, thereby enhancing existing eviction strategies with output-aware signals. Experiments on LLaMA and Qwen models demonstrate that replacing the heuristic scores in existing works, which estimate token saliency across different query positions, with OBCache's output-aware scores consistently improves long-context accuracy.

Related papers

• Back from the Future: Key-Value Cache Management by Counter-Causal Surprise [67.1056509495879]
  Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years.<n>We propose a simple yet effective KV eviction scheme motivated by the insight that past tokens which can be well-predicted from more recent tokens are redundant.<n>We evaluate our strategy on various open-source LLMs and benchmark datasets showing competitive or improved performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-30T02:42:51Z)
• Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM [12.14455026524814]
  Large language models (LLMs) excel at complex tasks like question answering and summarization.<n>Existing KV-cache eviction strategies leverage the sparsity in attention to selectively store a subset of tokens.<n>We introduce K-VEC, a novel coverage-aware KV-cache eviction strategy that prioritizes token coverage while evicting tokens in the cache.
  arXiv  Detail & Related papers  (2026-06-28T19:04:47Z)
• Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction [65.710271475739]
  We introduce a global retention-based KV eviction method that learns each token's future utility under a unified memory budget.<n>We show that our method substantially reduces KV memory while matching or surpassing full-cache inference.<n>These results suggest that learned, globally calibrated KV eviction is not only a compression technique, but also a mechanism for improving long-context reasoning.
  arXiv  Detail & Related papers  (2026-05-10T16:47:50Z)
• Reformulating KV Cache Eviction Problem for Long-Context LLM Inference [2.0590127484132523]
  Large language models (LLMs) support long-context inference but suffer from substantial memory and runtime overhead due to Key-Value ( KV) Cache growth.<n>Existing KV Cache eviction methods rely on local attention weights, neglecting the influence of value representations, output projection, and inter-head interactions.<n>We introduce LaProx, a novel eviction strategy that explicitly models the multiplicative interaction between attention maps and projected value states.
  arXiv  Detail & Related papers  (2026-05-08T04:37:22Z)
• Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs [26.951325519894525]
  We propose a novel approach that learns each token's intrinsic importance at creation time via a lightweight retention gate.<n>We show that it consistently outperforms strong eviction and learnable retrieval baselines, especially in low-memory regimes.<n>It even surpasses full-cache models in some settings, showing that selective retention can serve as a form of regularization.
  arXiv  Detail & Related papers  (2025-12-03T00:20:35Z)
• Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction [53.83828564664595]
  Large language models (LLMs) utilize key-value ( KV) cache to store historical information during sequence processing.<n>Current methods for KV cache eviction typically utilize the last window from the pre-filling phase as queries to compute the KV importance scores for eviction.<n>We propose Judge Q, a novel training method which incorporates a soft token list.
  arXiv  Detail & Related papers  (2025-09-13T03:34:12Z)
• Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction [58.044803442346115]
  Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive computational complexity and memory overhead during inference.<n>We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention via delayed bidirectional sparse caching.
  arXiv  Detail & Related papers  (2025-08-04T16:14:03Z)
• Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query [48.52389201779425]
  KV cache memory usage grows substantially with longer text sequences.<n>Existing KV cache eviction methods prune tokens using prefilling-stage attention scores.<n>Lookahead Q-Cache generates low-cost pseudo lookahead queries to better approximate the true decoding-stage queries.
  arXiv  Detail & Related papers  (2025-05-24T10:34:38Z)
• CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences [36.05521425453999]
  Large language models (LLMs) excel at processing long sequences, boosting demand for key-value ( KV) caching.<n>We introduce Cascading and Adaptive KV cache Eviction (CAKE), a novel approach that frames KV cache eviction as a "cake-slicing problem"<n>CAKE assesses layer-specific preferences by considering attention dynamics in both spatial and temporal dimensions, allocates rational cache size for layers accordingly, and manages memory constraints in a cascading manner.
  arXiv  Detail & Related papers  (2025-03-16T12:49:44Z)
• ThinK: Thinner Key Cache by Query-Driven Pruning [63.13363917871414]
  Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications.<n>This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference.<n>We propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels.
  arXiv  Detail & Related papers  (2024-07-30T17:59:08Z)
• Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
  We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models. We propose an importance-driven cache merging strategy to prune redundancy caches. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
  arXiv  Detail & Related papers  (2024-07-25T15:29:05Z)
• PQCache: Product Quantization-based KVCache for Long Context LLM Inference [27.523568511043273]
  Key-Value Cache (KVCache) is the intermediate representations of tokens within Large Language Models (LLMs)<n>We propose PQCache, which employs Product Quantization (PQ) to manage KVCache, maintaining model quality while ensuring low serving latency.<n>PQCache achieves both effectiveness and efficiency, with 4.60% score improvement over existing methods on InfiniteBench.
  arXiv  Detail & Related papers  (2024-07-01T13:05:42Z)
• Training-Free Exponential Context Extension via Cascading KV Cache [49.608367376911694]
  We introduce a novel mechanism that leverages cascading sub-cache buffers to selectively retain the most relevant tokens.<n>Our method reduces prefill stage latency by a factor of 6.8 when compared to flash attention on 1M tokens.
  arXiv  Detail & Related papers  (2024-06-24T03:59:17Z)
• CORM: Cache Optimization with Recent Message for Large Language Model Inference [57.109354287786154]
  We introduce an innovative method for optimizing the KV cache, which considerably minimizes its memory footprint. CORM, a KV cache eviction policy, dynamically retains essential key-value pairs for inference without the need for model fine-tuning. Our validation shows that CORM reduces the inference memory usage of KV cache by up to 70% with negligible performance degradation across six tasks in LongBench.
  arXiv  Detail & Related papers  (2024-04-24T16:11:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.