KVEraser: Learned KV cache editing for efficient localized context erasing in LLMs
KVEraser is a learned method for efficiently erasing specific spans from an LLM's KV cache without full recomputation of subsequent tokens. The approach replaces only the KV states of the erased interval with learned steering states, using a two-stage training pipeline of generic pre-training followed by task-specific fine-tuning. On contexts from 1K–32K tokens, KVEraser nearly matches full recomputation quality while incurring only 24% latency overhead versus a 17.6x increase for exact recomputation, with demonstrated generalization to long-document QA with harmful factual distractors.
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VaSE: Value-Aware Stochastic KV Cache Eviction improves reasoning model efficiency
A new arXiv preprint introduces Value-aware Stochastic KV Cache Eviction (VaSE), a training-free method for compressing KV caches in long-chain-of-thought reasoning models. The authors identify two key failure modes in prior eviction approaches — catastrophic repetition loops caused by evicting high-magnitude value states, and low cache diversity — and address both with targeted protections and stochastic eviction. On six reasoning tasks with Qwen3 models at 4x compression, VaSE outperforms the current best selection-based sparse attention method and exceeds the strongest eviction baseline by over 4%, while supporting FlashAttention2 and maintaining a static memory footprint.
Latent Context Language Models (LCLMs) achieve competitive encoder-decoder KV cache compression at scale
Researchers introduce Latent Context Language Models (LCLMs), a family of encoder-decoder compressors that map long token sequences to shorter latent embeddings consumed by a decoder, targeting the KV cache memory bottleneck in long-context inference. The authors conduct architecture search and continually pre-train 0.6B-encoder/4B-decoder models on over 350B tokens at compression ratios of 1:4, 1:8, and 1:16. LCLMs improve the Pareto frontier across general-task performance, compression speed, and peak memory, and are demonstrated as efficient backbones for long-horizon agents that can skim compressed context and expand relevant segments on demand. The work closes a previously noted gap between encoder-decoder approaches and KV cache compression methods on the accuracy-efficiency frontier.
Mastering Long Contexts in LLMs with KVPress
NVIDIA and Hugging Face present KVPress, a library for compressing the KV cache in large language models to enable more efficient long-context inference. The tool implements multiple KV cache compression ("pressing") algorithms that reduce memory footprint and latency without retraining models. KVPress is positioned as a practical toolkit for deploying LLMs in long-context scenarios where KV cache size becomes a bottleneck.
Unlocking Longer Generation with Key-Value Cache Quantization
This Hugging Face blog post covers KV cache quantization as a technique to reduce memory consumption during LLM inference, enabling longer context generation without proportional VRAM increases. The post likely explains how quantizing the key-value cache (e.g., to INT8 or lower precision) trades minimal accuracy for significant memory savings. This is directly relevant to inference efficiency and long-context deployment patterns.
Reroute: Training-free recoverable visual token routing for vision-language models
A new arXiv preprint proposes Reroute, a training-free plug-in that replaces the standard rank-and-remove visual token pruning paradigm in VLMs with a recoverable routing mechanism. Instead of permanently discarding low-ranked tokens, Reroute defers them to re-enter the candidate pool at later decoder stages, addressing the problem that token importance shifts across decoder depth. Evaluated on LLaVA-1.5 and Qwen backbones augmented with FastV, PDrop, and Nüwa pruning methods, Reroute improves grounding performance under aggressive token reduction without sacrificing general VQA accuracy. The approach preserves the theoretical compute and KV-cache budget of the underlying pruning method.
LCGuard: Adversarial Training Framework for Safe KV Cache Sharing in Multi-Agent LLM Systems
LCGuard introduces a framework for preventing sensitive information leakage when multi-agent LLM systems share KV caches as a latent communication channel. The approach formalizes leakage operationally via reconstruction: a shared cache artifact is deemed unsafe if an adversarial decoder can recover sensitive inputs from it. An adversarial training loop pits a reconstructor against LCGuard's representation-level transformations, which aim to preserve task-relevant semantics while suppressing recoverable sensitive content. Empirical results across multiple model families and multi-agent benchmarks show reduced reconstruction-based leakage and attack success rates with competitive task performance.
KV Cache from scratch in nanoVLM
This Hugging Face blog post walks through implementing a key-value (KV) cache from scratch within the nanoVLM framework, a minimal vision-language model codebase. The post serves as a technical tutorial explaining how KV caching works in transformer-based multimodal models and how to integrate it for inference efficiency. It targets practitioners seeking to understand the mechanics of KV caching in the context of VLMs rather than just using it as a black box.
ReasonAlloc: Hierarchical KV Cache Budget Allocation for Long-CoT Reasoning Models
ReasonAlloc is a training-free framework that reframes decoding-time KV cache compression as a hierarchical budget allocation problem, operating at both layer-wise (offline) and head-wise (online) levels. The method identifies an architecture-driven pattern called the 'Reasoning Wave' to guide layer preallocation, then dynamically reallocates to information-rich heads during decoding. Evaluated on MATH-500 and AIME 2024 using DeepSeek-R1-Distill and AceReason models, it outperforms uniform-budget baselines (R-KV, SnapKV, Pyramid-RKV) especially at small budgets of 128–512 tokens, with negligible overhead.