Your AI agent doesn't know when its memory is gone
A new paper introduces MemDecay, a training-free region-aware KV cache eviction policy for LLM agents. It assigns region-specific priorities and decay rates, preserving critical information under fixed cache budget. Experiments show system tokens have much longer half-lives than scratchpad tokens, and pinning system regions retains perfect accuracy where baselines fail.
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[Submitted on 12 Jul 2026]
Title:MemDecay: Region-Aware KV Cache Eviction for Efficient LLM Agent Inference
View a PDF of the paper titled MemDecay: Region-Aware KV Cache Eviction for Efficient LLM Agent Inference, by Venkatesha Matam and 1 other authors
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Abstract:Large language model (LLM) agents accumulate heterogeneous context, including system instructions, plans, user turns, retrieved documents, tool outputs, and intermediate reasoning, whose key-value (KV) cache can become a major memory bottleneck. Existing eviction policies generally apply the same attention- or recency-based rule to every token, ignoring semantic structure already available to the agent orchestrator.
We introduce MemDecay, a training-free, region-aware KV-cache eviction policy. MemDecay assigns tokens region-specific base priorities and decay rates, refreshes retention scores when tokens receive attention, and evicts the lowest-scoring pages under a fixed cache budget while allowing critical regions to be pinned. We also provide a procedure for calibrating decay rates from measured attention lifetimes.
We evaluate MemDecay at approximately 450 and 1,700 token contexts using Qwen2.5-1.5B and 3B. Across all settings, attention lifetimes differ by an order of magnitude across regions: system-token half-lives range from 148 to 189 decoding steps, compared with 14 to 16 for scratchpad tokens. Pinning preserves system-region facts at full-cache accuracy in every setting, while no baseline preserves more than 13 of 24. Region-aware retention remains effective as context grows, whereas recency-based retention collapses. Accumulated-attention retention performs better on unpinned content, however, and ablations identify attention-score normalization as the main limitation of the current formulation. These results establish semantic prompt structure as a robust signal for KV-cache management while clarifying how it should be combined with attention-based importance.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.10582 [cs.LG]
(or arXiv:2607.10582v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.10582
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Keon Kim [view email] [v1] Sun, 12 Jul 2026 05:35:26 UTC (86 KB)
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