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ElasticMem addresses the inefficiency of fixed memory allocations in LLM agents by treating long-term memory as an elastic latent resource optimized via policy learning. This approach dynamically assigns variable budgets to retrieved memories, significantly improving QA accuracy and embodied agent success rates while reducing token consumption. The framework demonstrates that adaptive retrieval mechanisms can outperform rigid similarity-based methods in complex, multi-step reasoning tasks.
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents
arXiv:2605.30690v2 Announce Type: replace Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. However, existing memory-augmented methods typically treat memory as a fixed resource: text-space approaches concatenate retrieved memories into the context window, causing substantial token overhead and sensitivity to noisy evidence, while latent-space approaches reduce textual cost but still rely on rigid retrieval or fixed-capacity memory interfaces. This creates a mismatch between query-dependent memory utility and fixed memory allocation. We propose ElasticMem, a memory-augmented LLM framework that learns to use memory as an elastic latent resource. ElasticMem builds an offline latent memory bank with retrieval keys and content caches, retrieves memories adaptively from the reasoner's hidden state, assigns each retrieved memory a variable latent budget through a learned policy, and injects selected latent states as soft memory tokens for generation. The full memory-use process is optimized with downstream task rewards through group-relative policy optimization. We evaluate ElasticMem on MemorySuite, covering memory-intensive QA and embodied agent control. Across Qwen2.5-3B-Instruct and Qwen2.5-7B-Instruct backbones, ElasticMem improves weighted-average QA accuracy by 26.2% and 24.6% and ALFWorld success rate by 66.3% and 27.2% relative to the strongest baselines, while consuming the fewest tokens on ALFWorld on average. Ablations and qualitative analyses further show that adaptive retrieval and elastic budget allocation help ElasticMem prioritize useful evidence and transferable plans beyond rigid cosine similarity.
Abstract:Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. However, existing memory-augmented methods typically treat memory as a fixed resource: text-space approaches concatenate retrieved memories into the context window, causing substantial token overhead and sensitivity to noisy evidence, while latent-space approaches reduce textual cost but still rely on rigid retrieval or fixed-capacity memory interfaces. This creates a mismatch between query-dependent memory utility and fixed memory allocation. We propose ElasticMem, a memory-augmented LLM framework that learns to use memory as an elastic latent resource. ElasticMem builds an offline latent memory bank with retrieval keys and content caches, retrieves memories adaptively from the reasoner's hidden state, assigns each retrieved memory a variable latent budget through a learned policy, and injects selected latent states as soft memory tokens for generation. The full memory-use process is optimized with downstream task rewards through group-relative policy optimization. We evaluate ElasticMem on MemorySuite, covering memory-intensive QA and embodied agent control. Across Qwen2.5-3B-Instruct and Qwen2.5-7B-Instruct backbones, ElasticMem improves weighted-average QA accuracy by 26.2% and 24.6% and ALFWorld success rate by 66.3% and 27.2% relative to the strongest baselines, while consuming the fewest tokens on ALFWorld on average. Ablations and qualitative analyses further show that adaptive retrieval and elastic budget allocation help ElasticMem prioritize useful evidence and transferable plans beyond rigid cosine similarity.
Computation and Language (cs.CL)
arXiv:2605.30690 [cs.CL]
(or arXiv:2605.30690v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.30690 Focus to learn more arXiv-issued DOI via DataCite
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