[Submitted on 26 Jul 2026]
Title:When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
View a PDF of the paper titled When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents, by Shweta Mishra and 1 other authors
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Abstract:Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dialogue history, not whether remembered facts change what a tool-using agent does. We present MERIT (Memory Evaluation for Realistic Instrumented Tasks), a benchmark and harness that measures the marginal utility of memory for task-executing agents under explicit cost accounting. MERIT provides episodic tool-use tasks in three domains whose dependence on earlier-episode facts is verified by an automated leak check; a difficulty ladder ending in updated-fact recall; controlled memory corruption; and full token and dollar metering of every memory operation. Across 23,440 scored episodes ($42.57), a two-generation pilot on gpt-4.1-mini and a preregistered 3-model x 3-seed grid (GPT-4.1, Claude Haiku 4.5; memory side held fixed), memory lifts dependent-task success from a leak-verified floor of 0.00 to 0.55-1.00. On updated facts, embedding retrieval collapses unpredictably (0.30-0.95 across models; max seed gap 0.45), and agents act on a correctly retrieved value only 55% of the time, while update-on-write stores (a structured fact store and, notably, LLM summarization) remain at 0.70-1.00; the hybrid is worse than the fact store alone. A latest-generation spot-check (Claude Sonnet 5, gated on a clean full-replay control) reproduces the pattern. Swapping a memory's implementation moves task success by up to 60 points, and full replay is never economical: the best condition per domain delivers 2.7-3.9x its marginal utility per dollar. We release the benchmark, harness, and all traces.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.05441 [cs.AI]
(or arXiv:2609.05441v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.05441
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shweta Mishra [view email] [v1] Sun, 26 Jul 2026 07:42:00 UTC (65 KB)
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