aiqbench analysis
This arXiv paper is about AI agent memory: how an LLM agent can answer questions about its own past trajectory by combining retrieval with executable, deterministic aggregation over an event ledger. The reported gains are substantial on the authors' benchmark (e.g., 35.2% to 49.0% exact match on Qwen3-32B), but the full AMA-Bench gain is only 1.3 points, and the authors themselves note that benefits depend on the task and event specification. Readers should treat this as promising evidence for a narrow class of aggregation-heavy memory tasks rather than a general solution to agent memory.
Beyond Retrieval: Evidence Access and Event Aggregation in Agent Memory
arXiv:2610.19474v1 Announce Type: new Abstract: Questions about an agent's history often require aggregating events, which retrieval alone does not resolve. We combine source-preserving evidence access with executable aggregation. Trajectory Memory (TM) retains the original trajectory and constructs a source-addressable reading view at question time. TallyMem augments it with a reusable event ledger, model-generated event specifications and deterministic execution. With a commit-specific adaptation on 776 questions from 99 held-out MINTEval repositories, TallyMem raises Qwen3-32B exact match from 35.2% to 49.0%, a 13.8-point gain over TM; gains span 12.8-13.9 points across three readers. A controlled probe with event sets parsed independently from raw logs shows an 11.0-point gain, from 82.8% to 93.8%, without routing. On full AMA-Bench, the routed system yields a smaller 1.3-point gain, from 69.8% to 71.1%. These results support executable aggregation on tasks that require combining events, with benefits dependent on the task and event specification, the largest residual system-error category in our diagnostic analysis.
Abstract:Questions about an agent's history often require aggregating events, which retrieval alone does not resolve. We combine source-preserving evidence access with executable aggregation. Trajectory Memory (TM) retains the original trajectory and constructs a source-addressable reading view at question time. TallyMem augments it with a reusable event ledger, model-generated event specifications and deterministic execution. With a commit-specific adaptation on 776 questions from 99 held-out MINTEval repositories, TallyMem raises Qwen3-32B exact match from 35.2% to 49.0%, a 13.8-point gain over TM; gains span 12.8-13.9 points across three readers. A controlled probe with event sets parsed independently from raw logs shows an 11.0-point gain, from 82.8% to 93.8%, without routing. On full AMA-Bench, the routed system yields a smaller 1.3-point gain, from 69.8% to 71.1%. These results support executable aggregation on tasks that require combining events, with benefits dependent on the task and event specification, the largest residual system-error category in our diagnostic analysis.
Artificial Intelligence (cs.AI)
arXiv:2610.19474 [cs.AI]
(or arXiv:2610.19474v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.19474 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 245 words
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