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DolphinBench: Mapping the Pareto Frontier of Agent Memory

Paper recorded by Signals 4 on 2026-09-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22

Category: cs.AI · 人工智能 · first seen 2026-09-22

Abstract

Agents today often take real-world actions that depend on long-term memory and context recall over time. However, most current memory benchmarks are built for a conversational question-answer format, where the question itself signals that some fact must be retrieved, and often which one. Moreover, benchmarks rarely require anything beyond accuracy from submissions, allowing memory systems to make

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#6 most recent of 340 cs.AI papers we have recorded · ↑ newer: RRSI: Regularized Recursive Self-Improvement of Agent Harnesses · ↓ older: Rare Event Estimation via Iterative Unalignment
Cite this page: DolphinBench: Mapping the Pareto Frontier of Agent Memory: the #6 most recent of 340 cs.AI papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/dolphinbench-mapping-the-pareto-frontier-of-agent-memory.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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