The supersession gap in LLM agents—failing to use current facts and discard superseded ones—is a distinct failure not fixed by scale or memory size, but improvable via RL training on a new environment.
Pan, Yuxin Jiang, and Kam-Fai Wong
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
ElasticMem enables LLM agents to learn adaptive latent memory retrieval and elastic budget allocation, improving QA accuracy by 24-26% and ALFWorld success by 27-66% over baselines with lower token cost.
MemLens benchmark shows long-context LVLMs lose accuracy with length while memory agents lose visual fidelity, with multi-session reasoning below 30% for most systems and neither approach solving the task alone.
citing papers explorer
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Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
The supersession gap in LLM agents—failing to use current facts and discard superseded ones—is a distinct failure not fixed by scale or memory size, but improvable via RL training on a new environment.
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ElasticMem: Latent Memory as a Learnable Resource for LLM Agents
ElasticMem enables LLM agents to learn adaptive latent memory retrieval and elastic budget allocation, improving QA accuracy by 24-26% and ALFWorld success by 27-66% over baselines with lower token cost.