REMORY is a neural memory network that enhances context compaction by adding soft memory tokens to summaries, improving source attribution without significant loss of insight coverage and achieving near-full-context performance with minimal input positions.
The chart will appear after repeat observations. The current metric comes from the source.
2 source votesReal observations only. History before source connection is not reconstructed.
It enhances context handling in long-horizon agents by adding memory tokens to summaries, improving performance with minimal input usage.
The paper introduces Trace2Env, a learning-free framework that uses historical interaction traces to create a reusable environment worldbook for simulating realistic environments without rebuilding the original system. It improves next-observation fidelity and long-horizon interaction consistency compared to conventional prompt-based language world models.
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4 days ago