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Geometry Guided Evidence Preserving Memory (GEM), a training free compressor that protects task and execution evidence before using geometric residuals to complete coverage, is introduced, showing that efficient agent history compression should optimize for preserved task evidence rather than geometric coverage alone.
This work proposes State Conditioned Compression (StateComp), a framework that determines when historical interactions can be safely compressed according to the current agent state, and demonstrates that it reduces total agent and summarization tokens while maintaining task performance, and achieves a 12.67-fold speedu...
The hidden state immediately before each agent action is studied and it is found that compression and recall needs are already encoded in the model's internal representations, which substantially reduces context consumption while maintaining competitive task performance.
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