Researchers discovered that language model agents encode signals for memory management (compression and recall) in their hidden states before taking actions, indicating the models already represent when these operations are needed. They propose PaMER, a framework combining state-guided compression with evidence retrieval that reduces context consumption while maintaining task performance on long-horizon agent benchmarks.