HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As interactions accumulate, detailed execution traces and intermediate outputs dominate the context, making it difficult for the model to retain and use high-level planning information. Most existing methods address this issue through compression or retrieval applied to a single, flat context, which does not clearly separate different types of context information and often leads to degraded reasoning. To address this challenge, we propose HyMem, a hierarchical framework that explicitly separates the agent's context into distinct functional l...
| Authors | XinQi Wang, Jinwei Xiao, Sijia Cui et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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