长任务乱？HyMem分层隔离上下文，用DeepSeek-V4在GAIA上比最强基线高6.1个百分点。
HyMem提出一种分层上下文管理框架，将智能体上下文按功能划分为规划、执行和推理等独立层。其隔离推理模块在完成复杂子任务时不把中间推理痕迹写入持久规划上下文，内存管理模块则通过结构化摘要保留任务进度。在GAIA和Browsecomp-plus基准上，使用DeepSeek-V4时，HyMem的Pass@1分别达到66.7%和61.3%，超出最强基线6.1和4.7个百分点。HyMem有效控制推理上下文增长，使模型在长时复杂任务中保持准确度。
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 layers. HyMem organizes context by function to separate high-level planning from execution and complex analysis. Its isolated reasoning module handles complex subtasks without adding intermediate reasoning traces to the persistent planning context, while its memory management module preserves task progress across context refreshes through structured summaries. These components reduce redundant context accumulation, retain task-critical information, and support coherent long-horizon reasoning within a limited context window. Experiments on GAIA and Browsecomp-plus show that, with DeepSeek-V4, HyMem achieves average Pass@1 scores of 66.7% and 61.3%, outperforming the strongest baseline by 6.1 and 4.7 percentage points, respectively. Further analysis indicates that HyMem effectively controls the growth of the reasoning context, allowing the model to maintain focus and accuracy across complex, long-horizon tasks.