ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents
Large language model (LLM) agents often struggle in long-context interactions. As the history grows, sliding windows and prompt compression may drop earlier information that later steps rely on. Retrieval-based memory systems surface relevant content but still overlook the causal and logical dependencies between steps. We introduce ContextWeaver, a selective and dependency-structured memory framework that organizes an agent's interaction trace into a graph of reasoning steps and selects the relevant context for future actions. ContextWeaver supports (1) dependency-based construction and traversal that link each step to the earlier steps it relies on; (2) compact dependency summaries of th...
| Authors | Yating Wu, Yuhao Zhang, Sayan Ghosh et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 14 October 2026 |
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