LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents
Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can overwhelm the agent, increasing costs and the risk of errors. We propose that effective context management should be adaptive: parts of the agent's trajectory are maintained at different levels of detail depending on their current relevance to the task. To operationalize this principle, we introduce Context-ReAct, a general agentic paradigm for elastic context orchestration that integrates reasoning, context management, and tool use in a unified loop. Context-ReAct provides five atomic operations: Skip, Compress, Ro...
| Authors | Yijun Lu, Rui Ye, Yuwen Du et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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