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 interme- diate 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 rel- evance 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, Rollback, Snippet and Delete, which allow the agent to dynamically reshape its working context, preserving important evidence, summarizing resolved information, discarding unhelpful branches, and controlling context size. Together, these operations form a fine-grained action space spanning abstractive compression, exact extraction, deletion, structural rollback, and no modification. Building on this paradigm, we develop LongSeeker, a long-horizon search agent fine-tuned from Qwen3-30B-A3B on 10k synthesized trajectories. Across four representative search benchmarks, LongSeeker achieves 61.5% on BrowseComp and 62.5% on BrowseComp-ZH, substantially outperforming Tongyi DeepResearch (43.4% and 46.7%) and AgentFold (36.2% and 47.3%). These re- sults highlight the potential of adaptive context management, showing that agents can achieve more reliable and efficient long-horizon reasoning by actively shaping their working memory.