Agents performing long-horizon tasks need adaptive context management—selectively compressing or discarding information—rather than naively accumulating everything, which improves efficiency and reduces hallucination.
LongSeeker introduces Context-ReAct, a framework that helps AI agents manage growing context during long tasks by selectively compressing, skipping, or deleting information based on relevance. The agent uses five operations to reshape its working memory, reducing costs and errors while maintaining task-critical information.