Papers by Songmiao Wang

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Challenge:	Existing LLMs break down on long-horizon tasks due to unbounded context growth and accumulated errors.
Approach:	They propose a framework that externalizes persistent state into a file-centric state abstraction and keeps the agent’s reasoning context strictly bounded regardless of task duration.
Outcome:	Experiments on DeepResearch and an 80-paper literature review show that the proposed framework maintains higher long-horizon coverage than baseline models without task-specific fine-tuning.