Year: 2026
Type: conference-paper
Abstract: LLM agents can reason and use tools, but they often break down on long-horizon tasks due to unbounded context growth and accumulated errors.Common remedies such as context compression or retrieval-augmented prompting introduce trade-offs between information fidelity and reasoning stability.We present InfiAgent, a general-purpose framework that keeps the agent's reasoning context strictly bounded regardless of task duration by externalizing persistent state into a filecentric state abstraction.At each step, the agent reconstructs context from a workspace state snapshot plus a fixed window of recent actions.Experiments on DeepResearch and an 80-paper literature review task show that, without task-specific fine-tuning, InfiAgent with a 20B open-source model is competitive with larger proprietary systems and maintains substantially higher long-horizon coverage than context-centric baselines.These results support explicit state externalization as a practical foundation for stable long-horizon agents.
Institutions: Hong Kong Polytechnic University, University of Hong Kong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166798138