InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents

• URL: http://arxiv.org/abs/2601.03204v1
• Date: Tue, 06 Jan 2026 17:35:57 GMT
• Title: InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents
• Authors: Chenglin Yu, Yuchen Wang, Songmiao Wang, Hongxia Yang, Ming Li,
• Abstract summary: InfiAgent keeps the agent's reasoning context strictly bounded regardless of task duration.<n>InfiAgent with a 20B open-source model is competitive with larger proprietary systems.
• Score: 36.740230738304525
• License: http://creativecommons.org/licenses/by/4.0/
• 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 file-centric 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. Github Repo:https://github.com/ChenglinPoly/infiAgent

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