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

Source: arXiv - 2601.03204v1

Overview

The paper introduces InfiAgent, a new framework that lets large‑language‑model (LLM) agents tackle tasks that stretch over many steps without blowing up their internal context window. By moving the agent’s “memory” out of the prompt and into a lightweight, file‑based state store, InfiAgent keeps the prompt size constant while still preserving everything the agent has learned so far. The authors demonstrate that even a 20 B open‑source model can rival much larger proprietary systems on long‑running research‑assistant tasks.

Key Contributions

• State externalization: A file‑centric abstraction that stores the agent’s persistent state outside the LLM prompt, guaranteeing a bounded context regardless of task length.
• Workspace snapshot + sliding window: At each reasoning step the model receives (1) a concise snapshot of the current workspace state and (2) a fixed‑size window of the most recent actions, enabling stable reasoning without context overflow.
• Task‑agnostic design: No task‑specific fine‑tuning is required; the same framework works for diverse long‑horizon problems such as literature reviews and multi‑step research pipelines.
• Empirical validation: Benchmarks on the DeepResearch suite and an 80‑paper literature‑review benchmark show competitive performance against larger, closed‑source agents while maintaining far higher coverage on long‑horizon tasks.
• Open‑source release: Full implementation, prompts, and evaluation scripts are provided on GitHub, encouraging community extensions.

Methodology

1. State Representation – The agent’s knowledge (e.g., collected facts, intermediate results, tool outputs) is serialized into a set of JSON/YAML files that constitute the workspace.
2. Snapshot Generation – Before each LLM call, the system creates a snapshot: a distilled view of the workspace (e.g., key variables, summary of past actions). This snapshot is deliberately small (a few hundred tokens).
3. Action Window – The most recent k actions (default = 5) are appended to the prompt, giving the model short‑term context for continuity.
4. LLM Invocation – The prompt consists of: system instructions, the snapshot, the action window, and a task‑specific query. The LLM generates the next action (e.g., “run tool X”, “store Y”, “ask clarification”).
5. State Update – The chosen action updates the workspace files, and the loop repeats. Because the workspace lives on disk, its size can grow arbitrarily without affecting the prompt length.

The approach is deliberately simple: it relies on standard file I/O and does not require custom neural memory modules, making it easy to plug into existing LLM‑as‑a‑service pipelines.

Results & Findings

Key observations

• Stable long‑horizon behavior: InfiAgent maintains >90 % success up to 50 reasoning steps, whereas context‑only baselines drop sharply after ~15 steps.
• No fine‑tuning needed: The same prompt template works across both benchmarks, confirming the generality of the state‑externalization idea.
• Resource efficiency: By keeping the prompt under 2 k tokens, inference latency stays comparable to baseline models, despite the extra file I/O.

Practical Implications

• Scalable autonomous assistants: Developers can build agents that manage complex workflows—e.g., multi‑stage data pipelines, continuous code refactoring, or long‑form content generation—without worrying about prompt overflow.
• Tool‑rich integrations: Because the state lives on disk, agents can easily read/write to databases, version‑control systems, or external APIs, making the framework a natural fit for DevOps automation or research assistants.
• Cost‑effective deployment: Using a modest 20 B open‑source model yields performance on par with much larger proprietary offerings, lowering compute budgets for startups and internal tooling teams.
• Simplified debugging & auditability: The workspace files provide a transparent log of every intermediate result, enabling developers to inspect, replay, or roll back an agent’s reasoning steps.

Limitations & Future Work

• State design overhead: Crafting an effective snapshot (what to include, how to summarize) still requires domain knowledge; a poorly designed snapshot can degrade performance.
• File‑system latency: For extremely high‑frequency loops, disk I/O may become a bottleneck; future work could explore in‑memory caches or vector‑store backends.
• Error propagation: While the framework mitigates context loss, logical errors made early in the workflow still propagate; integrating verification or self‑correction modules is an open direction.
• Scalability to truly massive state: The current prototype assumes the workspace fits on a single machine; distributed state stores (e.g., cloud object storage) would be needed for enterprise‑scale agents.

InfiAgent shows that a clean separation between “thinking” (the LLM prompt) and “remembering” (external state) can unlock stable, long‑running autonomous agents using today’s open‑source models. For developers looking to embed LLMs into complex pipelines, the framework offers a pragmatic, low‑cost path forward.

Authors

• Chenglin Yu
• Yuchen Wang
• Songmiao Wang
• Hongxia Yang
• Ming Li

Paper Information

• arXiv ID: 2601.03204v1
• Categories: cs.AI, cs.MA
• Published: January 6, 2026
• PDF: Download PDF