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[2601.01569] CaveAgent: Transforming LLMs into Stateful Runtime Operators
Summary
CaveAgent introduces a novel framework that transforms LLMs into stateful runtime operators, enhancing their ability to manage complex tasks and reduce context drift in multi-turn interactions.
Why It Matters
This research addresses the limitations of current LLM-based agents by providing a solution that improves task execution and memory management. By shifting the focus from text generation to a stateful runtime, CaveAgent enhances the efficiency and effectiveness of AI applications, making it relevant for developers and researchers in AI.
Key Takeaways
- CaveAgent shifts LLMs from text-centric to stateful runtime operators.
- The framework reduces context drift and improves memory management in multi-turn tasks.
- It enables complex task execution through persistent Python objects.
- CaveAgent integrates a skill management system for enhanced interoperability.
- The approach supports automated evaluation and reinforcement learning without human annotation.
Computer Science > Artificial Intelligence arXiv:2601.01569 (cs) [Submitted on 4 Jan 2026 (v1), last revised 18 Feb 2026 (this version, v2)] Title:CaveAgent: Transforming LLMs into Stateful Runtime Operators Authors:Maohao Ran, Zhenglin Wan, Cooper Lin, Yanting Zhang, Hongyu Xin, Hongwei Fan, Yibo Xu, Beier Luo, Yaxin Zhou, Wangbo Zhao, Lijie Yang, Lang Feng, Fuchao Yang, Jingxuan Wu, Yiqiao Huang, Chendong Ma, Dailing Jiang, Jianbo Deng, Sihui Han, Yang You, Bo An, Yike Guo, Jun Song View a PDF of the paper titled CaveAgent: Transforming LLMs into Stateful Runtime Operators, by Maohao Ran and 22 other authors View PDF HTML (experimental) Abstract:LLM-based agents are increasingly capable of complex task execution, yet current agentic systems remain constrained by text-centric paradigms that struggle with long-horizon tasks due to fragile multi-turn dependencies and context drift. We present CaveAgent, a framework that shifts tool use from ``LLM-as-Text-Generator'' to ``LLM-as-Runtime-Operator.'' CaveAgent introduces a dual-stream architecture that inverts the conventional paradigm: rather than treating the LLM's text context as the primary workspace with tools as auxiliary, CaveAgent elevates the persistent Python runtime as the central locus of state, with a lightweight semantic stream serving as its orchestrator. Beyond leveraging code generation to resolve interdependent sub-tasks (e.g., loops, conditionals) in a single step, CaveAgent introduces \textit{Stateful Runti...