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[2602.07883] ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Intrinsic Adaptation
Summary
The article introduces ToolSelf, a novel framework for enhancing agentic systems using Large Language Models (LLMs) by enabling runtime self-reconfiguration and adaptive task execution.
Why It Matters
ToolSelf addresses the limitations of static configurations in AI agents, allowing them to adapt dynamically to changing tasks. This advancement could significantly improve the efficiency and effectiveness of AI applications across various domains, paving the way for more autonomous and intelligent systems.
Key Takeaways
- ToolSelf enables agents to autonomously update their strategies based on task progression.
- The framework integrates task execution and self-adjustment into a unified action space.
- Configuration-Aware Two-stage Training (CAT) enhances the learning process for adaptive capabilities.
- ToolSelf demonstrates a 24.1% average performance gain over traditional methods.
- The approach highlights a shift from external rules to intrinsic parameters in AI systems.
Computer Science > Artificial Intelligence arXiv:2602.07883 (cs) [Submitted on 8 Feb 2026 (v1), last revised 22 Feb 2026 (this version, v2)] Title:ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Intrinsic Adaptation Authors:Jingqi Zhou, Sheng Wang, DeZhao Deng, Junwen Lu, Junwei Su, Qintong Li, Jiahui Gao, Hao Wu, Jiyue Jiang, Lingpeng Kong, Chuan Wu View a PDF of the paper titled ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Intrinsic Adaptation, by Jingqi Zhou and 10 other authors View PDF HTML (experimental) Abstract:Agentic systems powered by Large Language Models (LLMs) have demonstrated remarkable potential in tackling complex, long-horizon tasks. However, their efficacy is fundamentally constrained by static configurations governing agent behaviors, which are fixed prior to execution and fail to adapt to evolving task dynamics. Existing approaches, relying on manual orchestration or heuristic-based patches, often struggle with poor generalization and fragmented optimization. To transcend these limitations, we propose ToolSelf, a novel paradigm enabling tool-driven runtime self-reconfiguration. By abstracting configuration updates as a callable tool, ToolSelf unifies task execution and self-adjustment into a single action space, achieving a phase transition from external rules to intrinsic parameters. Agents can thereby autonomously update their sub-goals and context based on task progression, and correspondingl...
