ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation
arXiv:2602.07883v4 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution. Such rigidity forces a trade-off between domain-specific performance and cross-task generalization: strong priors and compact tool spaces aid specialization but weaken transfer, while task-agnostic workflows and broad action spaces expand coverage but dilute guidance. Existing pre-execution optimization, planner-worker orchestration, and configuration patching fall short of resolving this tension, as they de
The paper addresses a core limitation of current LLM-powered agentic systems, which are constrained by rigid configurations, at a time when autonomous agents are rapidly evolving.
This development could unlock significantly more adaptable and generalized AI agents, moving beyond domain-specific performance towards broader applicability for complex, long-horizon tasks.
The ability for AI agents to dynamically reconfigure their 'tool spaces' during execution removes a major barrier to their autonomous operation and emergent problem-solving capability.
- · AI Agent Developers
- · Automation Software Vendors
- · Cloud AI Platforms
- · Tasks requiring static rule-based automation
- · Domain-specific AI development lacking adaptability
More robust and general-purpose AI agents become commercially viable for a wider range of tasks.
Reduced need for human intervention in complex workflows as agents can adapt to unforeseen challenges.
Acceleration of 'lights-out' operations and fully autonomous decision-making systems across various industries.
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Read at arXiv cs.AI