arXiv:2601.01569v4 Announce Type: replace 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
The proliferation of LLMs creates an immediate need to enhance their operational capabilities beyond text-centric limitations, as existing agentic systems struggle with complex, long-horizon tasks.
A shift towards LLMs as 'runtime operators' signifies a crucial step in developing more robust and autonomous AI agents, moving beyond fragile multi-turn dependencies and context drift.
The conventional paradigm of LLM tool use is inverted, making the LLM's text context secondary to its role as an active operator, thereby expanding its capabilities in real-time task execution.
- · AI agent developers
- · Enterprises adopting AI automation
- · Cloud infrastructure providers
- · Legacy automation software vendors
- · Workflow orchestration tools with limited LLM integration
More reliable and capable AI agents become available for complex, extended operations.
Increased adoption of AI agents across industries, leading to greater automation of white-collar tasks.
The development of truly autonomous systems that can manage and execute multi-faceted projects with minimal human oversight.
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Read at arXiv cs.AI