arXiv:2601.03872v2 Announce Type: replace Abstract: The integration of large language models (LLMs) with external tools has significantly expanded the capabilities of AI agents. However, as the diversity of both LLMs and tools increases, selecting the optimal model-tool combination becomes a high-dimensional optimization challenge. Existing approaches often rely on a single model or fixed tool-calling logic, failing to exploit the performance variations across heterogeneous model-tool pairs. In this paper, we present ATLAS (Adaptive Tool-LLM Alignment and Synergistic Invocation), a dual-path f
The proliferation of various LLMs and specialized tools necessitates advanced orchestration to unlock their full potential, moving beyond siloed applications.
This development addresses a key bottleneck in AI agent capabilities, enabling more sophisticated and flexible multi-tool, multi-model reasoning for complex tasks.
The ability to dynamically select and align the best LLM and tool combination for specific sub-problems transforms how AI agents approach diverse and intricate challenges.
- · AI agent developers
- · Enterprises adopting AI agents
- · Foundation model providers
- · Tool developers
- · Monolithic AI solutions
- · Fixed-logic tool chains
AI agents become significantly more capable across a wider range of domains by adaptively using optimal models and tools.
This capability accelerates the automation of complex workflows currently requiring human intervention, particularly in white-collar sectors.
Increased reliance on dynamic agent orchestration could lead to new security and explainability challenges in complex, multi-model systems.
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