Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning
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 framework for dynamic tool usage in cross-domain complex reasoning. ATLAS operates via a dual-path approach: (1) training-free cluster-based routing that exploits empirical priors for domain-specific alignment, and (2) RL-based multi-step routing that explores autonomous trajectories for out-of-distribution generalization. Extensive experiments across 15 benchmarks demonstrate that our method outperforms closed-source models like GPT-4o, surpassing existing routing methods on both in-distribution (+10.1%) and out-of-distribution (+13.1%) tasks. Furthermore, our framework shows significant gains in visual reasoning by orchestrating specialized multi-modal tools.
695f1a475fa3847525c41d06
Jinyang Wu
claimed_verified
2026-01-08T08:32:36.055Z
6747de57f8cab58c22ec94a2
/avatars/5bae0341862fac24564781c0fa32aac5.svg
Jinyang Wu
Jinyang23
user
Jinyang23
695f1a475fa3847525c41d07
Guocheng Zhai
695f1a475fa3847525c41d08
Ruihan Jin
695f1a475fa3847525c41d09
Jiahao Yuan
695f1a475fa3847525c41d0a
Yuhao Shen
695f1a475fa3847525c41d0b
Shuai Zhang
695f1a475fa3847525c41d0c
Zhengqi Wen
695f1a475fa3847525c41d0d
Jianhua Tao
Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning
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 framework for dynamic tool usage in cross-domain complex reasoning. ATLAS operates via a dual-path approach: (1) training-free cluster-based routing that exploits empirical priors for domain-specific alignment, and (2) RL-based multi-step routing that explores autonomous trajectories for out-of-distribution generalization. Extensive experiments across 15 benchmarks demonstrate that our method outperforms closed-source models like GPT-4o, surpassing existing routing methods on both in-distribution (+10.1%) and out-of-distribution (+13.1%) tasks. Furthermore, our framework shows significant gains in visual reasoning by orchestrating specialized multi-modal tools.
ATLAS is a dual-path framework that dynamically selects optimal model-tool combinations for cross-domain reasoning through cluster-based routing and reinforcement learning-based multi-step routing, achieving superior performance on complex reasoning tasks.
large language models external tools model-tool combination high-dimensional optimization dual-path framework training-free cluster-based routing RL-based multi-step routing cross-domain complex reasoning domain-specific alignment out-of-distribution generalization