@inproceedings{wu-etal-2026-atlas,
title = "{ATLAS}: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning",
author = "Wu, Jinyang and
Zhai, Guocheng and
Jin, Ruihan and
Yuan, Jiahao and
Shen, Yuhao and
Zhang, Shuai and
Wen, Zhengqi and
Tao, Jianhua",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.867/",
pages = "17503--17535",
ISBN = "979-8-89176-395-1",
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** (**A**daptive **T**ool-**L**LM **A**lignment and **S**ynergistic 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 as well as 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."
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<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** (**A**daptive **T**ool-**L**LM **A**lignment and **S**ynergistic 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 as well as 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.</abstract>
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%0 Conference Proceedings
%T ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning
%A Wu, Jinyang
%A Zhai, Guocheng
%A Jin, Ruihan
%A Yuan, Jiahao
%A Shen, Yuhao
%A Zhang, Shuai
%A Wen, Zhengqi
%A Tao, Jianhua
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wu-etal-2026-atlas
%X 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** (**A**daptive **T**ool-**L**LM **A**lignment and **S**ynergistic 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 as well as 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.
%U https://aclanthology.org/2026.findings-acl.867/
%P 17503-17535
Markdown (Informal)
[ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning](https://aclanthology.org/2026.findings-acl.867/) (Wu et al., Findings 2026)
ACL
- Jinyang Wu, Guocheng Zhai, Ruihan Jin, Jiahao Yuan, Yuhao Shen, Shuai Zhang, Zhengqi Wen, and Jianhua Tao. 2026. ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17503–17535, San Diego, California, United States. Association for Computational Linguistics.