A^2FM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid
  Reasoning
Large language models split into two families: reasoning-centric LLMs, which
strengthen internal chain-of-thought reasoning but cannot invoke external
tools, and agentic LLMs, which learn to interact with environments and leverage
tools but often lag in deep reasoning. This divide arises from fundamentally
different training objectives, leading to mismatched strengths and inefficiency
on simple queries, where both families tend to overthink or over-call tools. In
this work, we present Adaptive Agent Foundation Model (A^2FM), a unified
framework that follows a route-then-align principle: the model first learns
task-aware routing and then aligns mode-specific trajectories under a shared
backbone. To address the inefficiency gap, we introduce a third
mode-instant-that handles simple queries directly, preventing unnecessary
reasoning or tool calls while complementing the agentic and reasoning modes. To
jointly enhance accuracy and efficiency, we propose Adaptive Policy
Optimization (APO), which enforces adaptive sampling across modes and applies a
cost-regularized reward. On the 32B scale, A^2FM achieves 13.4% on
BrowseComp, 70.4% on AIME25, and 16.7% on HLE, setting new SOTA among
comparable models and performing competitively with frontier LLMs across
agentic, reasoning, and general benchmarks. Notably, the adaptive execution
achieves a cost of pass of only $0.00487 per correct answer-cutting cost by
45.2% relative to reasoning and 33.5% relative to agentic, thus delivering
substantially higher cost efficiency while maintaining comparable accuracy.
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Qianben Chen
68f5c0698589920bf4d321ac
Jingyi Cao
68f5c0698589920bf4d321ad
Jiayu Zhang
68f5c0698589920bf4d321ae
Tianrui Qin
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tianrui
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tianyue818
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Xiaowan Li
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King Zhu
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2025-10-20T12:09:56.809Z
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king zhu
user
kangz
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Dingfeng Shi
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He Zhu
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Minghao Liu
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2025-10-24T16:14:09.023Z
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minghao
user
Liam-Liu
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Xiaobo Liang
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Xin Gui
68f5c0698589920bf4d321b6
Ge Zhang
68f5c0698589920bf4d321b7
Jian Yang
68f5c0698589920bf4d321b8
Yuchen Eleanor Jiang
68f5c0698589920bf4d321b9
Wangchunshu Zhou
A^2FM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid
  Reasoning
Large language models split into two families: reasoning-centric LLMs, which
strengthen internal chain-of-thought reasoning but cannot invoke external
tools, and agentic LLMs, which learn to interact with environments and leverage
tools but often lag in deep reasoning. This divide arises from fundamentally
different training objectives, leading to mismatched strengths and inefficiency
on simple queries, where both families tend to overthink or over-call tools. In
this work, we present Adaptive Agent Foundation Model (A^2FM), a unified
framework that follows a route-then-align principle: the model first learns
task-aware routing and then aligns mode-specific trajectories under a shared
backbone. To address the inefficiency gap, we introduce a third
mode-instant-that handles simple queries directly, preventing unnecessary
reasoning or tool calls while complementing the agentic and reasoning modes. To
jointly enhance accuracy and efficiency, we propose Adaptive Policy
Optimization (APO), which enforces adaptive sampling across modes and applies a
cost-regularized reward. On the 32B scale, A^2FM achieves 13.4% on
BrowseComp, 70.4% on AIME25, and 16.7% on HLE, setting new SOTA among
comparable models and performing competitively with frontier LLMs across
agentic, reasoning, and general benchmarks. Notably, the adaptive execution
achieves a cost of pass of only $0.00487 per correct answer-cutting cost by
45.2% relative to reasoning and 33.5% relative to agentic, thus delivering
substantially higher cost efficiency while maintaining comparable accuracy.
A unified framework, A$^2$FM, combines reasoning and agentic capabilities in large language models, improving efficiency and accuracy across benchmarks by adaptively routing queries and optimizing policy.
reasoning-centric LLMs agentic LLMs task-aware routing mode-specific trajectories shared backbone Adaptive Policy Optimization APO cost-regularized reward BrowseComp AIME25 HLE SOTA adaptive execution