AlphaApollo: Orchestrating Foundation Models and Professional Tools into
  a Self-Evolving System for Deep Agentic Reasoning
We present AlphaApollo, a self-evolving agentic reasoning system that aims to
address two bottlenecks in foundation model (FM) reasoning-limited
model-intrinsic capacity and unreliable test-time iteration. AlphaApollo
orchestrates multiple models with professional tools to enable deliberate,
verifiable reasoning. It couples (i) a computation tool (Python with numerical
and symbolic libraries) and (ii) a retrieval tool (task-relevant external
information) to execute exact calculations and ground decisions. The system
further supports multi-round, multi-model solution evolution via a shared state
map that records candidates, executable checks, and feedback for iterative
refinement. In evaluations on AIME 2024/2025 across multiple models,
AlphaApollo delivers consistent gains: +5.15% Average@32 and +23.34% Pass@32
for Qwen2.5-14B-Instruct, and +8.91% Average@32 with +26.67% Pass@32 for
Llama-3.3-70B-Instruct. Tool-use analysis shows that more than 80% of tool
calls are successfully executed, with consistent outperformance of non-tool
baselines, thereby lifting the capability ceiling of FMs. More empirical
results and implementation details will be updated at
https://github.com/tmlr-group/AlphaApollo.
68e722727ae125f9582e69c6
Zhanke Zhou
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Chentao Cao
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Xiao Feng
68e722727ae125f9582e69c9
Xuan Li
68e722727ae125f9582e69ca
Zongze Li
68e722727ae125f9582e69cb
Xiangyu Lu
68e722727ae125f9582e69cc
Jiangchao Yao
68e722727ae125f9582e69cd
Weikai Huang
68e722727ae125f9582e69ce
Linrui Xu
68e722727ae125f9582e69cf
Tian Cheng
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Guanyu Jiang
claimed_verified
2025-10-13T10:10:18.158Z
66838dcee365c0f666693d6f
/avatars/13f845f2efdfd8636dd8684648bfb068.svg
GUANYU JIANG
user
DjangoJungle
68e722727ae125f9582e69d1
Yiming Zheng
68e722727ae125f9582e69d2
Brando Miranda
68e722727ae125f9582e69d3
Tongliang Liu
68e722727ae125f9582e69d4
Sanmi Koyejo
68e722727ae125f9582e69d5
Masashi Sugiyama
68e722727ae125f9582e69d6
Bo Han
AlphaApollo: Orchestrating Foundation Models and Professional Tools into
  a Self-Evolving System for Deep Agentic Reasoning
We present AlphaApollo, a self-evolving agentic reasoning system that aims to
address two bottlenecks in foundation model (FM) reasoning-limited
model-intrinsic capacity and unreliable test-time iteration. AlphaApollo
orchestrates multiple models with professional tools to enable deliberate,
verifiable reasoning. It couples (i) a computation tool (Python with numerical
and symbolic libraries) and (ii) a retrieval tool (task-relevant external
information) to execute exact calculations and ground decisions. The system
further supports multi-round, multi-model solution evolution via a shared state
map that records candidates, executable checks, and feedback for iterative
refinement. In evaluations on AIME 2024/2025 across multiple models,
AlphaApollo delivers consistent gains: +5.15% Average@32 and +23.34% Pass@32
for Qwen2.5-14B-Instruct, and +8.91% Average@32 with +26.67% Pass@32 for
Llama-3.3-70B-Instruct. Tool-use analysis shows that more than 80% of tool
calls are successfully executed, with consistent outperformance of non-tool
baselines, thereby lifting the capability ceiling of FMs. More empirical
results and implementation details will be updated at
https://github.com/tmlr-group/AlphaApollo.
AlphaApollo, a self-evolving reasoning system, enhances foundation model performance through tool integration and iterative refinement, achieving significant improvements in accuracy and pass rates.
self-evolving agentic reasoning system foundation model model-intrinsic capacity test-time iteration computation tool retrieval tool shared state map iterative refinement AIME Qwen2.5-14B-Instruct Llama-3.3-70B-Instruct tool-use analysis