AlphaApollo: Orchestrating Foundation Models and Professional Tools into
  a Self-Evolving System for Deep Agentic Reasoning
Authors: name: Zhanke Zhou; name: Chentao Cao; name: Xiao Feng; name: Xuan Li; name: Zongze Li; name: Xiangyu Lu; name: Jiangchao Yao; name: Weikai Huang; name: Linrui Xu; name: Tian Cheng; name: Guanyu Jiang; name: Yiming Zheng; name: Brando Miranda; name: Tongliang Liu; name: Sanmi Koyejo; name: Masashi Sugiyama; name: Bo Han
Published: 2025-10-05 15:42:24

Abstract: 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.