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

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.