ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System

arXiv:2607.14178 · cs.AI, cs.MA · Submitted 2026-08-19 · Read on arXiv

Yutong He, Daibo Li, Guohong Li, Jiahe Geng, Zhengyang Huang, Can Ren, Zekun Zhang, Yifan Liu, Shuchen Zhu, Hengrui Zhang, Boao Kong, Ming Sun, Shu Li, Chenyi Li, Jiang Hu, Kun Yuan, Zaiwen Wen, Pingwen Zhang

cs.AI, cs.MA

Submitted: 2026-08-19

Updated: 2026-08-20

Code: https://github.com/reaslab/ReasFlow

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 67× in this paper · explained in 1 episode
• Federated Learning — 61× in this paper · explained in 14 episodes
• Core — 53× in this paper · explained in 2 episodes
• Stiefel manifold — 41× in this paper · explained in 2 episodes
• Verification — 38× in this paper · explained in 2 episodes
• Robustness — 36× in this paper · explained in 5 episodes
• Consistency — 35× in this paper · explained in 3 episodes
• Benchmark — 31× in this paper · explained in 2 episodes
• Ablation — 29× in this paper · explained in 1 episode
• Data Heterogeneity — 22× in this paper · explained in 3 episodes
• Alignment — 20× in this paper · explained in 1 episode
• Fidelity — 19× in this paper · explained in 2 episodes
• Hallucination — 18× in this paper · explained in 3 episodes
• Convergence Guarantees — 17× in this paper · explained in 3 episodes
• Spectral Gap — 13× in this paper · explained in 5 episodes
• Quantization — 12× in this paper · explained in 12 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Aggregation — 9× in this paper · explained in 1 episode
• Ablation Studies — 8× in this paper · explained in 1 episode
• Multi-Agent System — 8× in this paper · explained in 6 episodes
• Trace — 7× in this paper · explained in 2 episodes
• AI Agents — 6× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 6× in this paper · explained in 3 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Matrix Completion — 5× in this paper · explained in 3 episodes
• Non-IID Data — 5× in this paper · explained in 2 episodes
• Perplexity — 5× in this paper · explained in 3 episodes
• Ambient Dimension — 4× in this paper · explained in 1 episode
• Decentralized Learning — 4× in this paper · explained in 2 episodes
• Evaluation Dimensions — 4× in this paper · explained in 3 episodes
• Formal Verification — 4× in this paper · explained in 2 episodes
• Human-in-the-Loop — 4× in this paper · explained in 3 episodes
• Lyapunov function — 4× in this paper · explained in 2 episodes
• Objective Function — 4× in this paper · explained in 2 episodes
• Overall Balanced — 4× in this paper
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Paper Review — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Verifier — 3× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Local Optimization — 2× in this paper · explained in 2 episodes
• Modularity — 2× in this paper · explained in 9 episodes
• Overall Technical Focus — 2× in this paper
• Personalization — 2× in this paper · explained in 2 episodes
• Procedural Knowledge — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Riemannian optimization — 2× in this paper · explained in 2 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Approximation Bound — 1× in this paper · explained in 1 episode
• Associative Memory — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Formal Methods — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• In International Conference on Learning Representations — 1× in this paper
• International Mathematical Olympiad — 1× in this paper
• Meta-learning — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• First Proof
• QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems
• OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
• Early science acceleration experiments with GPT-5
• SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?
• Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience
• Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
• PRE: A Peer Review Based Large Language Model Evaluator
• Towards Autonomous Mathematics Research
• ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews
• Accelerating scientific discovery with Co-Scientist
• DeMuon: A Decentralized Muon for Matrix Optimization over Graphs
• FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
• Winning Gold at IMO 2025 with a Model-Agnostic Verification-and-Refinement Pipeline
• Matlas: A Semantic Search Engine for Mathematics
• Automated Conjecture Resolution with Formal Verification
• A Human-AI Collaborative Workflow for Mathematical Discovery: A Case Study in Grover-Compatible Riemannian Optimization
• A Retraction-Free EXTRA Method for Decentralized Optimization on the Stiefel Manifold
• LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
• SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning

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