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Talent Scout

W

Weida Liang

National University of Singapore

Y

Yiyou Sun

University of California, Berkeley

S

Shuyuan Nan

National University of Singapore

C

Chuang Li

National University of Singapore

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References (44)

[1]
Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs
2025Shihao Qi, Jie Ma et al.
[2]
MathArena: Evaluating LLMs on Uncontaminated Math Competitions
2025Mislav Balunovi'c, Jasper Dekoninck et al.
[3]
What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning
2025Gangwei Jiang, Yahui Liu et al.
[4]
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets
2025A. Younsi, Abdalgader Abubaker et al.
[5]
Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics
2025Hamed Mahdavi, Alireza Hashemi et al.
[6]
Self-Training Elicits Concise Reasoning in Large Language Models
2025Tergel Munkhbat, Namgyu Ho et al.
[7]
Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving
2025Xin Xu, Yan Xu et al.
[8]
When More is Less: Understanding Chain-of-Thought Length in LLMs
2025Yuyang Wu, Yifei Wang et al.
[9]
Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs
2025Sagnik Mukherjee, Abhinav Chinta et al.
[10]
Coarse-to-Fine Process Reward Modeling for Mathematical Reasoning
2025Yulan Hu, Ouyang Sheng et al.
[11]
Zero-Shot Verification-guided Chain of Thoughts
2025Jishnu Ray Chowdhury, Cornelia Caragea
[12]
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
2025Yiyao Yu, Yuxiang Zhang et al.
[13]
BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
2025Beichen Zhang, Yuhong Liu et al.
[14]
HARP: A challenging human-annotated math reasoning benchmark
2024Albert S. Yue, Lovish Madaan et al.
[15]
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
2024L. Ruis, Maximilian Mozes et al.
[16]
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning
2024Lang Cao, Chao Peng et al.
[17]
Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
2024Bofei Gao, Feifan Song et al.
[18]
Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement
2024Wenda Xu, Guanglei Zhu et al.
[19]
Are self-explanations from Large Language Models faithful?
2024Andreas Madsen, Sarath Chandar et al.
[20]
Solving olympiad geometry without human demonstrations
2024Trieu H. Trinh, Yuhuai Wu et al.

Showing 20 of 44 references

Founder's Pitch

"Selective Strategy Retrieval enhances mathematical reasoning in AI with tailored strategy combination for improved performance."

Mathematical ReasoningScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

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Why It Matters

This research actively closes the gap in AI and human capabilities in mathematical reasoning by enhancing model guidance effectiveness through tailored strategy combinations. It also offers empirically validated methods to consistently improve model performance on complex reasoning tasks.

Product Angle

The SSR framework can be productized into a SaaS offering aimed at educational platforms, providing advanced AI-guided strategies for math problems, thus enhancing human learning via model-based insights.

Disruption

The SSR method could replace traditional teaching aids by providing more dynamic, adaptable, and correct strategy-based guidance for math problem solving, thus making legacy products less relevant.

Product Opportunity

The commercial potential lies in educational technology, particularly for online learning and tutoring platforms targeting K-12 and college math students, where consistent improvement in solution accuracy could drive significant adoption.

Use Case Idea

Develop a tutoring tool for advanced math students that employs SSR to present the most effective problem-solving strategies, enhancing learning through AI-guided solutions tailored for individual comprehension.

Science

The paper identifies a gap between strategy usage and executability in AI-driven math reasoning, proposing Selective Strategy Retrieval (SSR). SSR combines human and model strategies, selectively retrieved based on empirical executability signals, significantly boosting performance on benchmark tests like AIME25 and Apex.

Method & Eval

The method, SSR, was tested on mathematical reasoning benchmarks where it showed significant accuracy improvements, up to +13 points on AIME25 and +5 points on Apex, indicating robust performance across different model sizes.

Caveats

Potential caveats include the reliance on high-quality paired datasets (human-model), scalability across diverse domains, and adaptability to non-mathematical problems. Moreover, effectiveness in real-world educational settings needs further exploration.

Author Intelligence

Weida Liang

National University of Singapore
weidaliang@nus.edu.sg

Yiyou Sun

University of California, Berkeley

Shuyuan Nan

National University of Singapore

Chuang Li

National University of Singapore

Dawn Song

University of California, Berkeley

Kenji Kawaguchi

National University of Singapore