# The Problem Is the Problem: Towards Scalable Mathematical Discovery

arXiv: https://arxiv.org/abs/2608.16977 · HTML: https://mlsanity.com/paper/2608.16977/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 208 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics](https://mlsanity.com/paper/2109.00110/) (2021)
- [DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition](https://mlsanity.com/paper/2504.21801/) (2025)
- [FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI](https://mlsanity.com/paper/2411.04872/) (2024)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning](https://mlsanity.com/paper/2511.22570/) (2025)
- [STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving](https://mlsanity.com/paper/2502.00212/) (2025)
- [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://mlsanity.com/paper/2604.08224/) (2026)
- [ProgramBench: Can Language Models Rebuild Programs From Scratch?](https://mlsanity.com/paper/2605.03546/) (2026)
- [Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity](https://mlsanity.com/paper/2607.00248/) (2026)
- [Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience](https://mlsanity.com/paper/2512.17260/) (2025)
- [Towards Autonomous Mathematics Research](https://mlsanity.com/paper/2602.10177/) (2026)
- [First Proof](https://mlsanity.com/paper/2602.05192/) (2026)
- [Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers](https://mlsanity.com/paper/2509.06493/) (2025)
- [Automated Conjecture Resolution with Formal Verification](https://mlsanity.com/paper/2604.03789/) (2026)
- [Advancing Mathematics Research with AI-Driven Formal Proof Search](https://mlsanity.com/paper/2605.22763/) (2026)
- [Neural Re-ranking in Multi-stage Recommender Systems: A Review](https://mlsanity.com/paper/2202.06602/) (2022)
- [AI co-mathematician: Accelerating mathematicians with agentic AI](https://mlsanity.com/paper/2605.06651/) (2026)
- [QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems](https://mlsanity.com/paper/2604.24021/) (2026)
- [Formal Conjectures: An Open and Evolving Benchmark for Verified Discovery in Mathematics](https://mlsanity.com/paper/2605.13171/) (2026)
- [LeanConjecturer: Automatic Generation of Mathematical Conjectures for Theorem Proving](https://mlsanity.com/paper/2506.22005/) (2025)
- [Moonshine: An Autonomous Mathematical Research Agent Centered on Conjecture Generation](https://mlsanity.com/paper/2606.10806/) (2026)
- [Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents](https://mlsanity.com/paper/2605.09915/) (2026)
- [Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering](https://mlsanity.com/paper/2605.15721/) (2026)

## Built on by (most cited first, 2 of 2)

- [EULER: Exploring Underused Links with Evidence-Checked Return for Multi-Agent Mathematical Discovery](https://mlsanity.com/paper/2609.00032/) (2026)
- [LANTERN: Illuminating Hidden Mathematical Knowledge in Language Models](https://mlsanity.com/paper/2609.32264/) (2026)

## Abstract (arXiv)

AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well is therefore central to making AI-assisted mathematical discovery efficient. In most current AI-for-math workflows, human effort is concentrated at the beginning and end, in selecting suitable research problems and later reviewing the resulting artifacts. These two stages are becoming bottlenecks for research-level mathematics. We address them by proposing a new human-AI discovery paradigm. The human input is no longer a single problem selected in advance, but a research direction in which the experts have interest and expertise. The system then searches a broad literature corpus for candidate problems in that direction. Inspired by search and recommender systems, we build Find, Attempt, and Recommend (FAR), a literature-to-review cascade that automates the search for suitable problems and focuses human attention on artifacts that have passed several stages of filtering. In a combinatorics pilot, the pipeline starts from 5,245 combinatorics papers, recovers 6,453 candidate conjectures or open problems, and filters them to 4,717 apparently well-posed and still-open conjectures. Subsequent reasoning and automated triage stages surface 598 potential resolutions and select 77 items for author-team review. Among them, we identify many interesting discoveries, including results on conjectures and questions of Davies--Jenssen--Perkins--Roberts, ErdHos--Straus, Ikenmeyer--Pak--Panova, and Lund--Saraf--Wolf. These results demonstrate the effectiveness of this new mode of human-AI collaboration for mathematical discovery.