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| affiliations: | ||
| 1. Department of Computer Science & Technology, University of Cambridge | ||
| link: https://arxiv.org/abs/2603.04528 | ||
| abstract: Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model for computational mathematical discovery based on this observation. Our system, conceived with research in mind, poses its own conjectures and then attempts to prove them, making decisions informed by this feedback and an evolving data distribution. Inspired by the history of Euler's conjecture for polyhedra and an open challenge in the literature, we benchmark with the task of autonomously recovering the concept of homology from polyhedral data and knowledge of linear algebra. Our system completes this learning problem. Most importantly, the experiments are ablations, statistically testing the value of the complete dynamic and controlling for experimental setup. They support our main claim: that the optimisation of the right combination of local processes can lead to surprisingly well-aligned notions of mathematical interestingness. |
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This abstract value is unquoted and contains : (main claim: that), which makes the YAML invalid for standard parsers; tooling that loads docs/papers.yml (for example docs/generate_papers.py) will fail before generating the showcase. This blocks docs pipelines whenever they parse this file.
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| - Carl Henrik Ek (1) | ||
| - Challenger Mishra (1) | ||
| affiliations: | ||
| 1. Department of Computer Science & Technology, University of Cambridge |
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The affiliation line is written as plain text (1. ...) rather than a mapping entry (1: ...), so affiliations is not the dict shape expected by docs/generate_papers.py (it calls paper["affiliations"].items()). Once YAML parsing succeeds, this entry will still crash page generation for this paper.
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| 1. Department of Computer Science & Technology, University of Cambridge | ||
| link: https://arxiv.org/abs/2603.04528 | ||
| abstract: Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model for computational mathematical discovery based on this observation. Our system, conceived with research in mind, poses its own conjectures and then attempts to prove them, making decisions informed by this feedback and an evolving data distribution. Inspired by the history of Euler's conjecture for polyhedra and an open challenge in the literature, we benchmark with the task of autonomously recovering the concept of homology from polyhedral data and knowledge of linear algebra. Our system completes this learning problem. Most importantly, the experiments are ablations, statistically testing the value of the complete dynamic and controlling for experimental setup. They support our main claim: that the optimisation of the right combination of local processes can lead to surprisingly well-aligned notions of mathematical interestingness. | ||
| image: |
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Leaving image: empty makes it null, but the repo validator (scripts/validate_papers_yml.py) requires a non-empty image string and the docs generator later calls .startswith on this field. In CI contexts that run validation/build, this new entry will fail checks or crash generation.
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Adding a paper to the PySR showcase