Let the Abyss Stare Back Adaptive Falsification for Autonomous Scientific Discovery
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911556143415296 |
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| author | Li, Peiran Lin, Fangzhou Xing, Shuo Sun, Jiashuo Zhang, Dylan Yang, Siyuan Ni, Chaoqun Tu, Zhengzhong |
| author_facet | Li, Peiran Lin, Fangzhou Xing, Shuo Sun, Jiashuo Zhang, Dylan Yang, Siyuan Ni, Chaoqun Tu, Zhengzhong |
| contents | Autonomous scientific discovery is entering a more dangerous regime: once the evaluator is frozen, a sufficiently strong search process can learn to win the exam without learning the mechanism the task was meant to reveal. This is the idea behind our title. To let the abyss stare back is to make evaluation actively push against the candidate through adaptive falsification, rather than passively certify it through static validation. We introduce DASES, a falsification-driven framework in which an Innovator, an Abyss Falsifier, and a Mechanistic Causal Extractor co-evolve executable scientific artifacts and scientifically admissible counterexample environments under a fixed scientific contract. In a controlled loss-discovery problem with a single editable locus, DASES rejects artifacts that static validation would have accepted, identifies the first candidate that survives the admissible falsification frontier, and discovers FNG-CE, a loss that transfers beyond the synthetic discovery environment and consistently outperforms CE and CE+L2 under controlled comparisons across standard benchmarks, including ImageNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29045 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Let the Abyss Stare Back Adaptive Falsification for Autonomous Scientific Discovery Li, Peiran Lin, Fangzhou Xing, Shuo Sun, Jiashuo Zhang, Dylan Yang, Siyuan Ni, Chaoqun Tu, Zhengzhong Computer Vision and Pattern Recognition Autonomous scientific discovery is entering a more dangerous regime: once the evaluator is frozen, a sufficiently strong search process can learn to win the exam without learning the mechanism the task was meant to reveal. This is the idea behind our title. To let the abyss stare back is to make evaluation actively push against the candidate through adaptive falsification, rather than passively certify it through static validation. We introduce DASES, a falsification-driven framework in which an Innovator, an Abyss Falsifier, and a Mechanistic Causal Extractor co-evolve executable scientific artifacts and scientifically admissible counterexample environments under a fixed scientific contract. In a controlled loss-discovery problem with a single editable locus, DASES rejects artifacts that static validation would have accepted, identifies the first candidate that survives the admissible falsification frontier, and discovers FNG-CE, a loss that transfers beyond the synthetic discovery environment and consistently outperforms CE and CE+L2 under controlled comparisons across standard benchmarks, including ImageNet. |
| title | Let the Abyss Stare Back Adaptive Falsification for Autonomous Scientific Discovery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.29045 |