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University of Cambridge / Hacker News

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2026-08-17

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2026-08-17 / HACKER NEWSCambridge's 'Red Queen Gödel Machine' Co-Evolves the Evaluator With the Agent, Reporting 1.78–1.86x Higher Paper AcceptanceCambridge's Machine Learning Systems Lab (Alex Iacob under Prof. Nic Lane, with NVIDIA, Flower Labs, MBZUAI, and Inria) is attacking the self-improvement plateau by making the grader improve too: 'The test does not merely measure progress, it defines it, so the efficacy of the test becomes a ceiling the agent cannot climb past.' The method alternates fixed-evaluator phases with checkpoints that swap in stronger evaluators, reporting 1.78–1.86x higher acceptance rates on scientific paper writing, 9% better grading accuracy, and ~13x lower search token cost via a hybrid open/proprietary model split. Freshness caveat: the arXiv paper is dated July 21, 2026, so this is the Cambridge press write-up reaching HN (78 points) rather than a new result.

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