Study Claims Fully Autonomous Agents Outperform Scaffolding in Math Discovery
rohanpaul_ai · x · 2026-08-27
Research from Cambridge, HKU, and others challenges the industry consensus of using rigid scaffolding to ensure model reliability. The paper argues that modern models are capable enough to act as researchers rather than just components, and that excessive scaffolding may cost more than it buys.
- Key Finding: Giving agents full autonomy to choose directions yields mathematical proofs and generalizations not asked for by evaluators. Gains come from agents spending budget on structure rather than search.
- Comparison: Unlike AlphaEvolve, which uses the model as a mutation operator within human-built machinery, this approach advocates for more open-ended exploration.
- Implications: This method excels when theorems can prune the search space, though it may be less effective where the best construction is irregular.
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