Resolution Automates Analysis of Analytic Singularities in Statistical Learning Theory
Current overview (not historical)
Researchers from Resolution have automatically formalized the analysis of analytic singularities within statistical learning theory, producing 600,000 lines of Lean code at a token cost of approximately $135,000. This work establishes a new automated pathway for studying the relationship between loss landscapes and generalization. By formalizing the parsing of singularities, the system extends statistical learning theory and aids in understanding model sampling behaviors, aligning with the goals of "patternization" research. The achievement demonstrates the feasibility of automating complex theoretical extensions in machine learning.
Written with AI from the reporting · updated 10/26/2026
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- 1Unclassified fact stationOct 26, 00:00recorded Oct 26, 21:18single source
Math for alignment; ProgramBench; and Mirendil shows what RS
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- Subject：
- Resolution
- Action：
- autoformalized the resolution of singularities
- Object：
- statistical learning theory
- Occurred (fact side)：
- Oct 26, 00:00
- Published (valid side)：
- Oct 26, 20:31
- First seen (known side)：
- Oct 26, 21:18
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- Import AIRelated fact: single sourceResolution autoformalizes resolution of singularities using AI agents
Report first recorded Oct 26, 21:18
AI startup Resolution has autoformalized the resolution of singularities in statistical learning theory, producing 600,000 lines of Lean code. The work, costing an estimated $135,000 in token spend, demonstrates how AI can automate complex mathematical research, potentially enabling a rigorous science of research automation.
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