VALG: An Agentic System for ML Theory Research
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TL;DR - VALG is an autonomous agentic workflow for formulating and proving machine-learning theory results. It matters because it explicitly distinguishes complete source-aligned theorems from relaxations, conditional findings, special cases, and failed attempts.
- Uses multi-level verification, adaptive problem formulation, and graph-structured proof development.
- Maintains fixed mathematical specifications and typed proof-dependency graphs within each theorem branch.
- Diagnoses failures as derivation, proof-structure, or theorem-formulation problems and routes retries accordingly.
- Across nine COLT 2026 subproblems, two runs finalized source-aligned theorem candidates; seven produced restricted, special-case, or conditional results.
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VALG: An Agentic System for ML Theory Research
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Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - VALG is an autonomous agentic workflow for formulating and proving machine-learning theory results. It matters because it explicitly distinguishes complete source-aligned theorems from relaxations, conditional findings, special cases, and failed attempts.
- Uses multi-level verification, adaptive problem formulation, and graph-structured proof development.
- Maintains fixed mathematical specifications and typed proof-dependency graphs within each theorem branch.
- Diagnoses failures as derivation, proof-structure, or theorem-formulation problems and routes retries accordingly.
- Across nine COLT 2026 subproblems, two runs finalized source-aligned theorem candidates; seven produced restricted, special-case, or conditional results.