| { |
| "schema_version": 1, |
| "title": "Repro: A Theoretical Framework for Modular Learning of Robust Generative Models", |
| "emoji": "jigsaw", |
| "space_id": "snaykey/repro-modular-robust-gen", |
| "paper": { |
| "openreview_id": "ToCArdndt6" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-ToCArdndt6" |
| ], |
| "updated_at": "2026-07-26T00:00:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Repro: A Theoretical Framework for Modular Learning of Robust Generative Models", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-robust-gate", |
| "title": "The robust gate existence result is proven via a saddle point of a linearized minimax game over the normalized gate space G_1 and the mixture simplex, established using Kakutani's Fixed Point Theorem (Theorem 3, Section 4.2).", |
| "file": "pages/claim-1-robust-gate/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-kl-bound", |
| "title": "Theorem 3 bounds the KL divergence of the robust gated model to any mixture by log-sum-exp of the per-expert guarantees epsilon_k minus an overlap term (target-weighted conditional entropy) minus a diversity term equal to the Jensen-Shannon Divergence, so the modular approach can outperform aggregate retraining by a gap characterized by JSD (Section 4.2).", |
| "file": "pages/claim-2-kl-bound/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-static-capacity", |
| "title": "Any static (non-adaptive) mixture-weight gate is shown to be fundamentally capacity-limited, with a capacity lower bound of log(sum_k e^{epsilon_k}) for disjoint domains, motivating the need for an input-dependent gate (Theorem 16, Appendix B).", |
| "file": "pages/claim-3-static-capacity/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-algorithm-distillation", |
| "title": "The paper introduces a Stochastic Primal-Dual algorithm to solve the constrained minimax game at scale and a Structural Distillation method that maps the non-causal gate to a causal router for efficient autoregressive inference (Sections 5 and 7).", |
| "file": "pages/claim-4-algorithm-distillation/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-empirical", |
| "title": "Empirical results on synthetic benchmarks and real-world datasets (Wikipedia, Code, FineWeb) show the modular gated architecture mitigates gradient conflict and outperforms monolithic baselines in high-interference regimes (Section 8, Section 8.1).", |
| "file": "pages/claim-5-empirical/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| } |
| } |
