{ "schema_version": 2, "title": "Reproduction: A Fully First-Order Layer for Differentiable Optimization", "emoji": "⚙️", "space_id": "amkkk/repro-a-fully-first-order-layer-for-differentiable-optimization", "paper": { "arxiv_id": "2512.02494", "openreview_id": "jJur8Fq7IK" }, "tags": [ "icml2026-repro", "paper-jJur8Fq7IK" ], "updated_at": "2026-08-01T20:48:18+00:00", "root": { "slug": "index", "title": "Reproduction: A Fully First-Order Layer for Differentiable Optimization", "file": "pages/index.md", "children": [ { "slug": "executive-summary", "title": "Executive summary", "file": "pages/executive-summary/page.md", "children": [] }, { "slug": "claim-1-algorithm", "title": "Claim 1: ε-approximate hypergradient with an active-set Lagrangian oracle", "file": "pages/claim-1-algorithm/page.md", "children": [] }, { "slug": "claim-2-ghost-bilevel", "title": "Claim 2: ghost bilevel reformulation (Theorem 4.1)", "file": "pages/claim-2-ghost-bilevel/page.md", "children": [] }, { "slug": "claim-3-complexity", "title": "Claim 3: O(δ⁻¹ε⁻³) oracle complexity and general convex constraints", "file": "pages/claim-3-complexity/page.md", "children": [] }, { "slug": "claim-4-qp-sudoku", "title": "Claim 4: synthetic QP and 9×9 Sudoku LP benchmarks", "file": "pages/claim-4-qp-sudoku/page.md", "children": [] }, { "slug": "claim-5-implementation", "title": "Claim 5: objective-agnostic PyTorch FFOLayer", "file": "pages/claim-5-implementation/page.md", "children": [] }, { "slug": "claim-6-lpgd", "title": "Claim 6: FFOLayer vs LPGD and Hessian-free backward", "file": "pages/claim-6-lpgd/page.md", "children": [] }, { "slug": "conclusion", "title": "Conclusion", "file": "pages/conclusion/page.md", "children": [] } ] }, "traces": [], "workspace": { "file": "workspace.json", "file_count": 0, "total_size": 0, "bucket_id": null }, "agent_view_tokens": 7937, "trace_view_tokens": 10, "workspace_view_tokens": 8, "revision": "b0ccebb1d0b7af9b7032" }