{ "schema_version": 2, "title": "Repro - FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over T", "emoji": "🎯", "space_id": "VeigaPunk/coINrhVRkL", "paper": { "arxiv_id": "2605.06328" }, "tags": [ "icml2026-repro", "paper-coINrhVRkL" ], "updated_at": "2026-07-22T09:17:18+00:00", "root": { "slug": "index", "title": "Repro - FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over T", "file": "pages/index.md", "children": [ { "slug": "claim-1-fab-integrates-push-pull-communication-strategy-with-value-function-based-penalt", "title": "Claim 1: FAB integrates Push–Pull communication strategy with value function-based penalt", "file": "pages/claim-1-fab-integrates-push-pull-communication-strategy-with-value-function-based-penalt/page.md", "children": [] }, { "slug": "claim-2-resolves-open-question-concerning-push-pull-convergence-over-time-varying-direct", "title": "Claim 2: Resolves open question concerning Push–Pull convergence over time-varying direct", "file": "pages/claim-2-resolves-open-question-concerning-push-pull-convergence-over-time-varying-direct/page.md", "children": [] }, { "slug": "claim-3-effective-on-hyperparameter-tuning-data-hyper-cleaning-and-reinforcement-learn", "title": "Claim 3: Effective on hyperparameter tuning, data hyper-cleaning, and reinforcement learn", "file": "pages/claim-3-effective-on-hyperparameter-tuning-data-hyper-cleaning-and-reinforcement-learn/page.md", "children": [] }, { "slug": "conclusion", "title": "Conclusion", "file": "pages/conclusion/page.md", "children": [] }, { "slug": "executive-summary", "title": "Executive summary", "file": "pages/executive-summary/page.md", "children": [] } ] }, "traces": [], "workspace": { "file": "workspace.json", "file_count": 0, "total_size": 0, "bucket_id": "VeigaPunk/coINrhVRkL-artifacts" }, "agent_view_tokens": 1666, "trace_view_tokens": 10, "workspace_view_tokens": 8, "revision": "7aa9c148fee48da9e902" }