| { |
| "schema_version": 2, |
| "title": "Reproduction: Divide and Learn: Multi-Objective Combinatorial Optimization at Scale", |
| "emoji": "🎯", |
| "space_id": "nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale", |
| "paper": null, |
| "tags": [ |
| "trackio", |
| "open-reproductions", |
| "icml2026", |
| "icml2026-repro", |
| "paper-TK82ECnJzD" |
| ], |
| "updated_at": "2026-07-30T19:35:40+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Divide and Learn: Multi-Objective Combinatorial Optimization at Scale", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-regret-bound-depends-on-subproblem-dimensionality", |
| "title": "Claim 1: regret bound depends on subproblem dimensionality", |
| "file": "pages/claim-1-regret-bound-depends-on-subproblem-dimensionality/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-coupling-error-bound-and-regret-decomposition", |
| "title": "Claim 2: coupling error bound and regret decomposition", |
| "file": "pages/claim-2-coupling-error-bound-and-regret-decomposition/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-multi-expert-framework-and-lagrangian-coordination", |
| "title": "Claim 3: multi-expert framework and Lagrangian coordination", |
| "file": "pages/claim-3-multi-expert-framework-and-lagrangian-coordination/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-moco-benchmark-efficiency-vs-bo-baselines", |
| "title": "Claim 4: MOCO benchmark efficiency vs BO baselines", |
| "file": "pages/claim-4-moco-benchmark-efficiency-vs-bo-baselines/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-bi-tsp-100-head-to-head-vs-specialized-solver-and-pmoco", |
| "title": "Claim 5: Bi-TSP-100 head-to-head vs specialized solver and PMOCO", |
| "file": "pages/claim-5-bi-tsp-100-head-to-head-vs-specialized-solver-and-pmoco/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-6-hw-sw-co-design-proxy-vs-mobo-qparego", |
| "title": "Claim 6: HW-SW co-design proxy vs MOBO-qParEGO", |
| "file": "pages/claim-6-hw-sw-co-design-proxy-vs-mobo-qparego/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": 13843, |
| "trace_view_tokens": 71640, |
| "workspace_view_tokens": 78, |
| "revision": "17a5c407ffc333a4d655", |
| "traces_ref": { |
| "repo_id": "nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale-traces", |
| "repo_type": "dataset", |
| "repo_url": "https://huggingface.co/datasets/nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale-traces", |
| "private": true |
| }, |
| "trace_dataset": "https://huggingface.co/datasets/nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale-traces", |
| "workspace_ref": { |
| "repo_id": "nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale-artifacts", |
| "repo_type": "bucket", |
| "repo_url": "https://huggingface.co/buckets/nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale-artifacts", |
| "private": true |
| }, |
| "workspace_bucket": "https://huggingface.co/buckets/nmaher/repro-divide-and-learn-multi-objective-combinatorial-optimization-at-scale-artifacts" |
| } |
