| { |
| "schema_version": 1, |
| "title": "Repro - FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment", |
| "emoji": "🎯", |
| "space_id": "Umong/repro-flexrank-nested-matrix-theorems", |
| "paper": { |
| "arxiv_id": "2602.02680" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-DK0kvnNelx" |
| ], |
| "updated_at": "2026-07-17T13:06:40+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Repro - FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "claim-1-post-training-selection-almost-surely-misses-nested-optima", |
| "title": "Claim 1: Post-training selection almost surely misses nested optima", |
| "file": "pages/claim-1-post-training-selection-almost-surely-misses-nested-optima/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-nested-subspace-learning-preserves-every-rank-minimizer", |
| "title": "Claim 2: Nested subspace learning preserves every rank minimizer", |
| "file": "pages/claim-2-nested-subspace-learning-preserves-every-rank-minimizer/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-all-subspaces-learning-has-a-positive-submodel-gap", |
| "title": "Claim 3: All-subspaces learning has a positive submodel gap", |
| "file": "pages/claim-3-all-subspaces-learning-has-a-positive-submodel-gap/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "sources-protocol-and-integrity", |
| "title": "Sources, protocol, and integrity", |
| "file": "pages/sources-protocol-and-integrity/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "flexrank-65469-repro", |
| "title": "flexrank-65469-repro", |
| "file": "pages/flexrank-65469-repro/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 2527, |
| "revision": "1784293600992582961" |
| } |