| { |
| "schema_version": 1, |
| "title": "Reproduction audit: Hybrid Sequence Models", |
| "emoji": "\ud83d\udd2c", |
| "proposed_target_slug": "repro-hybrid-seq-82ejxjzg6r", |
| "publication_status": "local-only; no Space created or modified", |
| "paper": { |
| "arxiv_id": "2603.08859v1", |
| "openreview_id": "82EJxJzG6r" |
| }, |
| "updated_at": "2026-07-28T00:00:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction audit", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-theorem-3-3-literal-bound", |
| "title": "Claim 1: Theorem 3.3 proves that any k-layer state-space model solving the function-composition tasks under injectivity conditions must have total log state-space size scaling as \u03a9(m\u00b7log|V| \u2212 q\u00b7log|Y|), linear in the hidden dimension m (Theorem 3.3).", |
| "file": "pages/claim-1-theorem-3-3-literal-bound/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-theorem-3-7-window-bound", |
| "title": "Claim 2: Theorem 3.7 proves that sliding-window Transformers solving the same tasks under a local-sensitivity condition require total window size scaling with the context-dependency range R (Theorem 3.7).", |
| "file": "pages/claim-2-theorem-3-7-window-bound/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-theorem-4-3-selective-copy", |
| "title": "Claim 3: Theorem 4.3 constructs a two-layer hybrid (Mamba + attention) model that solves the selective copying task using embedding dimension O(max(log|V|, log L)) and working memory \u00d5(N), versus \u03a9(L) required by pure Transformers (Theorem 4.3).", |
| "file": "pages/claim-3-theorem-4-3-selective-copy/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-theorem-4-6-associative-recall", |
| "title": "Claim 4: Theorem 4.6 constructs a three-layer hybrid model that achieves 99% accuracy on the associative recall task using embedding dimension O(max(log|V|, log L)) and window size \u00d5(|V|) (Theorem 4.6).", |
| "file": "pages/claim-4-theorem-4-6-associative-recall/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-figure-4-selective-copy-learning", |
| "title": "Claim 5: On the selective copying task, the learned hybrid model reaches perfect accuracy with roughly 2,000 parameters while pure Transformer/SSM models need roughly 12,000 parameters to match it, a 6x parameter gap (Figure 4).", |
| "file": "pages/claim-5-figure-4-selective-copy-learning/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-6-figures-5-6-associative-recall-learning", |
| "title": "Claim 6: On multi-key associative recall, the hybrid model reaches 60% accuracy using 6x fewer parameters than pure Transformers, which plateau near 40% accuracy on single-key associative recall (Figures 5-6).", |
| "file": "pages/claim-6-figures-5-6-associative-recall-learning/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "routes_built": { |
| "claim_pages": [ |
| "pages/claim-1-theorem-3-3-literal-bound/page.md", |
| "pages/claim-2-theorem-3-7-window-bound/page.md", |
| "pages/claim-3-theorem-4-3-selective-copy/page.md", |
| "pages/claim-4-theorem-4-6-associative-recall/page.md", |
| "pages/claim-5-figure-4-selective-copy-learning/page.md", |
| "pages/claim-6-figures-5-6-associative-recall-learning/page.md" |
| ] |
| } |
| } |
|
|
