| { |
| "schema_version": 1, |
| "title": "Reproduction: Rational Transductors", |
| "emoji": "🎯", |
| "space_id": "vimarsh/repro-rational-transductors", |
| "paper": { |
| "arxiv_id": "2602.07599" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-uEZpyELNuB" |
| ], |
| "updated_at": "2026-07-19T10:06:40+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Rational Transductors", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-rational-transductors-augment-a-transformer-stream-with-matrix-valued-recurrence-derived-from-weighted-finite-automata-wfa-injecting-this-signal-into-attention-via-a-deep-rational-injection-mechanism-section-2", |
| "title": "Claim 1: Rational Transductors augment a Transformer stream with matrix-valued recurrence derived from Weighted Finite Automata (WFA), injecting this signal into attention via a 'Deep Rational Injection' mechanism (Section 2).", |
| "file": "pages/claim-1-rational-transductors-augment-a-transformer-stream-with-matrix-valued-recurrence-derived-from-weighted-finite-automata-wfa-injecting-this-signal-into-attention-via-a-deep-rational-injection-mechanism-section-2/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-the-architecture-is-shown-to-capture-regular-languages-and-nc1-complete-problems-such-as-boolean-formula-evaluation-in-contrast-to-standard-self-attention-which-is-limited-to-ac0-under-hard-attention-or-tc0-under-soft-attention-sections-4-7", |
| "title": "Claim 2: The architecture is shown to capture Regular Languages and NC1-complete problems such as Boolean Formula Evaluation, in contrast to standard self-attention which is limited to AC0 under hard attention or TC0 under soft attention (Sections 4-7).", |
| "file": "pages/claim-2-the-architecture-is-shown-to-capture-regular-languages-and-nc1-complete-problems-such-as-boolean-formula-evaluation-in-contrast-to-standard-self-attention-which-is-limited-to-ac0-under-hard-attention-or-tc0-under-soft-attention-sections-4-7/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-random-rational-features-are-introduced-as-a-universal-basis-for-sequential-dependencies-while-differentiable-rational-features-are-introduced-to-close-the-remaining-representational-gap-of-the-rational-feature-approximation-section-3", |
| "title": "Claim 3: Random Rational Features are introduced as a universal basis for sequential dependencies, while Differentiable Rational Features are introduced to close the remaining representational gap of the rational-feature approximation (Section 3).", |
| "file": "pages/claim-3-random-rational-features-are-introduced-as-a-universal-basis-for-sequential-dependencies-while-differentiable-rational-features-are-introduced-to-close-the-remaining-representational-gap-of-the-rational-feature-approximation-section-3/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-the-proposed-architecture-preserves-a-parallel-training-time-complexity-of-o-l-log-t-in-sequence-length-t-retaining-the-parallelization-advantage-of-transformers-over-strictly-sequential-rnn-computation-section-2", |
| "title": "Claim 4: The proposed architecture preserves a parallel training time complexity of O(L + log T) in sequence length T, retaining the parallelization advantage of Transformers over strictly sequential RNN computation (Section 2).", |
| "file": "pages/claim-4-the-proposed-architecture-preserves-a-parallel-training-time-complexity-of-o-l-log-t-in-sequence-length-t-retaining-the-parallelization-advantage-of-transformers-over-strictly-sequential-rnn-computation-section-2/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-experiments-demonstrate-robust-length-generalization-on-algorithmic-tasks-requiring-state-tracking-the-regular-gap-closing-a-gap-where-standard-transformers-fail-appendix-b", |
| "title": "Claim 5: Experiments demonstrate robust length generalization on algorithmic tasks requiring state tracking (the 'Regular Gap'), closing a gap where standard Transformers fail (Appendix B).", |
| "file": "pages/claim-5-experiments-demonstrate-robust-length-generalization-on-algorithmic-tasks-requiring-state-tracking-the-regular-gap-closing-a-gap-where-standard-transformers-fail-appendix-b/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 6188, |
| "revision": "1784455600701660489" |
| } |
