Paper Citation Record · LEDGER

Repeat After Me: Transformers are Better than State Space Models at Copying

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2402.01032.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2402.01032 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:23:14.385860Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T07:49:39.775848Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4785e0e2-ffce-4e15-87fe-1fa1c43050d1 · inbound

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models cites this paper.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:58:17.519265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f6b2ad84-3d20-4dd8-bc39-137bce7e2a5b · inbound

An Empirical Study of Mamba-based Language Models cites this paper.

An Empirical Study of Mamba-based Language Models Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:31:03.861393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5e71ee86-8927-4fc7-b8d0-87a84a262889 · inbound

TTT3R: 3D Reconstruction as Test-Time Training cites this paper.

TTT3R: 3D Reconstruction as Test-Time Training Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-17T06:41:16.436769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4ec361f8-de23-415e-8b48-2abadecf5c13 · inbound

To model human linguistic prediction, make LLMs less superhuman cites this paper.

To model human linguistic prediction, make LLMs less superhuman Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T12:55:17.799547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3f7ac2c-eccf-44b4-984b-62f8888dbd4a · inbound

Kimi Linear: An Expressive, Efficient Attention Architecture cites this paper.

Kimi Linear: An Expressive, Efficient Attention Architecture Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-13T23:49:11.163140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8103b078-7df7-4329-8404-fb9a9755987a · inbound

Controllably Efficient Language Models cites this paper.

Controllably Efficient Language Models Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T23:33:56.548151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cd71372-04b3-411a-86a3-38862c2577d6 · inbound

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression cites this paper.

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:00:27.090936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 31bba847-f7ed-4acd-9271-7a3a672b259d · inbound

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers cites this paper.

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T15:22:50.161253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31684462-8ffd-4905-adcd-93b9b894d592 · inbound

The Bayesian Geometry of Transformer Attention cites this paper.

The Bayesian Geometry of Transformer Attention Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T16:20:20.659781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52bab556-5e0a-451b-974f-9fde3c04def9 · inbound

Towards Understanding What State Space Models Learn About Code cites this paper.

Towards Understanding What State Space Models Learn About Code Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T03:51:43.479561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation effe2026-80e9-4099-9641-bacc9a687709 · inbound

The UNDO Flip-Flop: A Controlled Probe for Reversible Semantic State Management in State Space Model cites this paper.

The UNDO Flip-Flop: A Controlled Probe for Reversible Semantic State Management in State Space Model Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:00:52.709421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5215fe50-c4af-46e9-bf7e-663a5d4856ef · inbound

The Recurrent Transformer: Greater Effective Depth and Efficient Decoding cites this paper.

The Recurrent Transformer: Greater Effective Depth and Efficient Decoding Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:21:04.627239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a966edb1-f8a6-4fb8-b3dd-aac28ccb0c79 · inbound

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models cites this paper.

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:45:56.203904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ad50ff5c-3d7c-4fe5-bd79-3d8a57f0aca5 · inbound

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators cites this paper.

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:25:56.325867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bc1e3a80-bddc-4fb0-b861-9bad26bb08db · inbound

Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases cites this paper.

Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:11:27.007244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e827a593-b678-41e4-b8a5-15afeea5ad62 · inbound

OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention cites this paper.

OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:09:23.228532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7606210a-7535-4e13-a8d3-435571d897c7 · inbound

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference cites this paper.

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:43:59.528847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f65382ec-1ece-4ce6-9e6f-5f4802358b7d · inbound

Zamba2-VL Technical Report cites this paper.

Zamba2-VL Technical Report Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T19:26:00.673759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3c7b0e8c-ea93-4311-8fa3-f457973d9ff0 · inbound

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It cites this paper.

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 112

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:37:40.365982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dff055e0-ab10-4f46-938d-37dd97af1876 · inbound

A Verifiable Search Is Not a Learnable Chain-of-Thought cites this paper.

A Verifiable Search Is Not a Learnable Chain-of-Thought Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T07:49:39.777090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ed936055-c6b7-4404-84e4-ce80ef412ce5 · inbound

A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets cites this paper.

A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:48:20.316429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c975545d-840b-4086-ad3b-027c90be10fd · inbound

Remembering Distinct Items, Not Tokens: A Learnable Dirichlet-Process Cache Between State-Space Models and Attention cites this paper.

Remembering Distinct Items, Not Tokens: A Learnable Dirichlet-Process Cache Between State-Space Models and Attention Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T14:50:03.831572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aea3c71-0acc-4a0a-831f-6f5ac09bd726 · inbound

DSSMs: State Space Models with Explicit Memory via Delay Differential Equations cites this paper.

DSSMs: State Space Models with Explicit Memory via Delay Differential Equations Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T13:15:38.524121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ce7cf71-0aa1-429a-acce-aae0ba99215a · inbound

Context by Distinct Information: An Auditable Dirichlet-Process Working Memory for Long, Redundant Context Streams cites this paper.

Context by Distinct Information: An Auditable Dirichlet-Process Working Memory for Long, Redundant Context Streams Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T11:45:09.424370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e90e7497-c8d5-4d09-bdfa-ee34ab6c017d · inbound

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale cites this paper.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T06:39:22.140957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3aa00d8-fc6d-4ebf-b856-35e685c3f74f · inbound

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale cites this paper.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T02:03:24.068474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86d98c18-52eb-4932-92da-eaadab7a7c71 · inbound

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones cites this paper.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T18:06:48.827702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d26c5665-9749-4773-b242-650aa8297dc6 · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:47.771481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 653d6092-2031-4eaf-9b84-61e86a5c15f9 · inbound

Raven: High-Recall Sequence Modeling with Sparse Memory Routing cites this paper.

Raven: High-Recall Sequence Modeling with Sparse Memory Routing Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:44:01.258380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74de6bad-a1d0-4c22-af69-25568eff1569 · inbound

DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling cites this paper.

DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T16:23:14.385860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.