Paper Citation Record · LEDGER

Priming: Hybrid State Space Models From Pre-trained Transformers

As of 4 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 3 inbound Pith citation observations for arXiv:2605.08301.

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

pith.paper-citation-record.v1
2605.08301 v1

Coverage vector

measured 100 of 103 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T01:14:01.584159Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:03:22.318771Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 103 outbound references displayed

verified exact31
verified fuzzy58
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ff7aafc-a2be-45bc-bef2-5cb6822ee13b · outbound

This paper cites GQA : Training generalized multi-query transformer models from multi-head checkpoints.

Priming: Hybrid State Space Models From Pre-trained Transformers GQA : Training generalized multi-query transformer models from multi-head checkpoints

Reference 1

Resolution: verified exact; doi, observed 2026-05-12T01:16:13.883063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 51e8d96c-ee7e-4d16-a986-214fc6daf6c7 · outbound

This paper cites Training-free long-context scaling of large language models.

Priming: Hybrid State Space Models From Pre-trained Transformers Training-free long-context scaling of large language models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.628756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b5a230cb-4f44-423f-bf26-dde20eb946b5 · outbound

This paper cites Zoology: Measuring and improving recall in efficient language models.

Priming: Hybrid State Space Models From Pre-trained Transformers Zoology: Measuring and improving recall in efficient language models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.623082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d32423a0-737b-4ff9-9aa2-8ef27a780df7 · outbound

This paper cites Hybrid Architectures for Language Models: Systematic Analysis and Design Insights.

Priming: Hybrid State Space Models From Pre-trained Transformers Hybrid Architectures for Language Models: Systematic Analysis and Design Insights

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.319329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 937d8321-0e24-474d-b17f-f5c9de20b1f2 · outbound

This paper cites Chan, James Demmel, June Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo, Charles Romine, and Henk van der Vorst.

Priming: Hybrid State Space Models From Pre-trained Transformers Chan, James Demmel, June Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo, Charles Romine, and Henk van der Vorst

Reference 5

Resolution: verified exact; doi, observed 2026-05-12T01:16:13.855279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6f681267-1086-4cd4-bc16-65167b3d0426 · outbound

This paper cites NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model.

Priming: Hybrid State Space Models From Pre-trained Transformers NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:02:08.433616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bfc6085b-37c1-430f-a58a-b651690c2097 · outbound

This paper cites o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, G \.

Priming: Hybrid State Space Models From Pre-trained Transformers o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, G \

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.624776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cb57d44-57fe-44b8-bfb0-692de7f97051 · outbound

This paper cites Transformers to ssms: Distilling quadratic knowledge to subquadratic models.

Priming: Hybrid State Space Models From Pre-trained Transformers Transformers to ssms: Distilling quadratic knowledge to subquadratic models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.619608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 81eea1c2-39bc-401c-bff5-fbec4135112c · outbound

This paper cites NVIDIA Nemotron 3: Efficient and Open Intelligence.

Priming: Hybrid State Space Models From Pre-trained Transformers NVIDIA Nemotron 3: Efficient and Open Intelligence

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:40:43.189893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9810ab0-561f-44cc-be02-b1746864dd73 · outbound

This paper cites Nemotron 3 nano: Open, efficient mixture-of- experts hybrid mamba-transformer model for agentic reasoning.

Priming: Hybrid State Space Models From Pre-trained Transformers Nemotron 3 nano: Open, efficient mixture-of- experts hybrid mamba-transformer model for agentic reasoning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:24.383814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d2e8985-7c82-48f3-9dfd-757aa98d9d5e · outbound

This paper cites Qwen3-Coder-Next Technical Report.

Priming: Hybrid State Space Models From Pre-trained Transformers Qwen3-Coder-Next Technical Report

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:12:48.468873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 73390f12-bb59-4489-a813-743d78cc460c · outbound

This paper cites MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention.

Priming: Hybrid State Space Models From Pre-trained Transformers MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:28:16.586976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d60bf531-4344-49c9-921a-7ce4e222f91f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Priming: Hybrid State Space Models From Pre-trained Transformers Evaluating Large Language Models Trained on Code

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.324208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 017c088b-7c25-41f6-81e3-d757de190138 · outbound

This paper cites Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, and Huan Sun.

Priming: Hybrid State Space Models From Pre-trained Transformers Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, and Huan Sun

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.617611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3020041d-d596-407a-8758-7b23be2f8263 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Priming: Hybrid State Space Models From Pre-trained Transformers Training Verifiers to Solve Math Word Problems

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.428564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7e8955e8-2cc8-4860-8c1b-c175cfb36fbe · outbound

This paper cites Albert, Pranesh Srinivasan, Haining Pan, Philippe Faist, Brian A Rohr, Michael J.

Priming: Hybrid State Space Models From Pre-trained Transformers Albert, Pranesh Srinivasan, Haining Pan, Philippe Faist, Brian A Rohr, Michael J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.621362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a78944fe-cdae-49a4-9fc0-fb2af640fb0c · outbound

This paper cites Transformer-xl: Attentive language models beyond a fixed-length context.

Priming: Hybrid State Space Models From Pre-trained Transformers Transformer-xl: Attentive language models beyond a fixed-length context

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.626741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bd33fdd7-d439-45d6-a3c7-359a5313b4ca · outbound

This paper cites Transformers are ssms: Generalized models and efficient algorithms through structured state space duality.

Priming: Hybrid State Space Models From Pre-trained Transformers Transformers are ssms: Generalized models and efficient algorithms through structured state space duality

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.630680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 01c2b27f-cbdc-4fdc-bcb6-ca9670a9df3c · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Priming: Hybrid State Space Models From Pre-trained Transformers Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.610375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 96e8e322-c921-43a0-b314-a6df5715f1a1 · outbound

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

Priming: Hybrid State Space Models From Pre-trained Transformers Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b3df9085-4f0a-4da3-9d68-d551fe8ab05c · outbound

This paper cites Fewer truncations improve language modeling.

Priming: Hybrid State Space Models From Pre-trained Transformers Fewer truncations improve language modeling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.608426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 363e801b-12a3-4f15-b0e3-bfc658e1c6b7 · outbound

This paper cites Hymba: A hybrid-head architecture for small language models.

Priming: Hybrid State Space Models From Pre-trained Transformers Hymba: A hybrid-head architecture for small language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.612412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 279ab5d2-cf54-4277-b35d-d2a7da0b70c5 · outbound

This paper cites A mathematical framework for transformer circuits.

Priming: Hybrid State Space Models From Pre-trained Transformers A mathematical framework for transformer circuits

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.614239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3cf975c1-395e-4bc3-9f16-7eedb95fba61 · outbound

This paper cites AREAL : A large-scale asynchronous reinforcement learning system for language reasoning.

Priming: Hybrid State Space Models From Pre-trained Transformers AREAL : A large-scale asynchronous reinforcement learning system for language reasoning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.603176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 426662d7-f5b7-4f1d-bd8b-15a064ad7429 · outbound

This paper cites arXiv preprint arXiv:2512.12167 (2025) 44.

Priming: Hybrid State Space Models From Pre-trained Transformers arXiv preprint arXiv:2512.12167 (2025) 44

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:21:24.341764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1cabd8dc-b39d-40ab-81f7-05a3ffbb71d6 · outbound

This paper cites Zamba: A Compact 7B SSM Hybrid Model.

Priming: Hybrid State Space Models From Pre-trained Transformers Zamba: A Compact 7B SSM Hybrid Model

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:24.308739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ce0bfaac-6b8b-4da5-921e-5945e98fe755 · outbound

This paper cites RADLADS : Rapid attention distillation to linear attention decoders at scale.

Priming: Hybrid State Space Models From Pre-trained Transformers RADLADS : Rapid attention distillation to linear attention decoders at scale

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.605008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f20a10db-f0f4-42aa-ac4f-4b9203b03277 · outbound

This paper cites The Llama 3 Herd of Models.

Priming: Hybrid State Space Models From Pre-trained Transformers The Llama 3 Herd of Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.292006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6ff6264f-8d7a-43a0-afb5-fd5dddbf66dd · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Priming: Hybrid State Space Models From Pre-trained Transformers Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.314225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 281c1068-8fdb-4e87-819f-1a4ac6d0763f · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

Priming: Hybrid State Space Models From Pre-trained Transformers Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.615947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e531da0d-4253-4a6d-bc90-599e5a9b4b53 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Priming: Hybrid State Space Models From Pre-trained Transformers Efficiently modeling long sequences with structured state spaces

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.632757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 16c30fae-2d50-47a4-baec-22e9372604a0 · outbound

This paper cites Jet-nemotron: Efficient language model with post neural architecture search.

Priming: Hybrid State Space Models From Pre-trained Transformers Jet-nemotron: Efficient language model with post neural architecture search

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.634462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3bf65d22-8989-4c53-97ea-5578daaa2961 · outbound

This paper cites A survey of model reduction by balanced truncation and some new results.

Priming: Hybrid State Space Models From Pre-trained Transformers A survey of model reduction by balanced truncation and some new results

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.599732Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8c850831-affb-47d6-bb81-6e593771b881 · outbound

This paper cites Measuring massive multitask language understanding.

Priming: Hybrid State Space Models From Pre-trained Transformers Measuring massive multitask language understanding

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.593886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8cf7fb8-eb70-48aa-8284-7c31f7af2c0f · outbound

This paper cites RULER : What s the real context size of your long-context language models? In First Conference on Language Modeling.

Priming: Hybrid State Space Models From Pre-trained Transformers RULER : What s the real context size of your long-context language models? In First Conference on Language Modeling

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.601514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1ae39118-3125-4b44-8c67-34f172dcf609 · outbound

This paper cites DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models.

Priming: Hybrid State Space Models From Pre-trained Transformers DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:07:22.650116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c30619b5-d517-4600-96a3-288bb4aaf694 · outbound

This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.

Priming: Hybrid State Space Models From Pre-trained Transformers Livecodebench: Holistic and contamination free evaluation of large language models for code

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.586669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e2ef0674-9eb1-41a4-aeda-305953802a16 · outbound

This paper cites Kakade, and eran malach.

Priming: Hybrid State Space Models From Pre-trained Transformers Kakade, and eran malach

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.606603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 47c771e0-f2e4-4a40-bd43-0864906837c8 · outbound

This paper cites SWE -bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations.

Priming: Hybrid State Space Models From Pre-trained Transformers SWE -bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.581759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 490f224e-a9ff-4fa4-88b5-de3383575342 · outbound

This paper cites an unresolved cited work.

Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-05-14T08:05:37.583305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2e58b95e-be58-4d01-9c25-cfc01b0401a0 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Priming: Hybrid State Space Models From Pre-trained Transformers Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.584966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3a2a1bfb-6e45-4952-a779-28f2d39b268f · outbound

This paper cites Reformer: The efficient transformer.

Priming: Hybrid State Space Models From Pre-trained Transformers Reformer: The efficient transformer

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.588437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 47bb123b-44ac-4f48-944c-bf5a5a20f7b3 · outbound

This paper cites BABIL ong: Testing the limits of LLM s with long context reasoning-in-a-haystack.

Priming: Hybrid State Space Models From Pre-trained Transformers BABIL ong: Testing the limits of LLM s with long context reasoning-in-a-haystack

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.591885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aea44e9c-66a0-4197-a0ae-7236307006a8 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Priming: Hybrid State Space Models From Pre-trained Transformers Gonzalez, Hao Zhang, and Ion Stoica

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.596001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8e55d16-425b-49ef-8448-f5994b95eb4e · outbound

This paper cites Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A.

Priming: Hybrid State Space Models From Pre-trained Transformers Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.590105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 988de457-b21d-4390-a0c1-2fa629a902f9 · outbound

This paper cites Liger: Linearizing large language models to gated recurrent structures.

Priming: Hybrid State Space Models From Pre-trained Transformers Liger: Linearizing large language models to gated recurrent structures

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.579760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 28d1534a-5c58-4db3-ac42-84b8dc67bb81 · outbound

This paper cites Distilling to hybrid attention models via kl-guided layer selection.

Priming: Hybrid State Space Models From Pre-trained Transformers Distilling to hybrid attention models via kl-guided layer selection

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:24.433997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a00fa614-b8a5-4108-a1d7-86958c0cce91 · outbound

This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Priming: Hybrid State Space Models From Pre-trained Transformers Jamba: A Hybrid Transformer-Mamba Language Model

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-13T14:11:27.316872Z

Source-reported events for the cited work

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Observation 49ceeb95-dd09-4998-8c1a-9b81a21cbe86 · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods.

Priming: Hybrid State Space Models From Pre-trained Transformers Truthfulqa: Measuring how models mimic human falsehoods

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.569193Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5b1386f6-2a5e-4c76-bd15-7c542f625b06 · outbound

This paper cites On the stochastic realization problem.

Priming: Hybrid State Space Models From Pre-trained Transformers On the stochastic realization problem

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.570902Z

Source-reported events for the cited work

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Observation d8b7613b-0b3d-4d70-beda-a375c2958f6a · outbound

This paper cites Ringattention with blockwise transformers for near-infinite context.

Priming: Hybrid State Space Models From Pre-trained Transformers Ringattention with blockwise transformers for near-infinite context

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.572567Z

Source-reported events for the cited work

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Observation 5188ea24-2971-405a-a5f6-d4d51d710546 · outbound

This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation.

Priming: Hybrid State Space Models From Pre-trained Transformers Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.574274Z

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Observation 161b96d5-0ac4-425c-8728-ed406cbc5ae8 · outbound

This paper cites PICASO : Permutation-invariant context composition with state space models.

Priming: Hybrid State Space Models From Pre-trained Transformers PICASO : Permutation-invariant context composition with state space models

Reference 53

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 565abb02-4b3a-4a55-8172-2a0bf9caab66 · outbound

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Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 54

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This paper cites Error propagation properties of recursive least-squares adaptation algorithms.

Priming: Hybrid State Space Models From Pre-trained Transformers Error propagation properties of recursive least-squares adaptation algorithms

Reference 55

Resolution: verified exact; doi, observed 2026-05-12T01:16:13.851121Z

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Observation a4f5633b-649f-4f16-a69d-e599b577a8cd · outbound

This paper cites When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.

Priming: Hybrid State Space Models From Pre-trained Transformers When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Reference 56

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Observation 882de4f2-0ee7-421c-b007-f59a54d3144f · outbound

This paper cites AMC/AIME : MAA invitational competitions.

Priming: Hybrid State Space Models From Pre-trained Transformers AMC/AIME : MAA invitational competitions

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.576248Z

Source-reported events for the cited work

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Observation df7df56e-506a-4215-8e78-6fb315c5543d · outbound

This paper cites Linearizing large language models.

Priming: Hybrid State Space Models From Pre-trained Transformers Linearizing large language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.560473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f562990e-7dfa-487a-bf7a-e7a8c67249af · outbound

This paper cites Landmark attention: Random-access infinite context length for transformers.

Priming: Hybrid State Space Models From Pre-trained Transformers Landmark attention: Random-access infinite context length for transformers

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.555534Z

Source-reported events for the cited work

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Observation e83cdf94-e5b8-4fbf-aaaf-6faf1ac9127b · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

Priming: Hybrid State Space Models From Pre-trained Transformers Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:17:00.308428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c0df41a-7846-4aab-9c6f-176c801b3f07 · outbound

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Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-05-14T08:05:37.557174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 60347e00-26d8-4a50-ad55-ce99597ea7b9 · outbound

This paper cites Expansion span: Combining fading memory and retrieval in hybrid state space models.

Priming: Hybrid State Space Models From Pre-trained Transformers Expansion span: Combining fading memory and retrieval in hybrid state space models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.562085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c39bae94-aef0-409d-ac04-5f7881ed9c77 · outbound

This paper cites In-context Learning and Induction Heads.

Priming: Hybrid State Space Models From Pre-trained Transformers In-context Learning and Induction Heads

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.351717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f5f4758e-6e05-4ffe-8dd6-90b914375b6f · outbound

This paper cites Resurrecting recurrent neural networks for long sequences.

Priming: Hybrid State Space Models From Pre-trained Transformers Resurrecting recurrent neural networks for long sequences

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.553782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aee01451-f1bc-4d82-8a51-f6d9ca9c3f38 · outbound

This paper cites Marconi: Prefix caching for the era of hybrid LLM s.

Priming: Hybrid State Space Models From Pre-trained Transformers Marconi: Prefix caching for the era of hybrid LLM s

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.558885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dcf34717-e677-46d3-8a55-f08bc8ed5e90 · outbound

This paper cites Patil, Huanzhi Mao, Charlie Cheng-Jie Ji, Fanjia Yan, Vishnu Suresh, Ion Stoica, and Joseph E.

Priming: Hybrid State Space Models From Pre-trained Transformers Patil, Huanzhi Mao, Charlie Cheng-Jie Ji, Fanjia Yan, Vishnu Suresh, Ion Stoica, and Joseph E

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.567478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 272b97d0-e517-4bce-a3ea-da3b4cea7187 · outbound

This paper cites Time-Varying Systems and Computations.

Priming: Hybrid State Space Models From Pre-trained Transformers Time-Varying Systems and Computations

Reference 67

Resolution: verified exact; doi, observed 2026-05-12T01:16:13.867011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6436c4c4-57c5-4314-a932-a569b495373c · outbound

This paper cites Ya RN : Efficient context window extension of large language models.

Priming: Hybrid State Space Models From Pre-trained Transformers Ya RN : Efficient context window extension of large language models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.597985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a8d63189-fa8e-4492-9239-41c59c345cb4 · outbound

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

Priming: Hybrid State Space Models From Pre-trained Transformers Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:04:17.792158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9fa358c9-7797-4344-8374-0f00905acf27 · outbound

This paper cites Generalizing verifiable instruction following.

Priming: Hybrid State Space Models From Pre-trained Transformers Generalizing verifiable instruction following

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.546893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a5ae0a2e-c954-49b4-9b55-9223e8962699 · outbound

This paper cites Ulysses sequence parallelism in the hugging face ecosystem.

Priming: Hybrid State Space Models From Pre-trained Transformers Ulysses sequence parallelism in the hugging face ecosystem

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.548622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 10b9c153-478a-4296-b8cb-82b16c474648 · outbound

This paper cites an unresolved cited work.

Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-05-14T08:05:37.543131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6a65d74d-56b4-496f-bb0e-1a5633daa46f · outbound

This paper cites Samba: Simple hybrid state space models for efficient unlimited context language modeling.

Priming: Hybrid State Space Models From Pre-trained Transformers Samba: Simple hybrid state space models for efficient unlimited context language modeling

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.516151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7ef43862-d7e3-4c5b-9a2e-a8a6ef7afcd8 · outbound

This paper cites Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi.

Priming: Hybrid State Space Models From Pre-trained Transformers Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:21:24.329790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 13a2ee2d-c848-4f4c-859d-419c44e8051d · outbound

This paper cites Sandberg and A.

Priming: Hybrid State Space Models From Pre-trained Transformers Sandberg and A

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:16:13.875977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9bd85cc4-e5dc-4a41-a84e-71edc2ef8922 · outbound

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Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-05-14T08:05:37.544778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dee51b14-2b7a-4c12-a90b-8989670958c9 · outbound

This paper cites an unresolved cited work.

Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-05-14T08:05:37.550215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 52ba9a51-c1c8-4f15-b6a8-98bc4b2fce2d · outbound

This paper cites Flashattention-3: Fast and accurate attention with asynchrony and low-precision.

Priming: Hybrid State Space Models From Pre-trained Transformers Flashattention-3: Fast and accurate attention with asynchrony and low-precision

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.521493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b4d2990-9ea2-48e9-9dd1-197840eb6047 · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

Priming: Hybrid State Space Models From Pre-trained Transformers Hybridflow: A flexible and efficient rlhf framework

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T01:16:13.863162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 326614f8-3d6d-40da-93d7-9b8f4ef33b72 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Priming: Hybrid State Space Models From Pre-trained Transformers Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.303495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8922d645-bb58-49af-94e9-c1af7a027842 · outbound

This paper cites Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation.

Priming: Hybrid State Space Models From Pre-trained Transformers Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.357221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ec2e9b3b-4ffb-49a6-8046-950a486a530f · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Priming: Hybrid State Space Models From Pre-trained Transformers Retentive Network: A Successor to Transformer for Large Language Models

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:21:24.346538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b68dc4de-f625-4eae-8803-362998f0c8d6 · outbound

This paper cites Challenging big-bench tasks and whether chain-of-thought can solve them.

Priming: Hybrid State Space Models From Pre-trained Transformers Challenging big-bench tasks and whether chain-of-thought can solve them

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.523437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 82074d65-a63b-463c-be2d-c2229ed33159 · outbound

This paper cites Scicode: A research coding benchmark curated by scientists.

Priming: Hybrid State Space Models From Pre-trained Transformers Scicode: A research coding benchmark curated by scientists

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.519793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cb3c5521-07db-497f-b58f-4e30e9442b2d · outbound

This paper cites Attention is all you need.

Priming: Hybrid State Space Models From Pre-trained Transformers Attention is all you need

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.518071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 66e2f336-3a1d-42fa-a17c-4159881331e2 · outbound

This paper cites Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries.

Priming: Hybrid State Space Models From Pre-trained Transformers Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries

Reference 86

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:21:24.297719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bb8b7f7b-f4c7-4ec3-b938-630a75f25846 · outbound

This paper cites MesaNet: Sequence Modeling by Locally Optimal Test-Time Training.

Priming: Hybrid State Space Models From Pre-trained Transformers MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-06-04T02:07:39.171975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 98579f67-ec15-4123-ad7b-0588d2c509bd · outbound

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

Priming: Hybrid State Space Models From Pre-trained Transformers An Empirical Study of Mamba-based Language Models

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:31:04.038637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 31c73377-1c84-489b-98cd-dae9d5eac527 · outbound

This paper cites The mamba in the llama: Distilling and accelerating hybrid models.

Priming: Hybrid State Space Models From Pre-trained Transformers The mamba in the llama: Distilling and accelerating hybrid models

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.538336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d67adf58-18a2-4995-9480-5d8e621832d2 · outbound

This paper cites M1: Towards scalable test-time compute with mamba reasoning models.

Priming: Hybrid State Space Models From Pre-trained Transformers M1: Towards scalable test-time compute with mamba reasoning models

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.540001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5356abb7-9ac4-4a42-9bb1-bd0936c3871f · outbound

This paper cites an unresolved cited work.

Priming: Hybrid State Space Models From Pre-trained Transformers Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-05-14T08:05:37.541613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 71612217-148e-4283-bb0c-1cfb27be1269 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Priming: Hybrid State Space Models From Pre-trained Transformers Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.551956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 412068f2-b15f-4266-acb1-1420bb73e247 · outbound

This paper cites Effective long-context scaling of foundation models.

Priming: Hybrid State Space Models From Pre-trained Transformers Effective long-context scaling of foundation models

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.533035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8bc08899-e54e-4ec7-b1c7-61b869b9ce6e · outbound

This paper cites Gated linear attention transformers with hardware-efficient training.

Priming: Hybrid State Space Models From Pre-trained Transformers Gated linear attention transformers with hardware-efficient training

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.534723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8a41a46f-e508-4d72-8d45-09fdfb443138 · outbound

This paper cites Parallelizing linear transformers with the delta rule over sequence length.

Priming: Hybrid State Space Models From Pre-trained Transformers Parallelizing linear transformers with the delta rule over sequence length

Reference 95

Resolution: verified exact; doi, observed 2026-05-12T01:16:13.870615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 647af476-cda2-4fbc-abbe-77fa6825e88c · outbound

This paper cites Gated delta networks: Improving mamba2 with delta rule.

Priming: Hybrid State Space Models From Pre-trained Transformers Gated delta networks: Improving mamba2 with delta rule

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.577993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0a75c586-7ea7-4804-82b0-6ca2995911c0 · outbound

This paper cites Ape: Faster and longer context-augmented generation via adaptive parallel encoding.

Priming: Hybrid State Space Models From Pre-trained Transformers Ape: Faster and longer context-augmented generation via adaptive parallel encoding

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.531221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e6b20170-6593-4a3f-93b6-389b03d502ba · outbound

This paper cites Helmet: How to evaluate long-context language models effectively and thoroughly.

Priming: Hybrid State Space Models From Pre-trained Transformers Helmet: How to evaluate long-context language models effectively and thoroughly

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.529449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b03622f6-866b-47b2-848d-461d9e794b13 · outbound

This paper cites Native sparse attention: Hardware-aligned and natively trainable sparse attention.

Priming: Hybrid State Space Models From Pre-trained Transformers Native sparse attention: Hardware-aligned and natively trainable sparse attention

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T08:05:37.536451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4f4d1986-d94c-4de3-a448-3c909abf6af2 · outbound

This paper cites Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection.

Priming: Hybrid State Space Models From Pre-trained Transformers Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:24.281364Z

Source-reported events for the cited work

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Pith citing papers

Observation 6e95951f-c6f1-41fe-b0cd-289351c35abe · inbound

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

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Priming: Hybrid State Space Models From Pre-trained Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T06:39:20.915609Z

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Observation 7241869c-1acf-401b-a44d-95865c955a0c · inbound

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

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Priming: Hybrid State Space Models From Pre-trained Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T02:03:22.318771Z

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Observation 8d8a1feb-65df-46c1-ba5e-724df7bd875e · inbound

Memory for Large Language Models cites this paper.

Memory for Large Language Models Priming: Hybrid State Space Models From Pre-trained Transformers

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T02:37:54.395898Z

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