Morphing into Hybrid Attention Models | Paper Citation Record

Observation 6378b773-e943-4c8b-8244-3e2257eccc3a · outbound

This paper cites Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes.

Morphing into Hybrid Attention Models Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.031487Z

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Observation 2cbf092b-14d8-41e9-b3f0-18b3a687240c · outbound

This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Morphing into Hybrid Attention Models Simple linear attention language models balance the recall-throughput tradeoff

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:27.957941Z

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Observation ca160262-2626-4636-b2d9-d1db4e3d979d · outbound

This paper cites Transformers to ssms: Distilling quadratic knowledge to subquadratic models.Advancesin neural information processing systems, 37:31788–31812, 2024.

Morphing into Hybrid Attention Models Transformers to ssms: Distilling quadratic knowledge to subquadratic models.Advancesin neural information processing systems, 37:31788–31812, 2024

Reference 3

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Observation fe18b65a-dc66-4d42-b0c0-5e322cdb8030 · outbound

This paper cites Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing.

Morphing into Hybrid Attention Models Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:27.964101Z

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Observation 529d5273-ca97-4c00-8d52-35ca2a09a9b7 · outbound

This paper cites Retrieval-aware distillation for transformer-ssm hybrids.

Morphing into Hybrid Attention Models Retrieval-aware distillation for transformer-ssm hybrids

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.026122Z

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Observation 99ca53df-da31-488f-b89f-75004e19b9eb · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Morphing into Hybrid Attention Models Piqa: Reasoning about physical commonsense in natural language

Reference 6

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Observation 253d5645-3a5c-412f-a1e7-6a484d5d36be · outbound

This paper cites Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models.

Morphing into Hybrid Attention Models Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

Reference 7

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Observation e30ea469-1e5f-49e7-b50c-378befa4935f · outbound

This paper cites InternLM2 Technical Report.

Morphing into Hybrid Attention Models InternLM2 Technical Report

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.058419Z

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Observation 3c561deb-0734-4287-aec6-9a746e9a97bc · outbound

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

Morphing into Hybrid Attention Models MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.061232Z

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Observation 792c7e69-cce9-44df-891a-04d3f9b4752b · outbound

This paper cites DiJiang: Efficient Large Language Models through Compact Kernelization.

Morphing into Hybrid Attention Models DiJiang: Efficient Large Language Models through Compact Kernelization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.061053Z

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Observation 691a4607-a72e-44ec-8251-90e8a8179933 · outbound

This paper cites Hybrid linear attention done right: Efficient distillation and effective architectures for extremely long contexts.arXiv preprint arXiv:2601.22156.

Morphing into Hybrid Attention Models Hybrid linear attention done right: Efficient distillation and effective architectures for extremely long contexts.arXiv preprint arXiv:2601.22156

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.064058Z

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Observation 1e68bdab-a9c2-4767-916b-d01850e48c37 · outbound

This paper cites Metala: Unified optimal linear approximation to softmax attention map.Advances in Neural Information Processing Systems, 37:71034–71067, 2024.

Morphing into Hybrid Attention Models Metala: Unified optimal linear approximation to softmax attention map.Advances in Neural Information Processing Systems, 37:71034–71067, 2024

Reference 12

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Observation 24127a82-0dbf-4eba-a758-237532d86cb3 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Morphing into Hybrid Attention Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.069926Z

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Observation 110f7a9d-f44e-4198-a983-9721c7b6eb90 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Morphing into Hybrid Attention Models Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.074982Z

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Observation a7519924-cc5d-42f5-b1a1-5f0cbd608222 · outbound

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

Morphing into Hybrid Attention Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.080460Z

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Observation 84fe8c76-4b81-4def-9814-1954a2ea2e6d · outbound

This paper cites Native Hybrid Attention for Efficient Sequence Modeling.

Morphing into Hybrid Attention Models Native Hybrid Attention for Efficient Sequence Modeling

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.030389Z

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Observation e5607450-8870-4483-a686-f90f8bda82ac · outbound

This paper cites Jacob Dunefsky, Philippe Chlenski, and Neel Nanda.

Morphing into Hybrid Attention Models Jacob Dunefsky, Philippe Chlenski, and Neel Nanda

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:44:28.035637Z

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Observation 9b613bd2-1361-4059-a51c-5406297ad6c1 · outbound

This paper cites He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C.

Morphing into Hybrid Attention Models He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:44:28.044488Z

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Observation 1e300dd8-4d6c-453a-9656-bca6e1ef25c9 · outbound

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

Morphing into Hybrid Attention Models Zamba: A Compact 7B SSM Hybrid Model

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.050046Z

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Observation 04c1926e-3fef-4c65-971f-355cbc71f955 · outbound

This paper cites Radlads: Rapid attention distillation to linear attention decoders at scale.arXiv preprint arXiv:2505.03005, 2025.

Morphing into Hybrid Attention Models Radlads: Rapid attention distillation to linear attention decoders at scale.arXiv preprint arXiv:2505.03005, 2025

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.025419Z

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Observation d93ffcb0-de7b-4b4f-b229-a2d321002968 · outbound

This paper cites The Llama 3 Herd of Models.

Morphing into Hybrid Attention Models The Llama 3 Herd of Models

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.040975Z

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Observation 69398d96-ea33-4a84-8340-1ba164b0db09 · outbound

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

Morphing into Hybrid Attention Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.072422Z

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Observation ff1c374e-90f9-46c4-bfa5-65a17de2543f · outbound

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

Morphing into Hybrid Attention Models Jet-nemotron: Efficient language model with post neural architecture search

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.067447Z

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Observation b9a2f5de-8dab-4a74-aa5d-172a159354c5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Morphing into Hybrid Attention Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.028797Z

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Observation ad3f96d0-4f74-4699-856b-e716726fe787 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Morphing into Hybrid Attention Models RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:27.994760Z

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Observation 4501207c-4b93-4749-a091-45643507ec53 · outbound

This paper cites Thomas Jiralerspong and Trenton Bricken.

Morphing into Hybrid Attention Models Thomas Jiralerspong and Trenton Bricken

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:44:28.001897Z

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Observation 711f743b-9111-40c6-a29c-9c255f6753f6 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Morphing into Hybrid Attention Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.013474Z

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Observation 077fadb5-df37-493e-8c97-9131ffcb12dc · outbound

This paper cites Finetuning pretrained transformers into rnns.

Morphing into Hybrid Attention Models Finetuning pretrained transformers into rnns

Reference 28

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Observation 1c4fd85f-c273-4c8f-bb4e-e4c61e9ba7a8 · outbound

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

Morphing into Hybrid Attention Models Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 29

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Observation f454b5e6-fb4e-47f8-b9b8-4a00cf4497ea · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Morphing into Hybrid Attention Models Efficient memory management for large language model serving with pagedattention

Reference 30

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Observation 39fc0d5a-a5f7-463c-ab25-f431eba3cd48 · outbound

This paper cites Li, Berlin Chen, Caitlin Wang, Aviv Bick, J.

Morphing into Hybrid Attention Models Li, Berlin Chen, Caitlin Wang, Aviv Bick, J

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:44:28.010763Z

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Observation f97b8acd-2ee9-411c-82fc-d1f741cc0eb9 · outbound

This paper cites Liger: Linearizing Large Language Models to Gated Recurrent Structures.

Morphing into Hybrid Attention Models Liger: Linearizing Large Language Models to Gated Recurrent Structures

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.046828Z

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Observation a347fbee-04b6-4ca8-8fa3-66283034c883 · outbound

This paper cites Datacomp-lm: In search of the next generation of training sets for language models.

Morphing into Hybrid Attention Models Datacomp-lm: In search of the next generation of training sets for language models

Reference 33

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Observation adc5bc30-2435-4923-8dbc-ff9884a0b96e · outbound

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

Morphing into Hybrid Attention Models Distilling to hybrid attention models via kl-guided layer selection

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:27.981200Z

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Observation 3a32e2fd-adb8-4d06-868d-c027357ed8b1 · outbound

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

Morphing into Hybrid Attention Models Jamba: A Hybrid Transformer-Mamba Language Model

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.028060Z

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Observation 951161f6-a95d-4c5a-a1ed-e0ab304ccc1b · outbound

This paper cites Lycheedecode: Accelerating long-context llm inference via hybrid-head sparse decoding.

Morphing into Hybrid Attention Models Lycheedecode: Accelerating long-context llm inference via hybrid-head sparse decoding

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.036457Z

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Observation d7bee9ed-cd35-4e30-820b-28bf82eaed8e · outbound

This paper cites DeepSeek-V3 Technical Report.

Morphing into Hybrid Attention Models DeepSeek-V3 Technical Report

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.041702Z

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Observation 204e3832-f957-4128-8687-4db889fd8996 · outbound

This paper cites Openceres: When open information extraction meets the semi-structured web.

Morphing into Hybrid Attention Models Openceres: When open information extraction meets the semi-structured web

Reference 38

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Observation 1ce228b4-6596-431c-8af7-15acfe92d8da · outbound

This paper cites Decoupled Weight Decay Regularization.

Morphing into Hybrid Attention Models Decoupled Weight Decay Regularization

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.047283Z

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Observation c4e0f3d8-37d8-48e1-a8c3-60528a04717a · outbound

This paper cites Linearizing Large Language Models.

Morphing into Hybrid Attention Models Linearizing Large Language Models

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:44:27.990513Z

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Observation 8fac34ce-565f-44b1-8c8d-75223dc12f4c · outbound

This paper cites Olmo Hybrid: From Theory to Practice and Back.

Morphing into Hybrid Attention Models Olmo Hybrid: From Theory to Practice and Back

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.022943Z

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Observation e8094d3a-8494-4fa8-a698-fc0581b6540a · outbound

This paper cites Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners.

Morphing into Hybrid Attention Models Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.077719Z

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Observation 8495e867-b9e2-442d-bd84-cb513c14417e · outbound

This paper cites Rwkv: Reinventing rnns for the transformer era.

Morphing into Hybrid Attention Models Rwkv: Reinventing rnns for the transformer era

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation 914d2c52-64d6-4bee-b3ca-a305c6f8e184 · outbound

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

Morphing into Hybrid Attention Models Yarn: Efficient context window extension of large language models

Reference 44

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Observation 34ca6402-f0aa-4515-9d94-61c396965d93 · outbound

This paper cites Hierarchically gated recurrent neural network for sequence modeling.

Morphing into Hybrid Attention Models Hierarchically gated recurrent neural network for sequence modeling

Reference 45

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Observation 375ebb43-7efb-4d36-8eec-854e9478d337 · outbound

This paper cites Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models.

Morphing into Hybrid Attention Models Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.072509Z

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Observation 7d24fd88-1556-4edf-a88b-f2fd67fe5c67 · outbound

This paper cites HGRN2: Gated Linear RNNs with State Expansion.

Morphing into Hybrid Attention Models HGRN2: Gated Linear RNNs with State Expansion

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.077887Z

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Observation 80be2391-2168-4dcd-bf61-d220cd1c23d4 · outbound

This paper cites Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free.Advances in Neural Information Processing Systems, 38:100092–100118, 2026.

Morphing into Hybrid Attention Models Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free.Advances in Neural Information Processing Systems, 38:100092–100118, 2026

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation 7b6b2e66-0acc-46d0-8314-dee7e455b15a · outbound

This paper cites Qwen3-coder-next technical report.

Morphing into Hybrid Attention Models Qwen3-coder-next technical report

Reference 49

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Observation 6b5faecf-4ace-4fde-9006-e26fc955ad4f · outbound

This paper cites Qwen3.5: Towards native multimodal agents, February 2026.

Morphing into Hybrid Attention Models Qwen3.5: Towards native multimodal agents, February 2026

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation d6f40a52-5ef1-4e7f-86dd-f674dd433e27 · outbound

This paper cites Know what you don’t know: Unanswerable questions for squad.

Morphing into Hybrid Attention Models Know what you don’t know: Unanswerable questions for squad

Reference 51

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Observation 718f5c9e-189d-49e1-970c-ac9c5b578a98 · outbound

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

Morphing into Hybrid Attention Models Samba: Simple hybrid state space models for efficient unlimited context language modeling

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation ae1d793e-725e-4f6e-82a4-0b04ebb2f26a · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

Morphing into Hybrid Attention Models Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation 77bc78ef-3414-4038-82cb-22fffc078316 · outbound

This paper cites Speed Always Wins: A Survey on Efficient Architectures for Large Language Models.

Morphing into Hybrid Attention Models Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.039242Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d2e80d1e-e17f-483a-9f4b-00104bb7aee9 · outbound

This paper cites Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts.

Morphing into Hybrid Attention Models Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.014211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3b8599c0-e6fb-4d1b-9c1e-94ba99e40b3b · outbound

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

Morphing into Hybrid Attention Models Kimi Linear: An Expressive, Efficient Attention Architecture

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.058764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cbb500e6-544f-4213-ab39-3a38a786703d · outbound

This paper cites Attention is all you need.Advancesin neural information processing systems, 30, 2017.

Morphing into Hybrid Attention Models Attention is all you need.Advancesin neural information processing systems, 30, 2017

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81f16ff3-92da-44af-ba65-e6c9d243e07e · outbound

This paper cites A Systematic Analysis of Hybrid Linear Attention.

Morphing into Hybrid Attention Models A Systematic Analysis of Hybrid Linear Attention

Reference 58

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T08:44:28.070031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 38864e67-6cd2-41d0-83e7-0fcb2dfc2f92 · outbound

This paper cites The mamba in the llama: Distilling and accelerating hybrid models.Advancesin Neural Information Processing Systems, 37:62432–62457, 2024.

Morphing into Hybrid Attention Models The mamba in the llama: Distilling and accelerating hybrid models.Advancesin Neural Information Processing Systems, 37:62432–62457, 2024

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

Source-reported events for the cited work

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Observation 29e77731-73f7-40c8-9eaa-796acb276a58 · outbound

This paper cites Rnns are not transformers (yet): The key bottleneck on in-context retrieval.

Morphing into Hybrid Attention Models Rnns are not transformers (yet): The key bottleneck on in-context retrieval

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation ea84efed-c492-41a8-b2c2-1db6d6ca5cbc · outbound

This paper cites Duoattention: Efficient long-context llm inference with retrieval and streaming heads.

Morphing into Hybrid Attention Models Duoattention: Efficient long-context llm inference with retrieval and streaming heads

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation 57de7c10-6b80-4e89-adf2-b7343ec3a232 · outbound

This paper cites Qwen3 Technical Report.

Morphing into Hybrid Attention Models Qwen3 Technical Report

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:27.960596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e943c0d3-134f-470b-9c69-1482387bd0b2 · outbound

This paper cites Zebra-llama: Towards extremely efficient hybrid models.Advancesin Neural Information Processing Systems, 38:78167–78194, 2026.

Morphing into Hybrid Attention Models Zebra-llama: Towards extremely efficient hybrid models.Advancesin Neural Information Processing Systems, 38:78167–78194, 2026

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

Source-reported events for the cited work

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Observation d6dd6b3f-78f5-40f6-a1f5-d51d89ff199b · outbound

This paper cites Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024.

Morphing into Hybrid Attention Models Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

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Observation 8f05ca20-d1a9-4624-bbca-44d251411af1 · outbound

This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

Morphing into Hybrid Attention Models Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.015972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e4c86267-1330-41b7-bda4-d58736fd2f15 · outbound

This paper cites Gated Delta Networks: Improving Mamba2 with Delta Rule.

Morphing into Hybrid Attention Models Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:44:28.033859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a77999e0-0390-4616-b17e-c450bfbc4348 · outbound

This paper cites Parallelizing linear transformers with the delta rule over sequence length.Advancesin neural information processing systems, 37:115491–115522, 2024.

Morphing into Hybrid Attention Models Parallelizing linear transformers with the delta rule over sequence length.Advancesin neural information processing systems, 37:115491–115522, 2024

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fafb3ec-239e-42a9-ab44-b8506dc7ccb3 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th annual meeting of the association for computational linguistics, pages 4791–4800, 2019.

Morphing into Hybrid Attention Models Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th annual meeting of the association for computational linguistics, pages 4791–4800, 2019

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3b9f803-7731-4e7b-9a09-f4b974e9bc04 · outbound

This paper cites LoLCATs: On Low-Rank Linearizing of Large Language Models.

Morphing into Hybrid Attention Models LoLCATs: On Low-Rank Linearizing of Large Language Models

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.083529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4d908499-bde7-424c-bd3f-adc9824ae1a2 · outbound

This paper cites The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry.

Morphing into Hybrid Attention Models The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:44:28.074977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 98c9a88e-de1e-4af1-838b-d56d5118a0d3 · outbound

This paper cites Gated slot attention for efficient linear-time sequence modeling.Advancesin Neural Information Processing Systems, 37:116870–116898, 2024.

Morphing into Hybrid Attention Models Gated slot attention for efficient linear-time sequence modeling.Advancesin Neural Information Processing Systems, 37:116870–116898, 2024

Reference 71

Resolution: unresolved; no resolver link, observed 2026-06-30T05:56:51.447893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e091ae8-936e-4da1-9189-ce5823b5a42a · outbound

This paper cites Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance.

Morphing into Hybrid Attention Models Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.064214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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