Paper Citation Record · LEDGER

Lizard: An Efficient Linearization Framework for Large Language Models

As of 4 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2507.09025.

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

pith.paper-citation-record.v1
2507.09025 v4

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 28 of 28 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T01:44:40.722957Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T01:45:50.815719Z

Reference resolution

27 of 27 outbound references displayed

verified exact20
verified fuzzy2
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cbcf536-b811-4eb8-88c5-443e2c5ff575 · outbound

This paper cites GPT-4 Technical Report.

Lizard: An Efficient Linearization Framework for Large Language Models GPT-4 Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.594270Z

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 feee02e8-4167-4086-b813-553c338deed0 · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Simple linear attention language models balance the recall-throughput tradeoff

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:42:04.615556Z

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 e4969b06-8f7d-4939-9e3d-6eecfc63c05a · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.606340Z

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 86a92dd1-ae79-4569-bca6-6c8feda35b2d · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Lizard: An Efficient Linearization Framework for Large Language Models FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.519962Z

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 3c99f5ce-0817-4179-97d9-bfbed795147e · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.585923Z

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 ad621e97-2937-4b58-bf06-027b3959d7c1 · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T04:42:04.534710Z

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 1190704a-829e-47b7-ba17-f7ca479d2fb3 · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T04:42:04.525610Z

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 00cab715-d72d-4f6c-a5ee-ca660bfe96e9 · outbound

This paper cites The Llama 3 Herd of Models.

Lizard: An Efficient Linearization Framework for Large Language Models The Llama 3 Herd of Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.577626Z

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 28017c77-9f03-413e-a8e2-06210ba48d0f · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.529897Z

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 256a55ea-9b62-47a0-88df-08cd6124b1d1 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Lizard: An Efficient Linearization Framework for Large Language Models Measuring Massive Multitask Language Understanding

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.539139Z

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 60692ec2-31f5-4fc1-8e86-1a497bccf65c · outbound

This paper cites Mistral 7B.

Lizard: An Efficient Linearization Framework for Large Language Models Mistral 7B

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.619077Z

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 2acfbd89-7815-46b2-8aaa-613e3183c044 · outbound

This paper cites Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache.

Lizard: An Efficient Linearization Framework for Large Language Models Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:42:04.568701Z

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 ff7dfa6d-c425-4cf7-84c4-78d789e2a2c0 · outbound

This paper cites Language Models are Few-Shot Learners.

Lizard: An Efficient Linearization Framework for Large Language Models Language Models are Few-Shot Learners

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.543954Z

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 5fe8ba5c-a0d7-4951-bd21-36aa440d4d47 · outbound

This paper cites Linearizing Large Language Models.

Lizard: An Efficient Linearization Framework for Large Language Models Linearizing Large Language Models

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T04:42:04.573598Z

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 6f655a4b-3844-4cbf-aba8-ac4ba30cdf1b · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 15

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 d9fe5a8c-793d-475d-8289-2abd405f6836 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Lizard: An Efficient Linearization Framework for Large Language Models RWKV: Reinventing RNNs for the Transformer Era

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T04:42:04.176977Z

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 bc6c667a-1a6c-476c-84e7-a206061178b5 · outbound

This paper cites TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer.

Lizard: An Efficient Linearization Framework for Large Language Models TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:42:04.610884Z

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 53024a91-b089-4b6d-bcca-e7e02eb2a968 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Lizard: An Efficient Linearization Framework for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.602214Z

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 16387a6d-68c8-4751-a9c6-541c162cb208 · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models An Empirical Study of Mamba-based Language Models

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.581966Z

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 23ddb5e6-5329-4607-8922-b68f9245a007 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Lizard: An Efficient Linearization Framework for Large Language Models Linformer: Self-Attention with Linear Complexity

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.557120Z

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 96df8805-ed18-46bc-b81d-df2b446bc4c6 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Lizard: An Efficient Linearization Framework for Large Language Models Efficient Streaming Language Models with Attention Sinks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.598288Z

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 9ab14263-c418-4485-af53-8e6b2ea0a725 · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.552847Z

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 aff46cfe-5900-4f81-8eca-6fad1a22eacd · outbound

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

Lizard: An Efficient Linearization Framework for Large Language Models The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:42:04.590335Z

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 8e3887bc-3eb4-4155-85eb-603d11156688 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Lizard: An Efficient Linearization Framework for Large Language Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.548123Z

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 7b330d2e-396b-44d8-8fb1-da469e6b038b · outbound

This paper cites First, Lizard still relies on a strong pretrained backbone to achieve high quality.

Lizard: An Efficient Linearization Framework for Large Language Models First, Lizard still relies on a strong pretrained backbone to achieve high quality

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T04:42:59.936049Z

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 c5ef7aa2-6db0-451b-abdc-c882af4952e2 · outbound

This paper cites an unresolved cited work.

Lizard: An Efficient Linearization Framework for Large Language Models Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-19T04:42:59.930321Z

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 6f7f6a6c-f927-4d6b-b2aa-9a3f11edb6f6 · outbound

This paper cites Chungus Among Us.

Lizard: An Efficient Linearization Framework for Large Language Models Chungus Among Us

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T04:42:59.932979Z

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.

Pith citing papers

Observation bc89b400-019d-4c57-9abc-5255a367504f · inbound

The Key to Going Linear: Analysis-Driven Transformer Linearization cites this paper.

The Key to Going Linear: Analysis-Driven Transformer Linearization Lizard: An Efficient Linearization Framework for Large Language Models

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:45:50.816968Z

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.