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Paper Citation Record · LEDGER

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing

As of 19 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.19368.

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

pith.paper-citation-record.v1
2607.19368 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:37:09.601762Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation 28148f91-9c41-46da-a185-6588eab65eef · outbound

This paper cites Generating long sequences with sparse transformers.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Generating long sequences with sparse transformers

Reference 1

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Observation 96aac12b-4901-4424-bd0d-dff597fba733 · outbound

This paper cites Big bird: Trans- formers for longer sequences.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Big bird: Trans- formers for longer sequences

Reference 2

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source=pdf_text observed=2026-08-02T11:37:06.187898Z digest=sha256:88b92e1fd7613a1f517e2ae7c3fac6336d995c4092c227f7dece4cc988c1f3c9

Observation 73239773-b574-470d-a348-4eccc9699909 · outbound

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

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 3

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Observation ea5ba4f6-d697-4302-8223-1e4873a1da70 · outbound

This paper cites Rethinking attention with performers.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Rethinking attention with performers

Reference 4

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source=pdf_text observed=2026-08-02T11:37:06.339911Z digest=sha256:b60693d2be3a327101ffbe27e98a04cb3620f146a6decc3e1bdae5dac1651729

Observation e5e789f1-f93e-4658-ad22-719fb5051437 · outbound

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

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 5

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source=pdf_text observed=2026-08-02T11:37:06.422652Z digest=sha256:5c4626f5aca1a69be675ec541a65240b5ab89337295b4d79c82106d6036c3636

Observation 48a88e29-c3b4-48bf-a587-8663bb43dcaf · outbound

This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing SnapKV: LLM Knows What You are Looking for Before Generation

Reference 6

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source=pdf_text observed=2026-08-02T11:37:06.554479Z digest=sha256:315b11443cc1909e73e747598cd3adf2bbc9b83d5a5f8b01d0f2812a95e769ce

Observation afda2078-55a6-4cc4-8611-c8a56bff268e · outbound

This paper cites H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Reference 7

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source=pdf_text observed=2026-08-02T11:37:06.681833Z digest=sha256:981bfdcf0d47d4974d59d2b02a6b661e7a6afae8faa47d0a897edae60f4c959f

Observation 667d0fbb-dee9-4cc3-a1bf-9ec9f2229dfa · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Efficient Streaming Language Models with Attention Sinks

Reference 8

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source=pdf_text observed=2026-08-02T11:37:06.818766Z digest=sha256:ff6a41674bfe9412b6563104935a77189fc8d00c1a4009538e91d200189c510e

Observation b0406970-d688-4c57-a030-8dbe36cf166a · outbound

This paper cites LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Reference 9

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source=pdf_text observed=2026-08-02T11:37:06.990703Z digest=sha256:916c3d78a3cb5411a7a2143e1d2d12556bef29fa47ada329b8ebd44512bd633d

Observation ef1e06b3-2472-40cf-a4b5-d094a7eea508 · outbound

This paper cites Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition

Reference 10

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source=pdf_text observed=2026-08-02T11:37:07.137742Z digest=sha256:c733b620837bf693cf37a60e3ab9079fc6c66880125de48dd82e0f6c69781c9f

Observation 383acf13-2b8c-47dc-9528-30bbbc18d761 · outbound

This paper cites Random features for large-scale kernel machines.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Random features for large-scale kernel machines

Reference 11

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Observation 5c67ff8d-0d7e-4016-bf70-0e1961cd7fe6 · outbound

This paper cites Longformer: The long-document trans- former.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Longformer: The long-document trans- former

Reference 12

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source=pdf_text observed=2026-08-02T11:37:07.374878Z digest=sha256:bd6d4a17871e6c515a8c914f4847ed55ef4b71fa052a46e9c2dc337bce5f3372

Observation 993aa43f-0dba-461a-8282-a186990c6b39 · outbound

This paper cites Linformer: Self- attention with linear complexity.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Linformer: Self- attention with linear complexity

Reference 13

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Observation 2dcda766-8b02-4677-9e45-c6b17d00dce2 · outbound

This paper cites Nyströmformer: A nyström-based algorithm for approximating self- attention.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Nyströmformer: A nyström-based algorithm for approximating self- attention

Reference 14

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source=pdf_text observed=2026-08-02T11:37:07.602041Z digest=sha256:44be3c22fe8cbce007d6c0451cb518e0afbb23a98a97fcee1de09c83a2043645

Observation b448ed3b-5ef9-4957-95c5-d4f3eb3c561e · outbound

This paper cites Universal Humanoid Motion Representations for Physics-Based Control.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Universal Humanoid Motion Representations for Physics-Based Control

Reference 15

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source=pdf_text observed=2026-08-02T11:37:07.699903Z digest=sha256:8f153815ce02878c19f8e2a1c0f3dc7b3e0cb61c9e219537b91863528d94683d

Observation b494edfa-de78-40f7-b727-7bdf014fe5de · outbound

This paper cites LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Reference 16

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source=pdf_text observed=2026-08-02T11:37:07.776703Z digest=sha256:0938eeb9751bf5ee1a12572a1c27b77969f6a22839585bc570d7788a310aa2ee

Observation 344c2ae5-8d46-4c86-9493-7eee9f163d32 · outbound

This paper cites LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

Reference 17

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Observation 929c06d1-2b03-4a98-ac7b-928a8df772c3 · outbound

This paper cites Unifying adjacency, Laplacian, and signless Laplacian theories.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Unifying adjacency, Laplacian, and signless Laplacian theories

Reference 18

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Observation 462c0bf6-1488-416a-bcf0-6da5ea5bb5bc · outbound

This paper cites Token merging: Your vit but faster.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Token merging: Your vit but faster

Reference 19

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source=pdf_text observed=2026-08-02T11:37:08.104808Z digest=sha256:a6ce79a335ae6f24b7166f481e9daa0c5fdc6e5a47a94f21723e01bcb3b5611c

Observation 4f16daca-a4b3-45cd-b4e4-916cf9905b31 · outbound

This paper cites Reformer: The efficient transformer.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Reformer: The efficient transformer

Reference 20

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source=pdf_text observed=2026-08-02T11:37:08.243776Z digest=sha256:f53044c01061db872b52cef78d0965f892b982edc59807e9faf46d32013a680e

Observation e0e94e7c-95f4-42e3-9cd9-2aef4255f40d · outbound

This paper cites SIAM, 2011.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing SIAM, 2011

Reference 21

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Observation b1fcd6f3-6e1b-44b2-a525-d9501405d087 · outbound

This paper cites Similarity estimation techniques from rounding algorithms.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Similarity estimation techniques from rounding algorithms

Reference 22

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Observation 89adde55-6e61-4217-8dfd-febc5b3d8ca6 · outbound

This paper cites Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

Reference 23

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source=pdf_text observed=2026-08-02T11:37:08.732017Z digest=sha256:1a6689251abc98e290895f09540da0a79fc2b7a5e5d261dd5b45e533c873f249

Observation fba98356-beb6-46c0-a2ae-013d4781fdb6 · outbound

This paper cites Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions

Reference 24

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source=pdf_text observed=2026-08-02T11:37:08.900874Z digest=sha256:63f96ca7c8a014bbda5bb4100fe2bd633a4c3c19d54fbc7f4cf8e7f65b065645

Observation 5e6df8b6-74c7-4bc4-9a46-2723e5b69a8d · outbound

This paper cites Mistral 7B.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Mistral 7B

Reference 25

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source=pdf_text observed=2026-08-02T11:37:09.019660Z digest=sha256:3896e9d89a6ca7b527ea10fb12246b11ab926f20636b3514b448a0ce3abf031e

Observation 56904154-2ce2-4623-8dc1-682a11c571b9 · outbound

This paper cites Qwen2 Technical Report.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Qwen2 Technical Report

Reference 26

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source=pdf_text observed=2026-08-02T11:37:09.206791Z digest=sha256:59f4f9b8289a9137dfb3b749baae139b9b45eda63b23249baf916a39ab175454

Observation 5ae8bc76-69ee-4a21-b46d-483c7609c1f2 · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 27

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source=pdf_text observed=2026-08-02T11:37:09.321469Z digest=sha256:140bbb5dae889019a913eea5f455df8d60ea8fd2cdfaee487d16a7fefc9b27e6

Observation 47876a50-eca3-412b-b036-b9c9a3ed8d0b · outbound

This paper cites an unresolved cited work.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Unresolved cited work

Reference 28

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Observation 17573305-f57a-4e4e-918f-aadea0ed83b2 · outbound

This paper cites Pointer sentinel mixture models.

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing Pointer sentinel mixture models

Reference 29

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Pith citing papers

No inbound Pith citation observations are available.