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Paper Citation Record · LEDGER

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers

As of 21 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2509.00935.

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

pith.paper-citation-record.v1
2509.00935 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:07:40.860889Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 543f5233-02c4-4b56-9cc3-523bf977d1bc · outbound

This paper cites DeciMamba: Exploring the Length Extrapolation Potential of Mamba.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers DeciMamba: Exploring the Length Extrapolation Potential of Mamba

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.814395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.814395Z digest=sha256:43633ac08d545116917420e2f61644115c8de0171a027cfb2f0fea3494d613a7

Observation 7ff82415-d74f-4690-aa1e-d2c1e7c09d44 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.818972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.818972Z digest=sha256:e4d8859f23c3c3448ccc2ae62909eb5f48fb3059a091f0a35506873569c0d260

Observation c9829234-a77b-4e8f-9937-9773c52a7f37 · outbound

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

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.823648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.823648Z digest=sha256:1f93b2dd2901d62aa3bf7e91a704b9f808c73d5c9e4663e0a7be601629642d20

Observation ab496551-37d8-489b-aea7-7f600b15f84a · outbound

This paper cites Reformer: The Efficient Transformer.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Reformer: The Efficient Transformer

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.833122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.833122Z digest=sha256:7a1b5184510269aa0695473673340f572f2b3667a3006cf6aebf4c665ec4d861

Observation 1e0edd95-3a4f-46c6-862c-3ed0e369d675 · outbound

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

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Jamba: A Hybrid Transformer-Mamba Language Model

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.837650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.837650Z digest=sha256:f43384ff8a09ef8d71a0d5abbd918df359045a1597b5dfdb06ce44a047fa24b1

Observation 32dfe37a-6188-4150-9353-bf11be0219ca · outbound

This paper cites Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.842500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.842500Z digest=sha256:0d715ff011d9bbe4f4c4ce2f528b0974f1234631d2bc05db69b359116ebb2abb

Observation db146689-26c6-4744-b3b2-cd40b4767e83 · outbound

This paper cites Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.851777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.851777Z digest=sha256:367aa28a8608be73fccc385b86b16f43976bd64fc7341c2a0d821ff1f9ed2a8d

Observation 96b141a4-03e3-4330-a7b7-225a202f226f · outbound

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

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.856419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.856419Z digest=sha256:a0d712551b942e0e1665ec8bba805f915e2c7385e616e9c42c1753ca0fa7f4c2

Observation b68bbc81-3d7a-486b-aa51-72c095e77cc8 · outbound

This paper cites LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.860889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.860889Z digest=sha256:4da791f31bab602cb5e5ac1ad5cd079044186e78138fee370c0572e9255c626f

Observation 09380d05-f3d8-417c-beeb-28a88431d4c8 · outbound

This paper cites Longformer: The Long-Document Transformer.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Longformer: The Long-Document Transformer

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.809712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.809712Z digest=sha256:0738f23b1a01963955a7cc241c26dddcca88df2975c6861eae31cb965becc2b6

Observation 11494bb2-003d-4ad6-8076-881c6277b9b8 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Efficiently Modeling Long Sequences with Structured State Spaces

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.828415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.828415Z digest=sha256:ceeec300acf7e05dba44fcddca8cce06d5aa33274115caae2cc059933f72877e

Observation 57dd4e0b-429f-4a96-a50e-5d34307d5166 · outbound

This paper cites https://www.anthropic.com/index/ introducing-claude.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers https://www.anthropic.com/index/ introducing-claude

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:07:41.094207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T13:07:40.800147Z digest=sha256:0741e29c1b678a16a04764e98168c50950bbb2d2144931b51694faeb18dd92f4

Observation b236f644-51fb-4966-83ca-4ba63d6cf19f · outbound

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

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers Simple linear attention language models balance the recall-throughput tradeoff

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.805014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.805014Z digest=sha256:84323b9837dc94a586c6e53a365ae0b710a89fab7eda4d2263f27547bb30ec86

Observation d22d9d7d-a7c5-4080-af3d-869c9601e213 · outbound

This paper cites The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs.

SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-05T13:07:40.846974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:07:40.846974Z digest=sha256:b24497e5b9c5750340318c6d7fdc827b1c65b7071ef59403601775fd67b94c93

Pith citing papers

No inbound Pith citation observations are available.