Paper Citation Record · LEDGER

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2511.20102.

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

pith.paper-citation-record.v1
2511.20102 v4

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T20:26:58.841866Z

measured 40 of 40 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:55:55.168098Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy0
unresolved38
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2a356653-4ea8-4e1b-a107-deca93b04064 · outbound

This paper cites RepoCoder: Repository-level code completion through iterative retrieval and generation.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space RepoCoder: Repository-level code completion through iterative retrieval and generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.729402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51e73469-61e7-4e73-8154-10c059e3cb70 · outbound

This paper cites SWE-bench: Can language models resolve real-world github issues? InThe Twelfth International Conference on Learning Representations, 2024.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space SWE-bench: Can language models resolve real-world github issues? InThe Twelfth International Conference on Learning Representations, 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.733410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a359517d-e8fe-49c5-b6dc-7cf31dcf5dd3 · outbound

This paper cites OpenAI o1 System Card.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space OpenAI o1 System Card

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.736622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfec3f38-2d17-445f-88db-b67548091657 · outbound

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

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.740340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93ae988c-d204-4e76-8d44-92f483953e53 · outbound

This paper cites DeepResearcher: Scaling deep research via reinforcement learning in real-world environments.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space DeepResearcher: Scaling deep research via reinforcement learning in real-world environments

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.743422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c23ae301-0286-43d0-98de-a0abe18a4c88 · outbound

This paper cites Qwen2.5-1M Technical Report.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Qwen2.5-1M Technical Report

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.746500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4378f1c6-97d9-46ba-89de-016d8a5aae22 · outbound

This paper cites Efficient attention mechanisms for large language models: A survey, 2025.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Efficient attention mechanisms for large language models: A survey, 2025

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.750088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4978751-50b7-4bae-b1ff-a57c17ba0049 · outbound

This paper cites InfLLM: Training-free long-context extrapolation for LLMs with an efficient context memory.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space InfLLM: Training-free long-context extrapolation for LLMs with an efficient context memory

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.752937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc1ec184-7f2e-44bc-9dbd-40d71ecb1d68 · outbound

This paper cites Efficient streaming language models with attention sinks.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Efficient streaming language models with attention sinks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.755690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f842d74-6e69-4460-ae91-d6c3290f0e49 · outbound

This paper cites Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, and Lili Qiu.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, and Lili Qiu

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.758559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ba24532-4334-4096-a549-310755b3a7a6 · outbound

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

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Native sparse attention: Hardware-aligned and natively trainable sparse attention

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.761292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6471f1e-407f-4319-97ba-bc373cdd48ae · outbound

This paper cites MoBA: Mixture of block attention for long-context LLMs.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space MoBA: Mixture of block attention for long-context LLMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.764159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd7dc74a-1242-4fe0-857c-5dbcd6c16cc0 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Generating Long Sequences with Sparse Transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.767079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb55ba6f-abab-4106-a3a9-4c5939cacc30 · outbound

This paper cites Longformer: The Long-Document Transformer.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Longformer: The Long-Document Transformer

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.770120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ece932-7827-4032-ad23-bd9a32b71982 · outbound

This paper cites Language models are few-shot learners.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Language models are few-shot learners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.773102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c69861b1-2179-4760-8eaf-42ed8d239f7a · outbound

This paper cites XAttention: Block sparse attention with antidiagonal scoring.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space XAttention: Block sparse attention with antidiagonal scoring

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.776015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa396816-1a8b-4ea2-8b8e-59f291aa802d · outbound

This paper cites Quest: Query- aware sparsity for efficient long-context llm inference.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Quest: Query- aware sparsity for efficient long-context llm inference

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.778802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 468b92de-4e2b-4c33-8ac4-e46054bc859b · outbound

This paper cites Spargeattention: Accurate and training-free sparse attention accelerating any model inference.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Spargeattention: Accurate and training-free sparse attention accelerating any model inference

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.781744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 978f0d5d-f254-40f8-ac1e-6b73528c029b · outbound

This paper cites Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.784475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185cfc53-2405-41ba-b190-71e690cbda3b · outbound

This paper cites Infllm-v2: Dense-sparse switchable attention for seamless short-to-long adaptation, 2025.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Infllm-v2: Dense-sparse switchable attention for seamless short-to-long adaptation, 2025

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.787691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f39bbbf-ee7d-4fbc-a758-93adb4117053 · outbound

This paper cites Deepseek-v3.2-exp: Boosting long-context efficiency with deepseek sparse attention, 2025.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Deepseek-v3.2-exp: Boosting long-context efficiency with deepseek sparse attention, 2025

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.790545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 343d7faa-8490-49bb-9f08-55ebaa653f43 · outbound

This paper cites Neural discrete representation learning.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Neural discrete representation learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.793340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14344ca0-4f15-4f08-bbd9-ad9936181a0a · outbound

This paper cites Training language models to follow instructions with human feedback.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Training language models to follow instructions with human feedback

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.796018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312e74e3-1f68-4acf-a347-dbd0c204bdf3 · outbound

This paper cites Fast R-CNN.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Fast R-CNN

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.798692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1badb153-3a0a-4ec1-8e95-8cb7864e031e · outbound

This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Flashattention-2: Faster attention with better parallelism and work partitioning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.802026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afdb395c-767f-4c3a-bb79-6fe0657abf8c · outbound

This paper cites The Llama 3 Herd of Models.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space The Llama 3 Herd of Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.804694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d21ea959-c08b-4e19-8e01-fd44a2d547cc · outbound

This paper cites SmolLM2: When smol goes big — data-centric training of a fully open small language model.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space SmolLM2: When smol goes big — data-centric training of a fully open small language model

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.807531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64fa50e5-efc8-44ec-acc1-c555546edad3 · outbound

This paper cites an unresolved cited work.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.810627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 041fc075-58e6-4f1c-a1b2-07adaed3c53b · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.813566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69759f69-382a-4a95-8de2-df1234edce60 · outbound

This paper cites HellaSwag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space HellaSwag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.816575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e28ee209-c4fb-4255-a529-ab3ddb0ca3e6 · outbound

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

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.819420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e76a162-02ee-4d87-bd99-9e2cf59417b6 · outbound

This paper cites Pointer sentinel mixture models.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Pointer sentinel mixture models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.822433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb07d4e4-d60f-4c62-9ea2-d9da5a91b540 · outbound

This paper cites LongBench: A bilingual, multitask benchmark for long context understanding.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space LongBench: A bilingual, multitask benchmark for long context understanding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.825242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 318f6f40-9ec1-445c-a24d-0f9b837a426c · outbound

This paper cites RULER: What’s the real context size of your long-context language models? InFirst Conference on Language Modeling, 2024.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space RULER: What’s the real context size of your long-context language models? InFirst Conference on Language Modeling, 2024

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.827903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8366c321-7f2b-42cc-835e-168c3340e697 · outbound

This paper cites Rae, Anna Potapenko, Siddhant M.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Rae, Anna Potapenko, Siddhant M

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.830546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fd507f8-a7bb-4933-ac4f-15bf8d910da7 · outbound

This paper cites Smith, and Mike Lewis.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Smith, and Mike Lewis

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.833629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 279b0f14-75d0-4847-8963-146fcfcac2ba · outbound

This paper cites Train short, test long: Attention with linear biases enables input length extrapolation.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Train short, test long: Attention with linear biases enables input length extrapolation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.836516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fc96259-379a-4c22-9238-f5d598e04f9d · outbound

This paper cites The language model evaluation harness, 07 2024.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space The language model evaluation harness, 07 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T20:26:58.839161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a62a12fa-367a-4f29-9af5-0eab09a17b81 · outbound

This paper cites Liger-kernel: Efficient triton kernels for LLM training.

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space Liger-kernel: Efficient triton kernels for LLM training

Reference 39

Resolution: malformed identifier; no resolver link, observed 2026-08-03T20:26:58.841866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8ad8611a-7984-4426-b3e8-ef3e1b5ac2bc · inbound

CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention cites this paper.

CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T02:55:55.168098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.