Paper Citation Record · LEDGER

Subquadratic Algorithms and Hardness for Attention with Any Temperature

As of 10 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2505.14840.

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

pith.paper-citation-record.v1
2505.14840 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:56.038504Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:10:59.474337Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:21:02.595394Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation cf9be8d4-929c-47ea-805f-5b2c3def07fb · outbound

This paper cites Optimal-degree polynomial approximations for exponentials and gaussian kernel density estimation.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Optimal-degree polynomial approximations for exponentials and gaussian kernel density estimation

Reference 1

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Observation eccc12aa-db0c-4c9f-a835-8fbd3ca266d5 · outbound

This paper cites More asymmetry yields faster matrix multiplication.

Subquadratic Algorithms and Hardness for Attention with Any Temperature More asymmetry yields faster matrix multiplication

Reference 2

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Observation 007036b9-dab1-4023-9d41-6dfc7128b05b · outbound

This paper cites Agarwal, Herbert Edelsbrunner, Otfried Schwarzkopf, and Emo Welzl.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Agarwal, Herbert Edelsbrunner, Otfried Schwarzkopf, and Emo Welzl

Reference 3

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Observation d27eb968-079b-41eb-bcfd-ecab3f0e7b28 · outbound

This paper cites Finer-grained hardness of kernel density estimation.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Finer-grained hardness of kernel density estimation

Reference 4

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Observation 63f53770-77d3-45d4-b035-19fa442e27e1 · outbound

This paper cites Fast attention requires bounded entries.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Fast attention requires bounded entries

Reference 5

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Observation 636e0c95-4aee-498a-ae0c-ceeb56d11482 · outbound

This paper cites The Fine-Grained Complexity of Gradient Computation for Training Large Language Models.

Subquadratic Algorithms and Hardness for Attention with Any Temperature The Fine-Grained Complexity of Gradient Computation for Training Large Language Models

Reference 6

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Reference 7

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Observation 85f6b080-20cf-4383-aace-4f2a271e4fcf · outbound

This paper cites More applications of the polynomial method to algorithm design.

Subquadratic Algorithms and Hardness for Attention with Any Temperature More applications of the polynomial method to algorithm design

Reference 8

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This paper cites More applications of the polynomial method to algorithm design.

Subquadratic Algorithms and Hardness for Attention with Any Temperature More applications of the polynomial method to algorithm design

Reference 9

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Observation 4fdf8855-638b-4b31-993c-ff75e112c908 · outbound

This paper cites Fundamental limitations on subquadratic alternatives to transformers.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Fundamental limitations on subquadratic alternatives to transformers

Reference 10

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Observation 3c46e104-686a-4983-8536-697d30f67d93 · outbound

This paper cites Edit distance cannot be computed in strongly subquadratic time (unless SETH is false).

Subquadratic Algorithms and Hardness for Attention with Any Temperature Edit distance cannot be computed in strongly subquadratic time (unless SETH is false)

Reference 11

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Subquadratic Algorithms and Hardness for Attention with Any Temperature Unresolved cited work

Reference 12

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Observation 0b4b1022-fedd-4ef0-99b3-892349274850 · outbound

This paper cites Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models

Reference 13

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Observation 420a362f-9cb1-4d1a-bd2b-56333775d765 · outbound

This paper cites Scatterbrain: Unifying sparse and low-rank attention.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Scatterbrain: Unifying sparse and low-rank attention

Reference 14

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Observation 71d2839d-0e94-4490-bb2e-e77f49194bb9 · outbound

This paper cites On the hardness of approximate and exact (bichromatic) maximum inner product.

Subquadratic Algorithms and Hardness for Attention with Any Temperature On the hardness of approximate and exact (bichromatic) maximum inner product

Reference 15

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Subquadratic Algorithms and Hardness for Attention with Any Temperature Unresolved cited work

Reference 16

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Observation 4ead6a3c-60a7-464e-8b30-b6ab0957472e · outbound

This paper cites Colwell, and Adrian Weller.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Colwell, and Adrian Weller

Reference 17

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Observation be6b873c-b514-4a07-822b-06eefd18f82e · outbound

This paper cites Chan and Ryan Williams.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Chan and Ryan Williams

Reference 18

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Observation b28b81d7-f8dd-4166-96a4-f4bdbcecfbd9 · outbound

This paper cites Approximation algorithms for min-distance problems in dags.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Approximation algorithms for min-distance problems in dags

Reference 19

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Observation 477ac53f-f88c-4aa6-ab43-da5744a65aa8 · outbound

This paper cites A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time.

Subquadratic Algorithms and Hardness for Attention with Any Temperature A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Reference 20

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This paper cites Fast quantum algorithm for attention computation.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Fast quantum algorithm for attention computation

Reference 21

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Observation 799a7b38-a586-4eeb-b831-df2531f5fef1 · outbound

This paper cites Differentially Private Attention Computation.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Differentially Private Attention Computation

Reference 22

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Observation 12f3d788-0006-4ce1-965a-49a7a0f3a83f · outbound

This paper cites Hyperattention: Long-context attention in near-linear time.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Hyperattention: Long-context attention in near-linear time

Reference 23

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Observation c5a46af3-c0b8-46df-9430-68a207c9b6b8 · outbound

This paper cites Singular value decomposition (svd).

Subquadratic Algorithms and Hardness for Attention with Any Temperature Singular value decomposition (svd)

Reference 24

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Observation f33c69ca-bbbc-4449-88b3-0cc493bd2fc1 · outbound

This paper cites Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries

Reference 25

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Observation 96368467-d70c-4dca-ae4e-bc96ba646f6b · outbound

This paper cites On the complexity of k-sat.

Subquadratic Algorithms and Hardness for Attention with Any Temperature On the complexity of k-sat

Reference 26

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Observation f1a1b9e0-a91d-42a1-8389-6650a5f42e80 · outbound

This paper cites Dynamic temperature scaling in contrastive self-supervised learning for sensor-based human activity recognition.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Dynamic temperature scaling in contrastive self-supervised learning for sensor-based human activity recognition

Reference 27

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Observation 4bdda98e-8538-444b-af14-dfdacc69668d · outbound

This paper cites Temperature schedules for self-supervised contrastive methods on long-tail data.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Temperature schedules for self-supervised contrastive methods on long-tail data

Reference 28

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Observation aebff8fb-1c35-4dd1-a2fc-0b7d5e984509 · outbound

This paper cites Reformer: The efficient transformer.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Reformer: The efficient transformer

Reference 29

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Observation c6017b96-d216-4211-a165-0b5c5ed760b5 · outbound

This paper cites Polysketchformer: Fast transformers via sketching polynomial kernels.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Polysketchformer: Fast transformers via sketching polynomial kernels

Reference 30

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Observation ac6abbbd-40e5-4ae4-a7a2-464ba15f3ba9 · outbound

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

Subquadratic Algorithms and Hardness for Attention with Any Temperature Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 31

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This paper cites On the computational complexity of self-attention.

Subquadratic Algorithms and Hardness for Attention with Any Temperature On the computational complexity of self-attention

Reference 32

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Observation 86ef437b-7541-4a68-be29-d5eb9070577f · outbound

This paper cites Efficient partition trees.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Efficient partition trees

Reference 33

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Observation e6c2f0df-64b6-414c-851c-0423c5980030 · outbound

This paper cites Dynamically Scaled Temperature in Self-Supervised Contrastive Learning.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Dynamically Scaled Temperature in Self-Supervised Contrastive Learning

Reference 34

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This paper cites Fine-tuning language models with just forward passes.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Fine-tuning language models with just forward passes

Reference 35

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This paper cites Trainable transformer in transformer.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Trainable transformer in transformer

Reference 36

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This paper cites Can contrastive learning avoid shortcut solutions? In Advances in Neural Information Processing Systems NeurIPS 34 , 2021.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Can contrastive learning avoid shortcut solutions? In Advances in Neural Information Processing Systems NeurIPS 34 , 2021

Reference 37

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Observation ad99f3c8-3d9d-4cfa-ab88-6322479b0cad · outbound

This paper cites The singular value decomposition (svd) and low-rank matrix approximations.

Subquadratic Algorithms and Hardness for Attention with Any Temperature The singular value decomposition (svd) and low-rank matrix approximations

Reference 38

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Observation 8eb03f50-1dfd-488e-b2bb-85b5af44c0ba · outbound

This paper cites Fast approximation algorithms for the diameter and radius of sparse graphs.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Fast approximation algorithms for the diameter and radius of sparse graphs

Reference 39

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Observation d8d15c9d-a049-4b43-a4a0-a590fd699371 · outbound

This paper cites Understanding transformer reasoning capabilities via graph algorithms.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Understanding transformer reasoning capabilities via graph algorithms

Reference 40

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Observation 1e9b2544-6d54-492a-96c9-5de3a69add48 · outbound

This paper cites Hsu, and Matus Telgarsky.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Hsu, and Matus Telgarsky

Reference 41

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This paper cites Transformers, parallel computation, and logarithmic depth.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Transformers, parallel computation, and logarithmic depth

Reference 42

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Observation 54cbe930-4419-42da-8403-23df6ae03bf4 · outbound

This paper cites I / O complexity of attention, or how optimal is F lash A ttention? In Proceedings of the 41st International Conference on Machine Learning , 2024.

Subquadratic Algorithms and Hardness for Attention with Any Temperature I / O complexity of attention, or how optimal is F lash A ttention? In Proceedings of the 41st International Conference on Machine Learning , 2024

Reference 43

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This paper cites Solving attention kernel regression problem via pre-conditioner.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Solving attention kernel regression problem via pre-conditioner

Reference 44

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Observation 96a1a9e8-9f30-4ab7-a05a-02346c063cbc · outbound

This paper cites All pairs shortest paths in undirected graphs with integer weights.

Subquadratic Algorithms and Hardness for Attention with Any Temperature All pairs shortest paths in undirected graphs with integer weights

Reference 45

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Observation fa768160-b9b3-4b0c-8c0d-ad5a47d6e672 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:57.635726Z

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Observation 3cb3c2a5-da3a-44a7-b740-a6787bee2fb0 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:57.346080Z

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Observation d18e179c-ad43-426c-b11e-2ba09960552f · outbound

This paper cites A new algorithm for optimal constraint satisfaction and its implications.

Subquadratic Algorithms and Hardness for Attention with Any Temperature A new algorithm for optimal constraint satisfaction and its implications

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:57.054364Z

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Observation 8f264577-561f-4f79-9797-4b50ff79d5cf · outbound

This paper cites Ryan Williams.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Ryan Williams

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.936522Z

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Observation 2cecae4b-471b-40ee-91c0-f978a17b8cfb · outbound

This paper cites Understanding the behaviour of contrastive loss.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Understanding the behaviour of contrastive loss

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.763351Z

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Observation f1fe6883-1b81-48c4-80e0-712940b5de1e · outbound

This paper cites Exploring the Impact of Temperature Scaling in Softmax for Classification and Adversarial Robustness.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Exploring the Impact of Temperature Scaling in Softmax for Classification and Adversarial Robustness

Reference 51

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Observation e681278f-1bcd-435e-a8bb-e59bc83e1b75 · outbound

This paper cites On constructing minimum spanning trees in k-dimensional spaces and related problems.

Subquadratic Algorithms and Hardness for Attention with Any Temperature On constructing minimum spanning trees in k-dimensional spaces and related problems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.647695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9eab0b74-f8f8-4aa0-8d99-4d97292a4159 · outbound

This paper cites Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:55.833385Z

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Observation 8b4b64dc-4ecf-4041-bb73-bdaa610e13af · outbound

This paper cites Big bird: Transformers for longer sequences.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Big bird: Transformers for longer sequences

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.507359Z

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Observation 3accb36a-a310-4010-88b2-ca9386b7f40f · outbound

This paper cites Kdeformer: Accelerating transformers via kernel density estimation.

Subquadratic Algorithms and Hardness for Attention with Any Temperature Kdeformer: Accelerating transformers via kernel density estimation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:56.363767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ee2aab9-5aac-4e8b-b1e9-5acf4eee16eb · outbound

This paper cites All pairs shortest paths using bridging sets and rectangular matrix multiplication.

Subquadratic Algorithms and Hardness for Attention with Any Temperature All pairs shortest paths using bridging sets and rectangular matrix multiplication

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:56.038504Z

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Pith citing papers

Observation e89f4b95-e4dd-4266-b404-8d946c58019e · inbound

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse cites this paper.

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse Subquadratic Algorithms and Hardness for Attention with Any Temperature

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:59.474337Z

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Observation 063b1293-4e99-4560-8953-4c1d2dd07be4 · inbound

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions cites this paper.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Subquadratic Algorithms and Hardness for Attention with Any Temperature

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:21:02.673064Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.