Paper Citation Record · LEDGER

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 4 inbound Pith citation observations for arXiv:2507.07814.

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

pith.paper-citation-record.v1
2507.07814 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:46:51.764217Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:54:36.267061Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:27:09.092002Z

Reference resolution

35 of 35 outbound references displayed

verified exact2
verified fuzzy12
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3594d00-1a25-4d67-8587-820c6bf3428d · outbound

This paper cites Transformers and large language models for chemistry and drug discovery.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Transformers and large language models for chemistry and drug discovery

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.698643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 593c5071-175e-49d2-a001-657920763c47 · outbound

This paper cites NeoBERT: A Next-Generation BERT.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers NeoBERT: A Next-Generation BERT

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.508757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2058d93-9ceb-4653-a9e6-01baea4aee19 · outbound

This paper cites Language models are few-shot learners.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Language models are few-shot learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.515685Z

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Unavailable: canonical work link unavailable.

Observation 2e35bfe4-9551-431f-afd1-6be179ed9eb5 · outbound

This paper cites How Smooth Is Attention?.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers How Smooth Is Attention?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.523721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd72cfe1-87a2-4e3c-9f6c-732264a66ba2 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.530782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 836f1834-60fa-4c79-a28d-4e4b8b6a8abc · outbound

This paper cites Lipschitz normalization for self-attention layers with application to graph neural networks.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Lipschitz normalization for self-attention layers with application to graph neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.651632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7fb2a085-45bf-467b-b2aa-1cfc734151b8 · outbound

This paper cites How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:46:52.322276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47e84c70-25c0-49df-843d-2343c1fbfdbd · outbound

This paper cites Attention is not all you need: pure ttention loses rank doubly exponentially with depth.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Attention is not all you need: pure ttention loses rank doubly exponentially with depth

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.632975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 214dc63e-5efc-466d-909d-77c51c83d7c5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.561178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15e2e45f-101d-486b-b684-2129e296937c · outbound

This paper cites EDIT: Enhancing vision transformers by mitigating attention sink through an encoder-decoder architecture.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers EDIT: Enhancing vision transformers by mitigating attention sink through an encoder-decoder architecture

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.568304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca8ca0de-7a68-4dc9-86a9-d4f7bff1521e · outbound

This paper cites On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.575245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9d470f3-5826-4f34-bbcd-0278672a9848 · outbound

This paper cites Regularisation of neural networks by enforcing lipschitz continuity.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Regularisation of neural networks by enforcing lipschitz continuity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.583140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab018f7c-6959-40c0-89c5-65904106ae02 · outbound

This paper cites When Attention Sink Emerges in Language Models: An Empirical View.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers When Attention Sink Emerges in Language Models: An Empirical View

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.589737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0355dff0-b433-45e2-b30c-5f4e984bf3d3 · outbound

This paper cites Transformer training instability of softmax and lipschitz- kernel attentions, 2024.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Transformer training instability of softmax and lipschitz- kernel attentions, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.591981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8a3de07-4c47-4814-b2ab-2b3a239b7726 · outbound

This paper cites Horn and Charles R.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Horn and Charles R

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.571777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ad78ada-128d-4585-9df2-121cff3fa90c · outbound

This paper cites Specformer: Guarding vision transformer robustness via maximum singular value penalization.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Specformer: Guarding vision transformer robustness via maximum singular value penalization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.551786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 23b745b4-46bb-49aa-9d1a-daeb32a4bd09 · outbound

This paper cites MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.617612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0e34c15-83c5-4344-b393-7e4ce1e1f554 · outbound

This paper cites The lipschitz constant of self-attention.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers The lipschitz constant of self-attention

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.528380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f3567613-5416-4719-97eb-1010d25c25da · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Efficient memory management for large language model serving with pagedattention

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.507721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1c9e34e0-b300-430b-8da3-d647d635aa37 · outbound

This paper cites On controllable sparse alternatives to softmax.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers On controllable sparse alternatives to softmax

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.484663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ece4ca1d-7c25-426c-a4c2-237df4ca70a9 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.646057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50dc4c1a-729e-4e73-b56c-9e13a115b502 · outbound

This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.653891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90243db6-6bbe-4f33-be03-32701d509418 · outbound

This paper cites LipsFormer: Introducing Lipschitz Continuity to Vision Transformers.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers LipsFormer: Introducing Lipschitz Continuity to Vision Transformers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.661357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd05e904-0c7d-441b-b645-5938aa02ec35 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Learning transferable visual models from natural language supervision

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.669016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 249f3fa9-c047-4022-ba70-e42969c807a8 · outbound

This paper cites Revisiting Over-smoothing in BERT from the Perspective of Graph.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Revisiting Over-smoothing in BERT from the Perspective of Graph

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.676269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76611c81-9483-45bf-96fa-36aad1e8a803 · outbound

This paper cites Springer Science & Business Media, 1997.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Springer Science & Business Media, 1997

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.444528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93e4be0d-7723-4ca8-8b3c-5d254ff02481 · outbound

This paper cites Attention is all you need.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Attention is all you need

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.689831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 833b3936-1613-470b-8e76-90830b7e6893 · outbound

This paper cites Adversarial Demonstration Attacks on Large Language Models.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Adversarial Demonstration Attacks on Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.696831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f5904bf-327c-4300-8307-c04fb8544ce4 · outbound

This paper cites On the Role of Attention Masks and LayerNorm in Transformers.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers On the Role of Attention Masks and LayerNorm in Transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.705543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c586bff1-31be-4d80-a846-8c5ff3c94304 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Efficient Streaming Language Models with Attention Sinks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.711799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cc3a302-17b0-48ef-b3d2-678399327dbe · outbound

This paper cites Learning Physical Simulation with Message Passing Transformer.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Learning Physical Simulation with Message Passing Transformer

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:46:51.890098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 846e949f-a265-4aaf-9834-d21fe8097a49 · outbound

This paper cites Differential Transformer.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Differential Transformer

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.742088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b92fdbad-857d-460c-94ec-5f69ee86a973 · outbound

This paper cites CutMix: Regularization strategy to train strong classifiers with localizable features.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers CutMix: Regularization strategy to train strong classifiers with localizable features

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.412232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f6b0cb8a-7d48-420d-bcdc-201a7b09c4a6 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers mixup: Beyond Empirical Risk Minimization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.757430Z

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Observation 064b6b5e-5cf2-4c0f-be14-d2eedf4177cd · outbound

This paper cites Exact” are averaged across the CIFAR-100 validation set. “Exact.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers Exact” are averaged across the CIFAR-100 validation set. “Exact

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:46:52.391208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 433f8d19-c98a-4500-a95b-2db8facdfab6 · inbound

Principles of Lipschitz continuity in neural networks cites this paper.

Principles of Lipschitz continuity in neural networks Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

Reference 225

Resolution: unresolved; no resolver link, observed 2026-08-03T04:54:36.267061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b25e135-81a5-4a00-9d06-5c962785b0d8 · inbound

A Mechanistic Analysis of Looped Reasoning Language Models cites this paper.

A Mechanistic Analysis of Looped Reasoning Language Models Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-08-10T01:13:53.509299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 24787a95-7869-4e9b-b0e2-3d70b3323fad · inbound

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion cites this paper.

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-08-10T01:13:53.509299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 429e813a-1482-47c7-9153-d81e9177abca · inbound

Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching cites this paper.

Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-08-10T01:13:53.509299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.