Paper Citation Record · LEDGER

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.14217.

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

pith.paper-citation-record.v1
2506.14217 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:23:55.631073Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

36 of 36 outbound references displayed

verified exact0
verified fuzzy21
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eb904b32-4d1f-4680-bcd0-dea22805649a · outbound

This paper cites On the Robustness of Interpretability Methods.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift On the Robustness of Interpretability Methods

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:50.336227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02253864-8304-4404-bc13-70b9bf527633 · outbound

This paper cites A Multi-Policy Framework for Deep Learning-Based Fake News Detection.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift A Multi-Policy Framework for Deep Learning-Based Fake News Detection

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:23:56.026024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6f0da04-c1cd-4db2-87ab-fbc88093d77a · outbound

This paper cites Transformer interpretability beyond attention visualization.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Transformer interpretability beyond attention visualization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.679621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 742ac470-09b0-433d-add3-722f44171545 · outbound

This paper cites When are saliency maps trustworthy? In International Conference on Learning Representations (ICLR), 2023.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift When are saliency maps trustworthy? In International Conference on Learning Representations (ICLR), 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.667441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40e80b01-6f7e-40b6-b243-f47953881e8d · outbound

This paper cites and Hein, M.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift and Hein, M

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.648934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a917d0c-7c5e-40de-b6b2-edd332b8c80f · outbound

This paper cites Training verified learners with learned verifiers.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Training verified learners with learned verifiers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:51.093805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7a22688-f8a2-490e-8eab-79c972306900 · outbound

This paper cites an unresolved cited work.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:24:12.632430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa2d56ff-ea84-43de-9866-775e3102d92a · outbound

This paper cites AI2 : Safety and robustness certification of neural networks with abstract interpretation.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift AI2 : Safety and robustness certification of neural networks with abstract interpretation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.616916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ccf92d50-1e96-4b8d-aa57-0d52682469b2 · outbound

This paper cites an unresolved cited work.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:24:12.596279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11a75fc9-f3fe-4607-aae7-178853247fa4 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Explaining and Harnessing Adversarial Examples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:52.020756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31cacdb3-022d-4c27-b8e6-c1711fe6c12b · outbound

This paper cites Improving robustness without sacrificing accuracy via learned data augmentation.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Improving robustness without sacrificing accuracy via learned data augmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.579389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c16ef11-0287-422d-aab6-ea1957056617 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.559277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe1e6bb3-924e-4bd2-9845-6e267b5e2991 · outbound

This paper cites A benchmark for interpretability methods in deep neural networks.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift A benchmark for interpretability methods in deep neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.539819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2ffd8ab-c0b6-4f89-ab29-25ce1b54fc99 · outbound

This paper cites Benchmark for evaluating saliency methods.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Benchmark for evaluating saliency methods

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.514638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a575e89-f343-4071-b1b1-558d28fb56ad · outbound

This paper cites The complete verification of neural networks: the eran approach.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift The complete verification of neural networks: the eran approach

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.492138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13aa7c62-7e58-4ce6-aa04-14893b02f09d · outbound

This paper cites Sanity Simulations for Saliency Methods.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Sanity Simulations for Saliency Methods

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:53.035334Z

Source-reported events for the cited work

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Observation 9169b464-fb00-4fa0-9473-dd6b9e0ae639 · outbound

This paper cites T., Dähne, S., and Erhan, D.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift T., Dähne, S., and Erhan, D

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.471185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2121133-f6bf-4206-b2f1-416f426a7661 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:53.297134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab1d656-2a97-4f94-8f73-6122c8d41acc · outbound

This paper cites A Physics-Based Hybrid Dynamical Model of Hysteresis in Polycrystalline Shape Memory Alloy Wire Transducers.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift A Physics-Based Hybrid Dynamical Model of Hysteresis in Polycrystalline Shape Memory Alloy Wire Transducers

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:23:55.809284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da320906-879a-4c15-86de-55baef3b6347 · outbound

This paper cites Centered kernel alignment losses for saliency method faithfulness.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Centered kernel alignment losses for saliency method faithfulness

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.445441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84fce78b-0391-4050-9b85-89c4ac8ff234 · outbound

This paper cites Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4a70117-7445-4065-bd05-0dcc8167812f · outbound

This paper cites Evaluating the visualization of what a deep neural network has learned.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Evaluating the visualization of what a deep neural network has learned

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.351917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0b6bc5a-fb32-417c-a18a-49305be9a3f2 · outbound

This paper cites Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.231022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a474b5ab-a71b-4f5a-bfe8-3d84dcd887a5 · outbound

This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Deep inside convolutional networks: Visualising image classification models and saliency maps

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:12.107351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 32335874-5767-406d-b366-b223bfa15e12 · outbound

This paper cites An abstract domain for certifying neural networks.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift An abstract domain for certifying neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:11.798922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14e7e4d0-bd27-4421-9855-49f0bc20f01f · outbound

This paper cites and Feizi, S.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift and Feizi, S

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:58.066255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53ef675c-c601-4d15-899a-bbe80524a2f1 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift SmoothGrad: removing noise by adding noise

Reference 27

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Source-reported events for the cited work

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Observation 01327b5a-d2d6-4ac8-8245-6149e3fb6039 · outbound

This paper cites Axiomatic attribution for deep networks.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Axiomatic attribution for deep networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:57.742593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be47ed0b-734e-4678-bd83-c11df9f4ea8c · outbound

This paper cites Z., Lin, C.-J., and Hsieh, C.-J.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Z., Lin, C.-J., and Hsieh, C.-J

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:57.366971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1437161-04f4-4240-a3b2-afe4b087c443 · outbound

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TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:23:56.982814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff62d5c9-91f8-4aed-bf53-420cd11bfd5c · outbound

This paper cites an unresolved cited work.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:23:56.679493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 160cdfae-401e-43e6-8afe-0796c08fa585 · outbound

This paper cites On the faithfulness and reliability of saliency explanations.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift On the faithfulness and reliability of saliency explanations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:56.506184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9686b0f-ca16-4c0e-bbea-d8d96e18e1da · outbound

This paper cites Efficient neural network verification with Auto-LiRPA : Towards scalable certified defense.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Efficient neural network verification with Auto-LiRPA : Towards scalable certified defense

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:56.359398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e026bc7-219d-48e8-8936-3668d2e2f9ba · outbound

This paper cites Towards Stable and Efficient Training of Verifiably Robust Neural Networks.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Towards Stable and Efficient Training of Verifiably Robust Neural Networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:55.459642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baa955b1-d652-40dd-bea7-941c02cc91fe · outbound

This paper cites Towards certified robustness of real-world neural networks.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Towards certified robustness of real-world neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:23:56.210835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9dc3ee3-ba89-4f3f-85d2-13141a4814c2 · outbound

This paper cites write newline.

TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift write newline

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:55.631073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.