Pith. sign in

Paper Citation Record · LEDGER

Captum: A unified and generic model interpretability library for PyTorch

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 55 inbound Pith citation observations for arXiv:2009.07896.

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

pith.paper-citation-record.v1
2009.07896 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 55 of 55 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 55 of 55 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:37:46.123862Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

643
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 394d3a8c-9c1d-40ad-b289-c0cb643e548c · inbound

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions cites this paper.

Saliency Maps are Ambiguous: Analysis of Logical Relations on First and Second Order Attributions Captum: A unified and generic model interpretability library for PyTorch

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-10T15:27:09.432427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:27:09.432427Z digest=sha256:177e29ddf1a08f941d08900b6b2936760b8ca209e39489a78e84d39f011c4378

Observation be8a1a2b-da51-4155-91c0-cc3bb18b8c02 · inbound

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods cites this paper.

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods Captum: A unified and generic model interpretability library for PyTorch

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-09T10:15:24.480669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:15:24.480669Z digest=sha256:b103f0a28d34d3be6d6071b1c8d37dd7d878d05200a2bdf7ac850dbd37ed2df8

Observation 449e9497-2c01-431f-92a5-e161251161a2 · inbound

MANTIS: Detection of Zero-Day Malicious Domains Leveraging Low Reputed Hosting Infrastructure cites this paper.

MANTIS: Detection of Zero-Day Malicious Domains Leveraging Low Reputed Hosting Infrastructure Captum: A unified and generic model interpretability library for PyTorch

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-07T20:33:18.549117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T20:33:18.549117Z digest=sha256:fcee10dec4a61e690f85036cdeee71e6cb5cb54554ebe788dc14d1d995ad3cb3

Observation 7d32ac0f-906c-4aef-a2e4-20a09f087c9c · inbound

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition cites this paper.

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition Captum: A unified and generic model interpretability library for PyTorch

Reference 142

Resolution
unresolved
no resolver link, observed 2026-08-10T17:37:46.123862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:37:46.123862Z digest=sha256:2cef7aac6c689201bd2cd78111c029d61005102ed445fb3358120313e39b7a61

Observation 0cc1cb44-ca50-47e3-aeba-74542ddc3d0a · inbound

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models cites this paper.

On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models Captum: A unified and generic model interpretability library for PyTorch

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T11:15:13.833344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:15:13.833344Z digest=sha256:233ec0460a96172e9a0ce13484368fbdeb4f61126469f4c19379f8d07d4d2a31

Observation b23eb441-ecc0-431a-8836-90bf353fdcec · inbound

ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications cites this paper.

ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications Captum: A unified and generic model interpretability library for PyTorch

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:59.603146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:09:59.603146Z digest=sha256:c961b0fa1b1e7c68f98669dbbb2ed9c5e6d27d438fbaa536b4ebad18f5c3445d

Observation ff6668a4-5ad6-4df9-8b84-19ad188b4c25 · inbound

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations cites this paper.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Captum: A unified and generic model interpretability library for PyTorch

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T23:12:13.432211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:12:13.432211Z digest=sha256:fe90cb18cf4db314d8e2ba3fa405f759220c52023e35c84b70e65c107f26b618

Observation 4ee5c366-5f06-4853-a83e-5912c687fb29 · inbound

Model Fusion via Retrofitting cites this paper.

Model Fusion via Retrofitting Captum: A unified and generic model interpretability library for PyTorch

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T23:52:02.234785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:52:02.234785Z digest=sha256:d3eff4251c1f513658ccb0e2feb691bf8ea4d236c8e4d23fcf5e0c7b3a7692fd

Observation 56d7bae6-8bff-4f3a-b1a3-31283c101097 · inbound

Exploring Pose-based Sign Language Translation: Ablation Studies and Attention Insights cites this paper.

Exploring Pose-based Sign Language Translation: Ablation Studies and Attention Insights Captum: A unified and generic model interpretability library for PyTorch

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:11.266236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:11.266236Z digest=sha256:bd12477a03d6748393184867e0adeb561416d46ce6d9b9880fe450beca65d860

Observation a466ba9d-d454-449e-a776-d84a0aee5712 · inbound

DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values cites this paper.

DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values Captum: A unified and generic model interpretability library for PyTorch

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T20:38:17.355402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:38:17.355402Z digest=sha256:cb284dae6eac9478f6f94acefe6e9020aabec895b7bb00075a68ba862d9f6628

Observation d53c1976-c78f-48f8-aa54-701a67170368 · inbound

TRACE: Training and Inference-Time Interpretability Analysis for Language Models cites this paper.

TRACE: Training and Inference-Time Interpretability Analysis for Language Models Captum: A unified and generic model interpretability library for PyTorch

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T20:07:55.784952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:07:55.784952Z digest=sha256:2413596b8634b37732fdfae3037cf1a2c638032c20c65cd0f42fedb4fef26db3

Observation 94634029-be39-4215-9f86-7f3316e02ccf · inbound

PyG 2.0: Scalable Learning on Real World Graphs cites this paper.

PyG 2.0: Scalable Learning on Real World Graphs Captum: A unified and generic model interpretability library for PyTorch

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T15:03:07.539446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:03:07.539446Z digest=sha256:1ce77330335a917a55ce0db318bef280acadea1b216c403dedd25246b1066ef4

Observation 5a87f90d-3930-432f-aceb-a0d3943b2112 · inbound

POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage cites this paper.

POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage Captum: A unified and generic model interpretability library for PyTorch

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T12:04:44.906255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:04:44.906255Z digest=sha256:570925b1191d725c7d51d48eec8313bee3992bf96097caf1ee97c2f1e7490ccb

Observation 7de2baca-6426-449b-82a3-0dd558f66627 · inbound

DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations cites this paper.

DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations Captum: A unified and generic model interpretability library for PyTorch

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T04:23:30.405600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:23:30.405600Z digest=sha256:baa140269194f3705dcabf6336e86f9b618242657d224f4be91ce2c95eda824f

Observation fec56d22-86e1-44ca-9907-bfcaf4bcf414 · inbound

Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil cites this paper.

Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil Captum: A unified and generic model interpretability library for PyTorch

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T10:23:12.703222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T10:23:12.703222Z digest=sha256:eed73e03a5d686fc2c73301cdcbca5037a25aafb6c3afbc8bb316fdf422a7317

Observation 871338d7-f0db-495b-9b11-5da98b5fe558 · inbound

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches cites this paper.

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches Captum: A unified and generic model interpretability library for PyTorch

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-05T14:21:47.334755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:21:47.334755Z digest=sha256:9e64bbcbc95708544924570a3078fd0b74e6b51a80203a9445f68ced7cbaee5f

Observation ab252a9a-7cf1-4b8f-a554-b7b0f9bc8ce6 · inbound

AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum cites this paper.

AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum Captum: A unified and generic model interpretability library for PyTorch

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T16:06:48.857320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:06:48.857320Z digest=sha256:7edb8ac8f9c4cb0b6b4f9fc687cc008ef61287a5209ef5e66e2e84081c0d4d48

Observation 232a8608-dab6-4b60-9a49-fea26c25b462 · inbound

Rashomon in the Streets: Explanation Ambiguity in Scene Understanding cites this paper.

Rashomon in the Streets: Explanation Ambiguity in Scene Understanding Captum: A unified and generic model interpretability library for PyTorch

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-05T11:09:48.069017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:09:48.069017Z digest=sha256:e25ec66154c4d69d4221911392083dfe3c1ba7947b8d225ff8bc57d73a64b7bc

Observation a9d08b1c-c0a6-4fca-bc8e-e54d23dcc02b · inbound

An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification cites this paper.

An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification Captum: A unified and generic model interpretability library for PyTorch

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T10:49:55.811697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:49:55.811697Z digest=sha256:9e5fb6951aa634e2664396cddcf09ead312f44f08503b7c337d2b039d86f5c66

Observation 2ae08232-5c33-4cca-9edf-d10e70f77e1c · inbound

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction cites this paper.

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction Captum: A unified and generic model interpretability library for PyTorch

Reference 84

Resolution
unresolved
no resolver link, observed 2026-08-04T18:48:59.399215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:48:59.399215Z digest=sha256:4cebd1b1c47d3e1b659eb6e4f8ebceffec82f70e713073c5a48186606a7ab1f1

Observation a8ac3910-9732-4e89-839c-dab4077b65e1 · inbound

Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring cites this paper.

Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring Captum: A unified and generic model interpretability library for PyTorch

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-17T04:11:30.730038Z

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.

source=pdf_text observed=2026-05-17T04:09:30.442579Z digest=sha256:b6aa42704b44e5495bdac90401a5e4701804a1feac1575045a065e1035db8338

Observation 3c7f807e-f4dd-4307-8dc0-e3ecdc0bf1be · inbound

Interpreto: An Explainability Library for Transformers cites this paper.

Interpreto: An Explainability Library for Transformers Captum: A unified and generic model interpretability library for PyTorch

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-03T17:25:00.641433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T17:25:00.641433Z digest=sha256:09758f95a7242f9edfc0f12901bb8e7aa3cb444fc503025769aa68519fce9206

Observation f39c2e4a-6438-4640-a935-de63d47c65fb · inbound

X-SYS: A Reference Architecture for Interactive Explanation Systems cites this paper.

X-SYS: A Reference Architecture for Interactive Explanation Systems Captum: A unified and generic model interpretability library for PyTorch

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-15T22:46:46.037718Z

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.

source=pdf_text observed=2026-05-15T22:44:44.720768Z digest=sha256:3e77329be48b1ef43b1b327804de15046fecd5d83fdcc803880cccb01ed1b0cd

Observation fc210c33-526e-44a7-8b8e-7693bb03efad · inbound

MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection cites this paper.

MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection Captum: A unified and generic model interpretability library for PyTorch

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-15T18:06:25.224433Z

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.

source=pdf_text observed=2026-05-15T18:06:09.012289Z digest=sha256:de017dc57ed9b8142747b744a092b89893d9bebd08a86a0351680932f608bf02

Observation 0d5dc86e-b348-493e-8166-5bc0637d6939 · inbound

What is Missing? Explaining Neurons Activated by Absent Concepts cites this paper.

What is Missing? Explaining Neurons Activated by Absent Concepts Captum: A unified and generic model interpretability library for PyTorch

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-15T00:00:46.717506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T00:00:46.717506Z digest=sha256:12e7c9478f868047945d47b39f493eb3d4b5ffe766e0670379a8d298e6bcbfe7

Observation 72a83a12-8877-4008-8da4-fbfe50365e53 · inbound

Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions? cites this paper.

Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions? Captum: A unified and generic model interpretability library for PyTorch

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-13T21:33:18.506723Z

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.

source=pdf_text observed=2026-05-13T21:29:35.075916Z digest=sha256:064348bb033f53c54e74d926585a523f1818249a818a32d1fd28a43cdf6a6816

Observation 301160ec-3afc-4870-87e3-7a7f860bc7a5 · inbound

Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model cites this paper.

Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model Captum: A unified and generic model interpretability library for PyTorch

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-09T21:18:24.839333Z

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.

source=arxiv_source observed=2026-05-09T21:17:24.132264Z digest=sha256:16f8559583dc8f004409017b157cb4cb80452aa2f6251471c970481625881e78

Observation 2d8c27f5-92bf-4063-a91a-431ae92c97c3 · inbound

Modeling Subjective Urban Perception with Human Gaze cites this paper.

Modeling Subjective Urban Perception with Human Gaze Captum: A unified and generic model interpretability library for PyTorch

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:47:16.267281Z

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.

source=pdf_text observed=2026-05-09T19:12:54.038217Z digest=sha256:279d334bb13fa070e289f48c840c59bd64f15c832ab184cd7b52fff8d128ef99

Observation 0d85d1c6-d496-4a35-9047-780ffee8fb6a · inbound

Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models cites this paper.

Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models Captum: A unified and generic model interpretability library for PyTorch

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:50:39.537591Z

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.

source=arxiv_source observed=2026-05-08T18:06:16.734687Z digest=sha256:45fbff55ff9f168672f77092800ad3230733c448cb972d8296755e813b749152

Observation 120959ae-135f-430a-92f0-452f560f28fa · inbound

Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality cites this paper.

Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality Captum: A unified and generic model interpretability library for PyTorch

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:26.165770Z

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.

source=pdf_text observed=2026-05-12T04:15:54.437833Z digest=sha256:e688b98c84a6425a2b88736453dc66d9f1691de10520650c02966ab53ca4f596

Observation 0f9fe856-3481-48aa-9f44-c62bf7520956 · inbound

Enabling Performant and Flexible Model-Internal Observability for LLM Inference cites this paper.

Enabling Performant and Flexible Model-Internal Observability for LLM Inference Captum: A unified and generic model interpretability library for PyTorch

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:17:27.974805Z

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.

source=pdf_text observed=2026-05-13T07:17:13.823853Z digest=sha256:2f06ac648b53c8cf2513a80b7ce5413ac428688a7f82d7e11de496f465747dfe

Observation a0b53bd3-089a-4ceb-a608-33d73ef37afa · inbound

Instructions Shape Production of Language, not Processing cites this paper.

Instructions Shape Production of Language, not Processing Captum: A unified and generic model interpretability library for PyTorch

Reference 246

Resolution
verified exact
arxiv_id, observed 2026-05-13T03:12:09.637783Z

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.

source=arxiv_source observed=2026-05-13T03:09:02.902912Z digest=sha256:e33a662bdbfe536f3d24265474113391c80e06678d89b427cd395b416efa6151

Observation 7d3e89a3-38e3-42a1-a4c6-05d50c3c828f · inbound

Instructions Shape Production of Language, not Processing cites this paper.

Instructions Shape Production of Language, not Processing Captum: A unified and generic model interpretability library for PyTorch

Reference 246

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:02:58.766062Z

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.

source=arxiv_source observed=2026-05-14T21:02:02.135970Z digest=sha256:65163ea7a5e59657f29c74b9d12e8479a2d19b155565ac2b3aea6f4a385e4485

Observation 03bb2df3-9733-4949-9ea6-4be144ecdff7 · inbound

Many-Shot CoT-ICL: Making In-Context Learning Truly Learn cites this paper.

Many-Shot CoT-ICL: Making In-Context Learning Truly Learn Captum: A unified and generic model interpretability library for PyTorch

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:17:50.528605Z

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.

source=arxiv_source observed=2026-05-14T19:15:44.379686Z digest=sha256:9b46d2d2cc5fba17987a45f9bf7f349a83f911e07071bd118775a0d05940595d

Observation 45eebebc-52f1-4905-a48e-e761e2aaf954 · inbound

AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks cites this paper.

AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks Captum: A unified and generic model interpretability library for PyTorch

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:27:41.266956Z

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.

source=arxiv_source observed=2026-05-19T17:26:39.809292Z digest=sha256:2f5cba588611316222cd6458071de2fc3612e47805982c34c10e5f8145db6c32

Observation 484c9a2b-9cb9-40fc-ac4b-27574fd04dd1 · inbound

MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs cites this paper.

MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs Captum: A unified and generic model interpretability library for PyTorch

Reference 46

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T03:09:44.639687Z

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.

source=pdf_text observed=2026-05-15T03:04:27.417831Z digest=sha256:75d6542b80e48814d2272aae5f76b9c2b909d6e9c0156fb0d446e950c44d128d

Observation c975b2f4-b882-4a8f-8f2e-0934d55ced10 · inbound

ExECG: An Explainable AI Framework for ECG models cites this paper.

ExECG: An Explainable AI Framework for ECG models Captum: A unified and generic model interpretability library for PyTorch

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-20T07:38:09.547472Z

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.

source=arxiv_source observed=2026-05-20T07:35:31.869977Z digest=sha256:18fbe13abbbbc4ae9b81a265f2e20db9b8e4fc3b058c3b446674ba9b1faaaf03

Observation 113bfc4d-cd83-45c8-8db8-9a8c052101a4 · inbound

Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants cites this paper.

Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants Captum: A unified and generic model interpretability library for PyTorch

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-06-30T14:14:45.436135Z

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.

source=pdf_text observed=2026-06-30T14:12:03.734109Z digest=sha256:41682e933144d52ef8b102c727063d8e41224c73e8615895ecdc5396a9763637

Observation 268ac960-6b8a-4e4c-b1fb-9d07a92a17e9 · inbound

Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text cites this paper.

Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text Captum: A unified and generic model interpretability library for PyTorch

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T12:33:24.931605Z

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.

source=pdf_text observed=2026-06-29T12:25:21.687730Z digest=sha256:a233201e54358d611adcbe26a9b8332c6d1c0a89f459843b0b649c22da3a1cb4

Observation 3c337d23-9e11-4d33-aea5-42f75ae0e1bc · inbound

OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models cites this paper.

OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models Captum: A unified and generic model interpretability library for PyTorch

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T17:37:14.501224Z

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.

source=pdf_text observed=2026-06-27T21:58:41.494980Z digest=sha256:4caeef61569f17e79a0e49d3441dd88a6c063111c30051ff7309941dec0138e4

Observation 6bd0fa80-ec06-45b5-bd51-bbc22910fa01 · inbound

Human-Centered Benchmarking of Driver Monitoring Models cites this paper.

Human-Centered Benchmarking of Driver Monitoring Models Captum: A unified and generic model interpretability library for PyTorch

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T20:57:23.681709Z

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.

source=pdf_text observed=2026-06-27T19:59:45.757893Z digest=sha256:9d0afd94a9a16bc45d2c6c18e75cabc50679c0f564f1041f9031abfd0976be39

Observation d3cfa648-8549-4d6c-a335-4ef03fb28a66 · inbound

One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability cites this paper.

One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability Captum: A unified and generic model interpretability library for PyTorch

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:47:26.198115Z

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.

source=pdf_text observed=2026-06-27T18:38:48.128635Z digest=sha256:5d607a00236be6e241acddefff2a8777381371cfba5678521c5585f2f08457c1

Observation dda53e0d-ee90-4cbf-bf33-c24a755ea2cb · inbound

Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals cites this paper.

Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals Captum: A unified and generic model interpretability library for PyTorch

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-07-03T06:57:43.424113Z

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.

source=pdf_text observed=2026-06-27T12:19:23.084167Z digest=sha256:683e5b4c2a017348eba2bc36dee148839dfef1fcf2f1dd8bb57a2c3c9be90ed3

Observation 7d6334e5-e2cc-4f48-8840-89824b116f15 · inbound

Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models cites this paper.

Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models Captum: A unified and generic model interpretability library for PyTorch

Reference 144

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:24:26.421005Z

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.

source=arxiv_source observed=2026-06-30T08:20:23.645840Z digest=sha256:8991644cc67ea76df833930c6adf19c2f0f38d220abc0a628af70f0e89c0093e

Observation 985da47c-c86d-4548-95e0-e9c922b85ad4 · inbound

Surrogate Fidelity: When Can Open LLMs Explain Closed Ones? cites this paper.

Surrogate Fidelity: When Can Open LLMs Explain Closed Ones? Captum: A unified and generic model interpretability library for PyTorch

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-01T09:45:40.087103Z

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.

source=arxiv_source observed=2026-07-01T06:15:42.011563Z digest=sha256:98851e52a12a69b31ace30744e8e4fc6eaebd8bd4761fb9ea34cb0fc970319fc

Observation e0be944f-2cb6-45d7-87d9-65a176beb3fc · inbound

A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models cites this paper.

A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models Captum: A unified and generic model interpretability library for PyTorch

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-12T07:05:03.545756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T07:05:03.545756Z digest=sha256:1dd7c2c3c2ab1c3d446d086c8358b191d1a3470683420924efdb34d09c8f757e

Observation 4fd95cc2-1897-4b00-9aeb-fd6094cdf015 · inbound

Two Black Boxes, One Solver: Encoder Probing and Decoder Attribution for Neural Multi-Attribute VRP under Hard-Mask and Recourse Decoders cites this paper.

Two Black Boxes, One Solver: Encoder Probing and Decoder Attribution for Neural Multi-Attribute VRP under Hard-Mask and Recourse Decoders Captum: A unified and generic model interpretability library for PyTorch

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-11T18:45:31.095808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T18:45:31.095808Z digest=sha256:f861ec9a998cbdfbf873e7da7db73eec11404b3e91e20781216923f4068783c1

Observation 71d2b963-f7e8-4720-94d3-f7698b19bfc4 · inbound

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training cites this paper.

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training Captum: A unified and generic model interpretability library for PyTorch

Reference 102

Resolution
unresolved
no resolver link, observed 2026-07-11T10:50:54.419477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T10:50:54.419477Z digest=sha256:5e31f057c8a1e12951ec6475680c1e19092e62e28f736482a1f74b9595dff22c

Observation 86c66dee-8041-4387-aa86-6188915599d2 · inbound

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts cites this paper.

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts Captum: A unified and generic model interpretability library for PyTorch

Reference 31

Resolution
unresolved
no resolver link, observed 2026-07-14T04:05:21.655970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T04:05:21.655970Z digest=sha256:221ac88d83851588b2e623b815b30a25de9356153da22aeebe2740cb87398e72

Observation b93a12a9-cba1-4834-b0d8-11336fde48bb · inbound

Scaling Time Series Classification via XAI-Driven Data Reduction cites this paper.

Scaling Time Series Classification via XAI-Driven Data Reduction Captum: A unified and generic model interpretability library for PyTorch

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T22:25:15.014884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:25:15.014884Z digest=sha256:f52b51e2321a52c9a3826bdde0da2f71885cb225bd502c58c8dac70aaedbe8e2

Observation 13eed409-2887-40b5-836e-4f86cb9ab377 · inbound

Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data cites this paper.

Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data Captum: A unified and generic model interpretability library for PyTorch

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-01T00:54:33.073227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:54:33.073227Z digest=sha256:a80e518797a0803c5fffd9b4d7ec8dbf8f12dbb54f54dfff2282406dd66f0dd0

Observation 31bd79d3-1080-442a-88cb-b4bde562023e · inbound

Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering cites this paper.

Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering Captum: A unified and generic model interpretability library for PyTorch

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-03T17:28:10.565466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T17:28:10.565466Z digest=sha256:4481ec0a2ad7ad33c16f595e89b4e94e0d5bdb2122677f47843f2979737a89a8

Observation 0d7a3759-df69-4c02-b1cb-eeb58f423233 · inbound

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors cites this paper.

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors Captum: A unified and generic model interpretability library for PyTorch

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-05T00:44:49.966363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T00:44:49.966363Z digest=sha256:c628862716608d8ff2edcf526d331668a3e5817ce6cee24e05415e44f2e2aedc

Observation 84eb791b-6aff-4e31-91d1-56f43f94008f · inbound

Does Explainability Transfer? A Controlled Benchmark of Attribution Methods on Vision Transformers and CNNs cites this paper.

Does Explainability Transfer? A Controlled Benchmark of Attribution Methods on Vision Transformers and CNNs Captum: A unified and generic model interpretability library for PyTorch

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-04T08:06:53.050998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:06:53.050998Z digest=sha256:0c12fe2313a534639bdf10a6cf735bdd170a363271958de86117f6994cd0d9a1

Observation 1b3eae41-c358-42b9-ac2f-da8480656b37 · inbound

A Physics-Flavored Transformer Network for Parametrizing Contraction Dynamics of Engineered Skeletal Muscle Tissues cites this paper.

A Physics-Flavored Transformer Network for Parametrizing Contraction Dynamics of Engineered Skeletal Muscle Tissues Captum: A unified and generic model interpretability library for PyTorch

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T05:39:25.732256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:39:25.732256Z digest=sha256:6964eced880e328116f8643106a2d0880fa2e3ed91a436bad696ae520a9d8c9d