Paper Citation Record · LEDGER

ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.00794.

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

pith.paper-citation-record.v1
2402.00794 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:54:43.905457Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T16:25:50.066636Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7a3846bb-28fb-4116-8a76-b2ede2c0469d · inbound

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data cites this paper.

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:43.905457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45867db1-62d7-4a11-a338-940656b3b9cb · inbound

Faithfulness Evaluation for Decoder-only LLM Attributions with Controlled Retained Information cites this paper.

Faithfulness Evaluation for Decoder-only LLM Attributions with Controlled Retained Information ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T12:26:59.690104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation affa6095-360b-406a-94af-1c11ea39cfa2 · inbound

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs cites this paper.

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:02.203269Z

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 1c549040-4912-41ca-b7c8-6bb166e8e0d4 · inbound

The Attribution Contract: Feature Attribution for Generative Language Models cites this paper.

The Attribution Contract: Feature Attribution for Generative Language Models ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:40.485068Z

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 8d94ccad-82ae-4ecb-bab7-295ff4b78c42 · inbound

The Attribution Contract: Feature Attribution for Generative Language Models cites this paper.

The Attribution Contract: Feature Attribution for Generative Language Models ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:56.181431Z

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 67a01bb0-d32d-40da-b9c9-6b3ee538ee6c · inbound

What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs cites this paper.

What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models

Reference 95

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T16:25:50.068166Z

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.