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Paper Citation Record · LEDGER

A Hybrid Attention Framework for Fake News Detection with Large Language Models

As of 18 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 2 inbound Pith citation observations for arXiv:2501.11967.

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

pith.paper-citation-record.v1
2501.11967 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:42:40.539599Z

measured 13 of 13 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:33:43.887384Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T22:39:55.318647Z

Reference resolution

11 of 11 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3e05c2c-593a-48b7-9fdb-0f71978e62a1 · outbound

This paper cites an unresolved cited work.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-10T17:42:40.745605Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.482151Z digest=sha256:69b9611f38da9a43f9ce706eb6d2acb042afe737bb65a05f445a2d1b9a41d0d9

Observation b9f9c11d-15d7-4d44-9089-23ceac22efa0 · outbound

This paper cites Zhou and R.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Zhou and R

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:42:40.729272Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.487779Z digest=sha256:c81b97f2c5eac34941fb8e86586f9ae87eb580c8c2fc6a305b34864b8776846f

Observation e5b71bea-f663-4588-acc9-95679b7d11b3 · outbound

This paper cites an unresolved cited work.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-10T17:42:40.713684Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.492742Z digest=sha256:4e161019a5545ff7314c4fad79ad53d2bfddef47cbb3dcf6f3897fe16c0e01d2

Observation 61280261-55a2-4d6c-be5a-4c3d21281c42 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

A Hybrid Attention Framework for Fake News Detection with Large Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-10T17:42:40.504211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:42:40.504211Z digest=sha256:12f165537eac64a47ce6ca0fa6ed6174e978a85035d5716478906cc73777e21c

Observation bb301e1c-5aa8-490d-a816-9a3368ad7973 · outbound

This paper cites an unresolved cited work.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-10T17:42:40.696924Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.509608Z digest=sha256:297703fddf249cdba466442ac424cca24540983d46866ab9cd6e9276a0703d55

Observation e34b4f6c-1ace-4c8e-9da2-b36f4727faf0 · outbound

This paper cites Holstein, B.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Holstein, B

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:42:40.679849Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.515369Z digest=sha256:6dfd950fe8a85329646f9d498f1968d57c993da78684ddd5de73a300d248d0e4

Observation 1a3a360b-b4bc-4dd8-902c-b6c1ae87fa1c · outbound

This paper cites Reich and J.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Reich and J

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:42:40.664008Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.520373Z digest=sha256:044afa415a82a9ae88c73eb1d65f2543be19536679c9f17956771f9b7771bbc2

Observation d484681b-199b-4c3e-83ad-cd172e725aa9 · outbound

This paper cites Chen and C.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Chen and C

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:42:40.647937Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.525202Z digest=sha256:5f23d9eb053467e60ad4435cd90972387e507e1f1a0efabd55185e70a2783942

Observation 42bbbd37-8c5b-4889-ada0-46285632aa49 · outbound

This paper cites an unresolved cited work.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-10T17:42:40.631581Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.530062Z digest=sha256:f10846a88140a336f6ae499fb6502ec3130b71aa20877c461c58e1dedae2fc72

Observation 616edbca-c8b5-4cef-94fe-c90440d16dca · outbound

This paper cites Jiang and Z.

A Hybrid Attention Framework for Fake News Detection with Large Language Models Jiang and Z

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:42:40.614892Z

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.

source=arxiv_source observed=2026-08-10T17:42:40.534791Z digest=sha256:223f5df11f2e67954360eeffd5b98142c8f0af81a818bf66b2cdc23b6fc5b810

Observation 359dfaa5-5c61-4dc4-a943-f907130857e8 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

A Hybrid Attention Framework for Fake News Detection with Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T17:42:40.539599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:42:40.539599Z digest=sha256:6a04619383056a2e9d11402e7d96f97d1131abac02c8b124194f929eca757606

Pith citing papers

Observation bf162bd8-4f4c-4750-8726-b7815e6ba43b · inbound

Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models cites this paper.

Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models A Hybrid Attention Framework for Fake News Detection with Large Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T00:33:43.887384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T00:33:43.887384Z digest=sha256:3958b11ccf35d04b65b57f74c2c56ed31431ed443ec17cb5f3dab74ca397abb3

Observation dd34e57d-c717-4e8b-8aaf-b686a0835bac · inbound

A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit cites this paper.

A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit A Hybrid Attention Framework for Fake News Detection with Large Language Models

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-07T22:39:55.323497Z

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.

source=pdf_text observed=2026-08-07T22:39:55.081095Z digest=sha256:f3608f5cd409ab20c757c09fb93129b03aa80f330df57f53b1418b431ecb2049