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Paper Citation Record · LEDGER

ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework

As of 16 August 2026, this Paper Citation Record lists 4 of 4 outbound references and 1 inbound Pith citation observation for arXiv:2502.05084.

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

pith.paper-citation-record.v1
2502.05084 v1

Coverage vector

measured 4 of 4 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:21:52.803272Z

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:05:07.296991Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:05:07.347544Z

Reference resolution

4 of 4 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a91816c8-ffe6-4248-bc24-f49cb2ed94c0 · outbound

This paper cites A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents.

ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-08T20:21:52.786774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:21:52.786774Z digest=sha256:b84d5d229a1ac3ca3d18e5b08c2b76f6b24452e50e6ad9ff2d2093fa0a815cfc

Observation c2172219-1729-4012-96a3-d41583130766 · outbound

This paper cites an unresolved cited work.

ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework Unresolved cited work

Reference 186

Resolution
unresolved
raw_fallback, observed 2026-08-08T20:21:52.894449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-08T20:21:52.793109Z digest=sha256:a463c3d0f6c3db2db2cae841ea9a91ffc99b1cc409736edc7a38f3951a701adf

Observation ad6f90d7-1094-4847-83ca-b0109ed767b5 · outbound

This paper cites Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts.

ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts

Reference 1279

Resolution
verified exact
local_arxiv, observed 2026-08-08T20:21:52.846221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-08T20:21:52.803272Z digest=sha256:6f6119a7b7382d4ce83c3e5b68001c4e8fa12811c63b5bce3c9d1c40c366c8cd

Observation 5664066c-cbd1-4672-b565-900df2a2464e · outbound

This paper cites The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations.

ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

Reference 2273

Resolution
unresolved
no resolver link, observed 2026-08-08T20:21:52.797877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:21:52.797877Z digest=sha256:408bdb5d2c08c9ecd42aa9cae357e605ed6091bde724904cede401b43de826b7

Pith citing papers

Observation 56c3ab34-57d0-42fa-ba1b-0bf75146533a · inbound

DK-RRT: Deep Koopman RRT for Collision-Aware Motion Planning of Space Manipulators in Dynamic Debris Environments cites this paper.

DK-RRT: Deep Koopman RRT for Collision-Aware Motion Planning of Space Manipulators in Dynamic Debris Environments ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:05:07.350486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T20:05:07.296991Z digest=sha256:748e4252e51f602c213e9446e031e77e8e124a96e7557f6f2a29c2ea252cf13b