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Paper Citation Record · LEDGER

Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.10552.

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

pith.paper-citation-record.v1
2404.10552 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:16:32.851326Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T14:26:28.037909Z

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 1b997804-eff4-4d00-a9f5-fd7dc189e56f · inbound

Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks cites this paper.

Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T23:16:32.851326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:16:32.851326Z digest=sha256:8ea735351008ea1fedef990799cd4c62dac7fdf535f390ef98e74127900c12a9

Observation b51c28e8-cd44-4020-a760-ad37569500be · inbound

RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks cites this paper.

RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-18T14:26:28.041341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-18T14:25:25.576642Z digest=sha256:9bfcfa2bfb05092fb59aaf35d15b9e659c5933393ee8527e8c2e6f08df71cc5f

Observation 7becebb1-fd72-4a14-88b4-a227bcad8887 · inbound

LLM Watermark Evasion via Bias Inversion cites this paper.

LLM Watermark Evasion via Bias Inversion Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-04T14:51:54.508102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:51:54.508102Z digest=sha256:6ace2af696c8813c673ccc18d32f12ff76cd4d5be19ccadd4109d37280ed2192