Pith. sign in

Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 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 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:51:54.508102Z

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 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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-18T14:25:25.576642Z digest=sha256:dbeec693509a6200e070312ea23d042341257924407117bb5f5d33941237dd5a

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:eb7f29e580d380ca5b26e300c64bc8fedb91ae64f5e5babe9d0cf8a8731cff2a