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Paper Citation Record · LEDGER

Large Language Model (LLM) Bias Index -- LLMBI

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.14769.

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

pith.paper-citation-record.v1
2312.14769 v3

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:12:22.978844Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T00:30:51.186541Z

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 7f840faa-bef0-42ee-b0dc-83a1ca6f1fa7 · inbound

Relative Bias: A Comparative Framework for Quantifying Bias in LLMs cites this paper.

Relative Bias: A Comparative Framework for Quantifying Bias in LLMs Large Language Model (LLM) Bias Index -- LLMBI

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T15:12:22.978844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:12:22.978844Z digest=sha256:d721f410b47eb6b8397618d9d0e7337497019e8c626a2510e51fc79c92f3aed3

Observation 0450afdd-8f76-4c2a-a819-c91780ae1af4 · inbound

Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning cites this paper.

Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning Large Language Model (LLM) Bias Index -- LLMBI

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:30:51.189547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T18:30:17.607269Z digest=sha256:381506ffdc4639628672afd9ff57f7b5352edabf19241eb96a2c22485c323645