Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2401.16788.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T12:35:47.132103Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T23:25:53.828114Z
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 21ed5251-c58b-499b-b1f8-05b415085c39 · inbound
The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment cites this paper.
The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T10:36:17.166384Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f7f60efa-51c8-4524-9b71-0dd0d6cd0b71 · inbound
Benchmarking LLM-based Relevance Judgment Methods cites this paper.
Benchmarking LLM-based Relevance Judgment Methods Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:35:47.132103Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e320f7bf-b1a1-4f84-87b0-134252a3aa29 · inbound
Generative AI Act II: Test Time Scaling Drives Cognition Engineering cites this paper.
Generative AI Act II: Test Time Scaling Drives Cognition Engineering Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:02:42.784034Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd5030dc-b7ad-4f50-91ec-f625b2d85ee4 · inbound
ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data cites this paper.
ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:45:07.304009Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd6a15b7-77aa-44f1-8b53-a9df7295ca61 · inbound
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate cites this paper.
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:11:10.537701Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6ee4100-cff8-4116-ad67-63afce0e9c73 · inbound
Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials cites this paper.
Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:56:20.064068Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93a1ffad-dabd-437f-8aa7-8d98a91b72ca · inbound
Learning to Interrupt in Language-based Multi-agent Communication cites this paper.
Learning to Interrupt in Language-based Multi-agent Communication Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:25:53.830634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.