Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.09841.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:18:00.236516Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T11:11:00.003848Z
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 e93d957f-447a-4409-9691-a9071dc102d5 · inbound
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression cites this paper.
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:18:00.236516Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afe79740-abfa-44dc-a403-5fe0b96dab89 · inbound
CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models cites this paper.
CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:53:12.413207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 313d4f2c-4a72-430c-9f3c-87d0cc99b35a · inbound
Math Natural Language Inference: this should be easy! cites this paper.
Math Natural Language Inference: this should be easy! Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T11:11:00.010692Z
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.