Paper Citation Record · LEDGER

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2306.09841.

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

pith.paper-citation-record.v1
2306.09841 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:40:57.733880Z

measured 0 of 1 external citation measurements

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 e68580c5-533a-4706-954a-bbd6c8dcf546 · inbound

Disentangling Exploration of Large Language Models by Optimal Exploitation cites this paper.

Disentangling Exploration of Large Language Models by Optimal Exploitation Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T20:18:58.500173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90d3695f-17c2-4e97-b0e4-025ad30b140f · inbound

Reasoning Capabilities and Invariability of Large Language Models cites this paper.

Reasoning Capabilities and Invariability of Large Language Models Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:40:57.733880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53d80a29-79cc-4682-ad6f-6a4a680e9708 · inbound

Enigme: Generative Text Puzzles for Evaluating Reasoning in Language Models cites this paper.

Enigme: Generative Text Puzzles for Evaluating Reasoning in Language Models Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:20:08.550424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-17T06:30:58.91139+00:00.