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Paper Citation Record · LEDGER

Evaluating the Robustness of Analogical Reasoning in Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2411.14215.

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

pith.paper-citation-record.v1
2411.14215 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:10:32.572118Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:28:58.093447Z

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 86f4fb34-241e-452a-8ee5-9011c9417d3c · inbound

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning cites this paper.

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 86

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:32:33.270067Z

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=2026-05-23T04:30:38.804702Z digest=sha256:f7e8a887660b85e879621e472160ef6e4a47371e65bbcc5283f59a24c4668365

Observation c1938ca5-e950-4a21-b155-715664967264 · inbound

Mechanistic Interpretability Needs Philosophy cites this paper.

Mechanistic Interpretability Needs Philosophy Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T23:50:47.367863Z

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=pdf_text observed=2026-05-21T23:49:19.683025Z digest=sha256:4360300656a7ab2202987f90802cc9695de967c83519e4d2abb8d885799bfd5e

Observation d87c7968-9b19-4564-bd8b-cbfae4473aab · inbound

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning cites this paper.

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T21:10:32.572118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:10:32.572118Z digest=sha256:ce3285475efd7746fe966d79dfdc35488aeb1b1cc068910094f92790cf1be0ee

Observation 25a96312-2b0b-475c-91b2-649f60b911e9 · inbound

On Robustness and Reliability of Benchmark-Based Evaluation of LLMs cites this paper.

On Robustness and Reliability of Benchmark-Based Evaluation of LLMs Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T10:31:02.177268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:31:02.177268Z digest=sha256:225826ed579f4253e1f6ad109cf4a8d1f30710245cc097e741151d6bdbbd658c

Observation a0322c30-3942-409f-a309-44e1e89970ea · inbound

Can Large Language Models Generalize Procedures Across Representations? cites this paper.

Can Large Language Models Generalize Procedures Across Representations? Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-03T05:00:57.477973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T05:00:57.477973Z digest=sha256:5c3567433df91ab868c019df00f09f5d55617f426277d06af4fe2ff772a5723e

Observation 838842db-da98-4aa3-9e63-08afcba0ecf0 · inbound

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid cites this paper.

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 225

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:56:06.210285Z

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=2026-05-09T14:33:11.033906Z digest=sha256:a0666e7e497522cce2d1810daa5b2a854fe101585ca86c07f728a49606e04088

Observation 1ca954c3-7fbb-4e6c-af01-05b0621262f0 · inbound

AGC-Bench: Measuring Artificial General Creativity cites this paper.

AGC-Bench: Measuring Artificial General Creativity Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 43

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T12:36:56.051567Z

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=2026-07-02T12:33:53.029578Z digest=sha256:0bb10f10f597ec018f1a5eebf0f26ea69476aa0b083d77966ac8cbe7846d45d6

Observation 08c71db3-2592-4c22-8b56-38fb14706716 · inbound

AGC-Bench: Measuring Artificial General Creativity cites this paper.

AGC-Bench: Measuring Artificial General Creativity Evaluating the Robustness of Analogical Reasoning in Large Language Models

Reference 43

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T21:28:58.095269Z

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=2026-07-03T21:25:14.030920Z digest=sha256:afc65c924c8f17802142544b585ad5f9c5c4544706de02697edb5f5d364552de