Paper Citation Record · LEDGER

Reasoning Capabilities and Invariability of Large Language Models

As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2505.00776.

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

pith.paper-citation-record.v1
2505.00776 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:40:57.830784Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a0e2a76-ff21-4e4c-91fd-4618b36685ff · outbound

This paper cites Attention is all you need,.

Reasoning Capabilities and Invariability of Large Language Models Attention is all you need,

Reference 1

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Observation 49abdaaa-0d05-4ccd-af3c-5735886f6258 · outbound

This paper cites Emergent abilities of large language models,.

Reasoning Capabilities and Invariability of Large Language Models Emergent abilities of large language models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.683402Z

Source-reported events for the cited work

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Observation 46931e7b-34c7-46ba-aa67-f257e020037c · outbound

This paper cites Are Emergent Abilities of Large Language Models a Mirage?.

Reasoning Capabilities and Invariability of Large Language Models Are Emergent Abilities of Large Language Models a Mirage?

Reference 3

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Source-reported events for the cited work

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Observation 86a26ba1-9d24-4a34-882e-0b075ac1d714 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Reasoning Capabilities and Invariability of Large Language Models Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 4

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Reasoning Capabilities and Invariability of Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

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Observation 9e10901c-176b-4ae2-932f-173f49fbbd0e · outbound

This paper cites Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity.

Reasoning Capabilities and Invariability of Large Language Models Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

Reference 6

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Observation 80c5585b-addb-4cc0-94d1-36a16fc9146a · outbound

This paper cites Reasoning with language model prompting: A survey,.

Reasoning Capabilities and Invariability of Large Language Models Reasoning with language model prompting: A survey,

Reference 7

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Observation d77ba5b8-e56d-4acf-a535-ac2de4cdf1fd · outbound

This paper cites The winograd schema chal lenge: evaluating progress in commonsense reasoning,.

Reasoning Capabilities and Invariability of Large Language Models The winograd schema chal lenge: evaluating progress in commonsense reasoning,

Reference 8

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Observation d25ec48a-5334-4464-92b1-3f809c5d2c4c · outbound

This paper cites Diagnos ing the ﬁrst-order logical reasoning ability through LogicNLI,.

Reasoning Capabilities and Invariability of Large Language Models Diagnos ing the ﬁrst-order logical reasoning ability through LogicNLI,

Reference 9

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Observation 78a150a1-e14f-4ba6-b151-f8aaa204cafb · outbound

This paper cites LogiQA 2.0—an improved dataset for logical reasoning in na tural language understanding,.

Reasoning Capabilities and Invariability of Large Language Models LogiQA 2.0—an improved dataset for logical reasoning in na tural language understanding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.599328Z

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Observation f1f51430-7ead-4029-a86f-fb1e10b2f493 · outbound

This paper cites From lsat: The progress and challenges of complex r ea- soning,.

Reasoning Capabilities and Invariability of Large Language Models From lsat: The progress and challenges of complex r ea- soning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.586753Z

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Observation c8909a41-fc7e-49f8-a759-18936379eaaa · outbound

This paper cites Exploring self-supervised logic-enhanced training for large langua ge models,.

Reasoning Capabilities and Invariability of Large Language Models Exploring self-supervised logic-enhanced training for large langua ge models,

Reference 12

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Observation ed4e991e-c4f6-40ab-810f-939a9ca7d229 · outbound

This paper cites On the paradox of learning to reason from data,.

Reasoning Capabilities and Invariability of Large Language Models On the paradox of learning to reason from data,

Reference 13

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Observation 95d45642-b199-4bad-a6e7-35324f48c97f · outbound

This paper cites Survey of hallucination in natural language generation,.

Reasoning Capabilities and Invariability of Large Language Models Survey of hallucination in natural language generation,

Reference 14

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Observation e4d81408-de17-418a-8820-bd43955cdc92 · outbound

This paper cites Working memo ry involve- ment in propositional and spatial reasoning,.

Reasoning Capabilities and Invariability of Large Language Models Working memo ry involve- ment in propositional and spatial reasoning,

Reference 15

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Observation 80977f2a-5ac4-4b55-bc69-75d64b883ca2 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting metho ds in natural language processing,.

Reasoning Capabilities and Invariability of Large Language Models Pre-train, prompt, and predict: A systematic survey of prompting metho ds in natural language processing,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.502845Z

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Observation d3d31b94-67db-4fec-bd5f-125934c3aa16 · outbound

This paper cites Baader, D.

Reasoning Capabilities and Invariability of Large Language Models Baader, D

Reference 17

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Observation 988f5a01-2809-4af0-9f14-2fc51e01cb48 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Reasoning Capabilities and Invariability of Large Language Models Chain-of-thought prompting elicits reasoning in large language models,

Reference 18

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Observation b7765eaf-25a3-4ae2-971b-30f6480a8f1a · outbound

This paper cites Evaluating Large Language Models: A Comprehensive Survey.

Reasoning Capabilities and Invariability of Large Language Models Evaluating Large Language Models: A Comprehensive Survey

Reference 19

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Observation 1d4d9d5d-3137-4565-8749-6df3b68d1997 · outbound

This paper cites Knowledge Engineer ing Using Large Language Models,.

Reasoning Capabilities and Invariability of Large Language Models Knowledge Engineer ing Using Large Language Models,

Reference 20

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Observation 858a5b1d-cbe5-4940-aa73-b3dff955cf59 · outbound

This paper cites Evaluating language models for knowledge base completion,.

Reasoning Capabilities and Invariability of Large Language Models Evaluating language models for knowledge base completion,

Reference 21

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Observation 62960fc4-3c2c-403e-bad3-ae14f00011fe · outbound

This paper cites Assessing t he factual accuracy of generated text,.

Reasoning Capabilities and Invariability of Large Language Models Assessing t he factual accuracy of generated text,

Reference 22

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Source-reported events for the cited work

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Observation 2cf79479-7fa8-46e9-a193-7d8006e245d4 · outbound

This paper cites How context affects language models’ factual pr edictions,.

Reasoning Capabilities and Invariability of Large Language Models How context affects language models’ factual pr edictions,

Reference 23

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

This paper cites Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond.

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

Reference 24

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This paper cites Logicbench: Towards systematic evaluation o f logical reasoning ability of large language models,.

Reasoning Capabilities and Invariability of Large Language Models Logicbench: Towards systematic evaluation o f logical reasoning ability of large language models,

Reference 25

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Observation bc4b8878-c7be-4003-bf3f-570d83538b64 · outbound

This paper cites Cladder: A benchmark to assess causal reasoning capabilities of langu age models,.

Reasoning Capabilities and Invariability of Large Language Models Cladder: A benchmark to assess causal reasoning capabilities of langu age models,

Reference 26

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Observation a3b102e8-c2c8-42c1-8957-15291b7fa36d · outbound

This paper cites LLMs Are Prone to Fallacies in Causal Inference.

Reasoning Capabilities and Invariability of Large Language Models LLMs Are Prone to Fallacies in Causal Inference

Reference 27

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Observation 15f77896-445a-4d86-ae8a-fb7f0bce8c3a · outbound

This paper cites Large language models cannot self-correct reason ing yet,.

Reasoning Capabilities and Invariability of Large Language Models Large language models cannot self-correct reason ing yet,

Reference 28

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Observation 3285a5dc-dc5c-4df0-99e7-d6422bd2b1ff · outbound

This paper cites On the paradox of learning to reason from data,.

Reasoning Capabilities and Invariability of Large Language Models On the paradox of learning to reason from data,

Reference 29

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Observation 3dd869f8-f837-4dba-885f-a37e377c397c · outbound

This paper cites Le arning deductive reasoning from synthetic corpus based on formal l ogic,.

Reasoning Capabilities and Invariability of Large Language Models Le arning deductive reasoning from synthetic corpus based on formal l ogic,

Reference 30

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Observation 01d86d6c-c917-4766-8e1d-50a37c143436 · outbound

This paper cites This is not a dataset: A large negation benchmark to challen ge large language models,.

Reasoning Capabilities and Invariability of Large Language Models This is not a dataset: A large negation benchmark to challen ge large language models,

Reference 31

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Observation e489ba07-8f6c-4915-8ad9-d8bb64162f84 · outbound

This paper cites Lang uage models are not naysayers: an analysis of language models on negation benchmarks,.

Reasoning Capabilities and Invariability of Large Language Models Lang uage models are not naysayers: an analysis of language models on negation benchmarks,

Reference 32

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Observation a6a9bd31-00dc-4227-aa1c-d1c8bd007f6f · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Reasoning Capabilities and Invariability of Large Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 33

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This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Reasoning Capabilities and Invariability of Large Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 34

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This paper cites Stable LM 2 1.6B Technical Report.

Reasoning Capabilities and Invariability of Large Language Models Stable LM 2 1.6B Technical Report

Reference 35

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Reasoning Capabilities and Invariability of Large Language Models Qwen Technical Report

Reference 36

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Observation e22b3db2-c8f3-4901-8364-ec2b36c0b1c5 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Reasoning Capabilities and Invariability of Large Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 37

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Reasoning Capabilities and Invariability of Large Language Models Yi: Open Foundation Models by 01.AI

Reference 38

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Reasoning Capabilities and Invariability of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 39

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Reasoning Capabilities and Invariability of Large Language Models Llama 3 model card,

Reference 40

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Observation 9a0f11e1-9086-4b70-852d-7226e5c7229a · outbound

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Reasoning Capabilities and Invariability of Large Language Models GPT-4 Technical Report

Reference 41

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This paper cites Dekking, A Modern Introduction to Probability and Statistics: Under - standing Why and How , ser.

Reasoning Capabilities and Invariability of Large Language Models Dekking, A Modern Introduction to Probability and Statistics: Under - standing Why and How , ser

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.215712Z

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Observation 747fa141-875a-4b58-b67e-6cbecba13a6f · outbound

This paper cites Note on the sampling error of the differenc e between correlated proportions or percentages,.

Reasoning Capabilities and Invariability of Large Language Models Note on the sampling error of the differenc e between correlated proportions or percentages,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.199882Z

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Observation 3a40fc20-6a84-4080-bff3-2cca7ec97a48 · outbound

This paper cites A survey of fake news: Fundamen tal theories, detection methods, and opportunities,.

Reasoning Capabilities and Invariability of Large Language Models A survey of fake news: Fundamen tal theories, detection methods, and opportunities,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.182581Z

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Observation e7489d87-1501-4c5d-a16a-e74e4634fb08 · outbound

This paper cites Cont ent based fake news detection using knowledge graphs,.

Reasoning Capabilities and Invariability of Large Language Models Cont ent based fake news detection using knowledge graphs,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.167590Z

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Observation 332e6518-9ec6-4651-b89d-27436e389611 · outbound

This paper cites Evaluation of fake news detection with knowledge-enhanced language mo dels,.

Reasoning Capabilities and Invariability of Large Language Models Evaluation of fake news detection with knowledge-enhanced language mo dels,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.153499Z

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This paper cites Credibility in social media: op inions, news, and health information—a survey,.

Reasoning Capabilities and Invariability of Large Language Models Credibility in social media: op inions, news, and health information—a survey,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.140392Z

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Observation b93c2af2-ebad-44fd-8131-fcef44c3d3a5 · outbound

This paper cites Knowledge Engineering using Large Language Models.

Reasoning Capabilities and Invariability of Large Language Models Knowledge Engineering using Large Language Models

Reference 48

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Observation fb81b92f-b644-4622-9603-3d7898c63bea · outbound

This paper cites Available: https://arxiv.org/abs/2206.

Reasoning Capabilities and Invariability of Large Language Models Available: https://arxiv.org/abs/2206

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:40:58.668082Z

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Observation 9312397b-4131-4bdc-aa3c-0ee4c8c38217 · outbound

This paper cites Available: https://doi.org/10.1145/356 0815.

Reasoning Capabilities and Invariability of Large Language Models Available: https://doi.org/10.1145/356 0815

Reference 2023

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.