Paper Citation Record · LEDGER

On the Reasoning Capacity of AI Models and How to Quantify It

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2501.13833.

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

pith.paper-citation-record.v1
2501.13833 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:38:37.872964Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:25:49.461260Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:16:15.729965Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy9
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 18b02b68-d690-44aa-8dca-f97fb2bc6498 · outbound

This paper cites What is 2 + 2?.

On the Reasoning Capacity of AI Models and How to Quantify It What is 2 + 2?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.507502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 75d634e0-2a6c-4b2f-bd76-1493eb8d6d48 · outbound

This paper cites GPT-4 Technical Report.

On the Reasoning Capacity of AI Models and How to Quantify It GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.332783Z

Source-reported events for the cited work

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Observation 98a16d17-e5dc-4d12-bc2c-9bc902087601 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

On the Reasoning Capacity of AI Models and How to Quantify It LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.414799Z

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Observation 513e532c-38c8-485f-96c1-1364b0b518d4 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

On the Reasoning Capacity of AI Models and How to Quantify It Gemini: A Family of Highly Capable Multimodal Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.459542Z

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Observation 354427ac-4e33-43ab-8364-cc7f12f90ac3 · outbound

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

On the Reasoning Capacity of AI Models and How to Quantify It Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.463428Z

Source-reported events for the cited work

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Observation b46fb890-7237-44c7-91b8-01744d9fe674 · outbound

This paper cites an unresolved cited work.

On the Reasoning Capacity of AI Models and How to Quantify It Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:38:38.428486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 43305c0a-dd35-4185-95dc-03cb37cd35c9 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

On the Reasoning Capacity of AI Models and How to Quantify It Training Verifiers to Solve Math Word Problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.472723Z

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Observation 8932eb75-e256-4371-b190-fd38da0bc035 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

On the Reasoning Capacity of AI Models and How to Quantify It GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.476988Z

Source-reported events for the cited work

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Observation cae2543c-5c9c-464f-9500-08e524451f5c · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

On the Reasoning Capacity of AI Models and How to Quantify It Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.480762Z

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Observation 983bde7a-45fb-4639-b51f-400c5e376f73 · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

On the Reasoning Capacity of AI Models and How to Quantify It GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.484754Z

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Observation b2c851b4-0bf9-494b-8d79-e9beed6a6559 · outbound

This paper cites LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models.

On the Reasoning Capacity of AI Models and How to Quantify It LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.537068Z

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Observation 6b89c6f0-bb6c-447c-b3b5-122d3201f67e · outbound

This paper cites Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks.

On the Reasoning Capacity of AI Models and How to Quantify It Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.595403Z

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Observation 42f887fb-1f70-4361-ac05-1dd50c780b01 · outbound

This paper cites Gunning and D.

On the Reasoning Capacity of AI Models and How to Quantify It Gunning and D

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.418432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ed7ed4f-24a2-48c7-be32-b196d4031a1b · outbound

This paper cites A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law.

On the Reasoning Capacity of AI Models and How to Quantify It A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.605902Z

Source-reported events for the cited work

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Observation ddf6b04f-6eff-4d13-9c35-2754852acac7 · outbound

This paper cites Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning.

On the Reasoning Capacity of AI Models and How to Quantify It Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.609456Z

Source-reported events for the cited work

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Observation bc59994b-c18c-456e-a981-c49940276022 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

On the Reasoning Capacity of AI Models and How to Quantify It Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.613135Z

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Observation d4b273d7-2585-48fe-ad20-abbf2dab4070 · outbound

This paper cites Dziri, X.

On the Reasoning Capacity of AI Models and How to Quantify It Dziri, X

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.408271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d57b22c-67e0-451c-967b-f12344acc88d · outbound

This paper cites Impact of Pretraining Term Frequencies on Few-Shot Reasoning.

On the Reasoning Capacity of AI Models and How to Quantify It Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.619568Z

Source-reported events for the cited work

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Observation 6bc718f5-5329-4aa0-98b1-9e0ddfe57208 · outbound

This paper cites A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners.

On the Reasoning Capacity of AI Models and How to Quantify It A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.622999Z

Source-reported events for the cited work

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Observation 56faf873-7670-4ffd-8b00-472bec2df561 · outbound

This paper cites Large Language Models Are Not Strong Abstract Reasoners.

On the Reasoning Capacity of AI Models and How to Quantify It Large Language Models Are Not Strong Abstract Reasoners

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.626155Z

Source-reported events for the cited work

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Observation 6d25bb6b-82d3-4e84-b865-50c4048afa17 · outbound

This paper cites Tovey, S.

On the Reasoning Capacity of AI Models and How to Quantify It Tovey, S

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.396766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa2e6a75-2c8d-46d1-9298-b61079a2c1b4 · outbound

This paper cites an unresolved cited work.

On the Reasoning Capacity of AI Models and How to Quantify It Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:38:38.386102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ea314c8-a515-4c9c-90ec-7c8522cfb263 · outbound

This paper cites Golgoon, K.

On the Reasoning Capacity of AI Models and How to Quantify It Golgoon, K

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.375826Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42220632-f80c-4327-a83d-fdcd5507a5bb · outbound

This paper cites Mechanistic Interpretability for AI Safety -- A Review.

On the Reasoning Capacity of AI Models and How to Quantify It Mechanistic Interpretability for AI Safety -- A Review

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.752513Z

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Observation 7291690f-f046-464a-9a55-98bad866d2be · outbound

This paper cites an unresolved cited work.

On the Reasoning Capacity of AI Models and How to Quantify It Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:38:38.322974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ee27578-1ed7-47d5-ba90-6dc722289725 · outbound

This paper cites Valmeekam, A.

On the Reasoning Capacity of AI Models and How to Quantify It Valmeekam, A

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.293993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5706ed2f-4d7f-4eda-baab-67ad3e776b57 · outbound

This paper cites Language models show human-like content effects on reasoning tasks.

On the Reasoning Capacity of AI Models and How to Quantify It Language models show human-like content effects on reasoning tasks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.765391Z

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Observation a0e80c6b-6779-4cd3-a7eb-81cef4be312f · outbound

This paper cites Adversarial Examples for Evaluating Reading Comprehension Systems.

On the Reasoning Capacity of AI Models and How to Quantify It Adversarial Examples for Evaluating Reading Comprehension Systems

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.768340Z

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Observation db25369a-b7a2-4aaa-a512-16d3903dffe7 · outbound

This paper cites Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference.

On the Reasoning Capacity of AI Models and How to Quantify It Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.771308Z

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Observation a02743ca-fccf-4bb8-a1a9-ec4d32f72e99 · outbound

This paper cites Eliminating Position Bias of Language Models: A Mechanistic Approach.

On the Reasoning Capacity of AI Models and How to Quantify It Eliminating Position Bias of Language Models: A Mechanistic Approach

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.774258Z

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Observation 7c6d2660-e3f3-4d21-82a4-6ef55fa18e28 · outbound

This paper cites Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions.

On the Reasoning Capacity of AI Models and How to Quantify It Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.777384Z

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Observation 1f0d8331-cff6-4fd2-ba34-c66df23b51b2 · outbound

This paper cites Serial Position Effects of Large Language Models.

On the Reasoning Capacity of AI Models and How to Quantify It Serial Position Effects of Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.780577Z

Source-reported events for the cited work

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Observation 9e04efbc-6ff2-4f93-8bbd-0cb94eb2fd20 · outbound

This paper cites Zheng, H.

On the Reasoning Capacity of AI Models and How to Quantify It Zheng, H

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.284412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 577b710c-a284-4ab1-ac6b-bea31cb810a3 · outbound

This paper cites Mitigating Selection Bias with Node Pruning and Auxiliary Options.

On the Reasoning Capacity of AI Models and How to Quantify It Mitigating Selection Bias with Node Pruning and Auxiliary Options

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.850447Z

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Observation 9c70d1bf-d7a4-4176-944d-2591a85a6a83 · outbound

This paper cites Mitigate Position Bias in Large Language Models via Scaling a Single Dimension.

On the Reasoning Capacity of AI Models and How to Quantify It Mitigate Position Bias in Large Language Models via Scaling a Single Dimension

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.854774Z

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Observation 4e077d67-0ba1-4881-b134-97b2b1c344c0 · outbound

This paper cites Bias Testing and Mitigation in LLM-based Code Generation.

On the Reasoning Capacity of AI Models and How to Quantify It Bias Testing and Mitigation in LLM-based Code Generation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.858493Z

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Observation 5b5ddad0-4cd5-4dbd-8e3b-606d3fa12c0d · outbound

This paper cites Blumenfeld, D.

On the Reasoning Capacity of AI Models and How to Quantify It Blumenfeld, D

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.273982Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cefb9ad8-7810-4a7e-b7ed-ea630a660049 · outbound

This paper cites Phases of learning dynamics in artificial neural networks: with or without mislabeled data.

On the Reasoning Capacity of AI Models and How to Quantify It Phases of learning dynamics in artificial neural networks: with or without mislabeled data

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.865514Z

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Observation cc1535f2-7703-46cb-a5a2-5dff3a87df94 · outbound

This paper cites an unresolved cited work.

On the Reasoning Capacity of AI Models and How to Quantify It Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:38:38.261924Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df02b9e4-44c2-44b1-9aac-d091697c99a4 · outbound

This paper cites In our case, the questions predominantly involve queries that are heavily reliant onreasoning.

On the Reasoning Capacity of AI Models and How to Quantify It In our case, the questions predominantly involve queries that are heavily reliant onreasoning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:38:38.190771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation b4909391-9c1b-4176-a1c7-1f02507edeb2 · inbound

Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain, Tree, and Graph Structures cites this paper.

Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain, Tree, and Graph Structures On the Reasoning Capacity of AI Models and How to Quantify It

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T20:25:49.461260Z

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Observation d56c56ec-29e2-4287-9919-7b8e9fba6e9e · inbound

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration cites this paper.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration On the Reasoning Capacity of AI Models and How to Quantify It

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.733589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.