Paper Citation Record · LEDGER

Who Reasons in the Large Language Models?

As of 8 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2505.20993.

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

pith.paper-citation-record.v1
2505.20993 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:47:30.778992Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

55 of 55 outbound references displayed

verified exact0
verified fuzzy3
unresolved52
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 77669f41-c167-40fb-88cb-742e12c3eb62 · outbound

This paper cites Physics of Language Models: Part 3.1, Knowledge Storage and Extraction.

Who Reasons in the Large Language Models? Physics of Language Models: Part 3.1, Knowledge Storage and Extraction

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:24.624633Z

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Observation 55e856c7-db6e-4c6f-a896-1412ca79a6e5 · outbound

This paper cites Physics of Language Models: Part 3.2, Knowledge Manipulation.

Who Reasons in the Large Language Models? Physics of Language Models: Part 3.2, Knowledge Manipulation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:24.678987Z

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Observation 6f112d02-2bfd-4ede-9044-a566f22c6636 · outbound

This paper cites Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws.

Who Reasons in the Large Language Models? Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:24.755626Z

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Observation 16afb019-3eed-4473-8272-41ca8b68d353 · outbound

This paper cites Layer Normalization.

Who Reasons in the Large Language Models? Layer Normalization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:24.855390Z

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Observation 23b2176d-4e82-425e-b8f6-2866d3ff9d2b · outbound

This paper cites Qwen Technical Report.

Who Reasons in the Large Language Models? Qwen Technical Report

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:24.912754Z

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Observation 0e78677b-d749-4156-97ad-df0e180273ac · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Who Reasons in the Large Language Models? Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:25.069604Z

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Observation f956631e-ccf7-4c9e-b442-8946d512c008 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Who Reasons in the Large Language Models? PaLM: Scaling Language Modeling with Pathways

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:25.192447Z

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Observation 3b07cbc9-2fe7-4eb5-bd37-4acc7e6ba60d · outbound

This paper cites Deep reinforcement learning from human preferences.

Who Reasons in the Large Language Models? Deep reinforcement learning from human preferences

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:47:32.118884Z

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Observation 76a9c62a-db9e-42db-9c6c-268e6b526357 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Who Reasons in the Large Language Models? Scaling Instruction-Finetuned Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:25.493364Z

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Observation 538f4554-f4af-4177-9424-1cfb588c36d4 · outbound

This paper cites The language model evaluation harness, 07 2024.

Who Reasons in the Large Language Models? The language model evaluation harness, 07 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:25.663234Z

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Observation 35203ada-1183-40bf-bb23-db69617bd102 · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

Who Reasons in the Large Language Models? Transformer Feed-Forward Layers Are Key-Value Memories

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:25.795656Z

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Observation 57d631bd-940d-4bb4-a26d-94866e5818a6 · outbound

This paper cites The Llama 3 Herd of Models.

Who Reasons in the Large Language Models? The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:25.908214Z

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Observation c7ec514e-d42c-4b3e-b9e6-ab71b275919e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Who Reasons in the Large Language Models? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.007593Z

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Observation 6526c668-c881-48fa-8a4f-e764fa798f23 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

Who Reasons in the Large Language Models? DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.097333Z

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Observation d57bea06-c005-4779-8f3f-6f73cb935acf · outbound

This paper cites A structural probe for finding syntax in word representations.

Who Reasons in the Large Language Models? A structural probe for finding syntax in word representations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.164284Z

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Observation a6b67b36-161a-4aa8-9b42-736a8be2d767 · outbound

This paper cites Transformer quality in linear time.

Who Reasons in the Large Language Models? Transformer quality in linear time

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:47:31.943650Z

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.

Observation 9404bb06-8c59-4963-a31b-5bede295ce9d · outbound

This paper cites Qwen2.5-Coder Technical Report.

Who Reasons in the Large Language Models? Qwen2.5-Coder Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.344745Z

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Observation a92aa6b2-df33-4a1f-a0a6-14bed57ae34d · outbound

This paper cites OpenAI o1 System Card.

Who Reasons in the Large Language Models? OpenAI o1 System Card

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.450679Z

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Observation 46473601-accf-49e8-8151-3b11b46f4197 · outbound

This paper cites Aime 2024 dataset.

Who Reasons in the Large Language Models? Aime 2024 dataset

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:47:31.781356Z

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.

Observation c7873328-d311-4e15-93de-13dcae1fbd32 · outbound

This paper cites an unresolved cited work.

Who Reasons in the Large Language Models? Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.791888Z

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Observation a08b6bea-a8e2-4b2f-a94c-8b9f16c56159 · outbound

This paper cites Scaling Laws for Neural Language Models.

Who Reasons in the Large Language Models? Scaling Laws for Neural Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:26.914583Z

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Observation d744b9d3-7208-4b3a-b03e-6c69eddc6a02 · outbound

This paper cites Natural questions: a benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453–466, 2019.

Who Reasons in the Large Language Models? Natural questions: a benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453–466, 2019

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.072927Z

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Observation 05eebde5-217a-4e5a-a942-1a43d401d6b0 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Who Reasons in the Large Language Models? Gonzalez, Hao Zhang, and Ion Stoica

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.243545Z

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Observation 790bb079-e538-4293-abbb-8c2952537878 · outbound

This paper cites Locating and editing factual associations in gpt.Advances in neural information processing systems, 35:17359–17372, 2022.

Who Reasons in the Large Language Models? Locating and editing factual associations in gpt.Advances in neural information processing systems, 35:17359–17372, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.357354Z

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Observation 1d462011-07bb-4801-85eb-52ea02e07e5b · outbound

This paper cites s1: Simple test-time scaling.

Who Reasons in the Large Language Models? s1: Simple test-time scaling

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.471116Z

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Observation f278091a-edae-4c81-9c8c-a70845b48c9d · outbound

This paper cites Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond.

Who Reasons in the Large Language Models? Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.605528Z

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Observation 487c46fb-91ac-4517-9ce8-93468f423a98 · outbound

This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

Who Reasons in the Large Language Models? Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.756683Z

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Observation 772acc8d-e0ed-4740-bcd4-15973c10a3b2 · outbound

This paper cites Training language models to follow instructions with human feedback.

Who Reasons in the Large Language Models? Training language models to follow instructions with human feedback

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:27.901823Z

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Observation 1615f6ce-752d-44ea-8092-719a5b7cefb2 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Who Reasons in the Large Language Models? Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.088761Z

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Observation 6513d4a9-7b40-4753-9048-81b292eab7d7 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Who Reasons in the Large Language Models? Direct preference optimization: Your language model is secretly a reward model

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.202466Z

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Observation 1d9c83d3-0b94-45f9-9752-da730b22d677 · outbound

This paper cites Zero: Memory optimiza- tions toward training trillion parameter models.

Who Reasons in the Large Language Models? Zero: Memory optimiza- tions toward training trillion parameter models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.279472Z

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Observation 23bbd5ee-539a-4574-9273-afced8c71bbb · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Who Reasons in the Large Language Models? SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.396444Z

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Observation d96b7ba3-347b-417b-9acc-d4c29d5687d2 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

Who Reasons in the Large Language Models? Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.464962Z

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Observation 950eddb8-c341-4a38-8a4e-0a214ddec4b5 · outbound

This paper cites The mechanistic basis of data dependence and abrupt learning in an in-context classification task.

Who Reasons in the Large Language Models? The mechanistic basis of data dependence and abrupt learning in an in-context classification task

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.549745Z

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Observation db5fb179-7aad-4d40-bd83-0590fa768dc7 · outbound

This paper cites Toolformer: Language Models Can Teach Themselves to Use Tools.

Who Reasons in the Large Language Models? Toolformer: Language Models Can Teach Themselves to Use Tools

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.633238Z

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Observation b32b02d2-68c3-4fc3-ba5b-cb681d4b3e85 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Who Reasons in the Large Language Models? Proximal Policy Optimization Algorithms

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.708526Z

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Observation ea66fc28-bf33-4d36-b9a2-a785ebb4e587 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Who Reasons in the Large Language Models? DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.800385Z

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Observation 625ec01b-3389-4c84-aec7-3e07aa2fabee · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024.

Who Reasons in the Large Language Models? Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:28.927468Z

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Observation d5713037-a29d-423c-bf5b-d68e5a559267 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Who Reasons in the Large Language Models? Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.015912Z

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Observation a915bc97-4c15-4a4c-a220-53e14de63428 · outbound

This paper cites QwQ-32B: Embracing the Power of Reinforcement Learning, March 2025.

Who Reasons in the Large Language Models? QwQ-32B: Embracing the Power of Reinforcement Learning, March 2025

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.122167Z

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Observation 9c98f776-9920-4f11-9b0e-09fda579b3c4 · outbound

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

Who Reasons in the Large Language Models? LLaMA: Open and Efficient Foundation Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.253806Z

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Observation aaf4adad-0b7f-4e3b-b0a4-94f6abcd97d3 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Who Reasons in the Large Language Models? Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.347634Z

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Observation abb096ab-1dc4-4289-bdc9-9be6b47cd701 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Who Reasons in the Large Language Models? Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.454118Z

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Observation 4469a898-7bae-4833-a8f2-9177657715f0 · outbound

This paper cites Analyzing the Structure of Attention in a Transformer Language Model.

Who Reasons in the Large Language Models? Analyzing the Structure of Attention in a Transformer Language Model

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.564033Z

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Observation fa770ff0-fdef-4baa-a2c8-f076ce87e907 · outbound

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

Who Reasons in the Large Language Models? Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.685237Z

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Observation aeef9f74-6ff1-4123-b262-84ce4547d92d · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Who Reasons in the Large Language Models? Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.756977Z

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Observation 579ce010-cb2f-4bf5-9f5b-89d4cda66837 · outbound

This paper cites Emergent Abilities of Large Language Models.

Who Reasons in the Large Language Models? Emergent Abilities of Large Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.827906Z

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Observation a48acdd5-5b4d-457d-875d-800835a61bce · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Who Reasons in the Large Language Models? Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:29.952370Z

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Observation a7a1e557-55b2-4048-addc-15e258a0e983 · outbound

This paper cites an unresolved cited work.

Who Reasons in the Large Language Models? Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:47:31.504773Z

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Observation 3bf64778-3d8a-4aa9-bb19-0ef691507091 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Who Reasons in the Large Language Models? Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.111332Z

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Observation 672aecf7-59f0-4c11-9d0e-c9384872401d · outbound

This paper cites Qwen3 technical report, 2025.

Who Reasons in the Large Language Models? Qwen3 technical report, 2025

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.199921Z

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Observation af7e55bc-a736-48fe-a1a0-4f91129163dd · outbound

This paper cites Qwen2.5 Technical Report.

Who Reasons in the Large Language Models? Qwen2.5 Technical Report

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.385722Z

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Observation 179123ed-0508-40c4-be52-42873490cf41 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

Who Reasons in the Large Language Models? Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.494845Z

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Observation 72f78c0d-a16e-4278-b6c2-547478ded6a5 · outbound

This paper cites Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild, 2025.

Who Reasons in the Large Language Models? Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild, 2025

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.644570Z

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Observation 6a200537-80a8-422b-84fc-27bfe6553f86 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Who Reasons in the Large Language Models? Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.778992Z

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Pith citing papers

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