Paper Citation Record · LEDGER

Intent Factored Generation: Unleashing the Diversity in Your Language Model

As of 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2506.09659.

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

pith.paper-citation-record.v1
2506.09659 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:47:42.536900Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:58:11.179607Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:46:51.365287Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy5
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08f14e11-fff6-4e0d-a653-99dcf741ffb4 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Toolformer: Language models can teach themselves to use tools

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.307485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1068d545-faf6-40b2-ba1d-05c047cf1fcf · outbound

This paper cites Witscript 2: A System for Generating Improvised Jokes Without Wordplay.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Witscript 2: A System for Generating Improvised Jokes Without Wordplay

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:47:43.310189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 77a927c4-a009-4bbc-8429-9c85c85060ea · outbound

This paper cites Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:43.521622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a938c66-f774-4813-ae13-200438797199 · outbound

This paper cites Is Temperature the Creativity Parameter of Large Language Models?.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Is Temperature the Creativity Parameter of Large Language Models?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.329196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c071ac34-907c-48f9-b21f-1c53f41dfd01 · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.334596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aca9d154-0e07-4303-91e7-a17b63026535 · outbound

This paper cites CodeMonkeys: Scaling Test-Time Compute for Software Engineering.

Intent Factored Generation: Unleashing the Diversity in Your Language Model CodeMonkeys: Scaling Test-Time Compute for Software Engineering

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.339882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc7bd500-c51c-429a-a4cd-85cd8458651d · outbound

This paper cites Gold-medalist performance in solving olympiad geometry with alphageometry2.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Gold-medalist performance in solving olympiad geometry with alphageometry2

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.347166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c3bf2f5-bbaa-4cbe-aa0d-b4f53a75aaf8 · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Teaching Large Language Models to Reason with Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.354458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2646f08b-b6d5-46cc-9a03-cc6691371841 · outbound

This paper cites Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.359580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ed102c7-e87b-43ee-9931-60757c7ad37e · outbound

This paper cites Instruction tuning for large language models: A survey.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Instruction tuning for large language models: A survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.365102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11cbc623-1c19-48b5-b6df-53578cd3cb3d · outbound

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

Intent Factored Generation: Unleashing the Diversity in Your Language Model Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.370483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff903e42-6fd3-4517-8ebd-398cb70fc5a8 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Measuring Mathematical Problem Solving With the MATH Dataset

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.375652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcfe3a63-cc89-4cab-8a4e-0b2b715488c9 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Intent Factored Generation: Unleashing the Diversity in Your Language Model LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.380870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fe3d650-485e-469d-bea5-3164f86674d2 · outbound

This paper cites an unresolved cited work.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:47:43.504122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 627e3dab-bf1a-4fa4-9119-99a783fdea99 · outbound

This paper cites A neural probabilistic language model.

Intent Factored Generation: Unleashing the Diversity in Your Language Model A neural probabilistic language model

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:43.479714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1af1507c-1b90-481a-92fb-c17afa668858 · outbound

This paper cites Language models are unsupervised multitask learners.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Language models are unsupervised multitask learners

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.395400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 759c3712-8d6c-4c0a-85fd-020e4ca0d293 · outbound

This paper cites Reinforcement learning: An introduction.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Reinforcement learning: An introduction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.399531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf8be332-3fb6-47d5-bd1c-6f9257cf498e · outbound

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

Intent Factored Generation: Unleashing the Diversity in Your Language Model DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.404651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecaff3a5-cd06-4143-839f-ef6f18d77e92 · outbound

This paper cites VinePPO: Refining Credit Assignment in RL Training of LLMs.

Intent Factored Generation: Unleashing the Diversity in Your Language Model VinePPO: Refining Credit Assignment in RL Training of LLMs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.410649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01f99c58-477e-430c-9f17-9c7f33c41790 · outbound

This paper cites STaR: Bootstrapping Reasoning With Reasoning.

Intent Factored Generation: Unleashing the Diversity in Your Language Model STaR: Bootstrapping Reasoning With Reasoning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.415670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b634c8f2-10a5-459a-a500-a2cf55820b76 · outbound

This paper cites The Primacy Bias in Deep Reinforcement Learning.

Intent Factored Generation: Unleashing the Diversity in Your Language Model The Primacy Bias in Deep Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.422173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c859add1-bdd9-44c2-b912-df623d482175 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Finetuned Language Models Are Zero-Shot Learners

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.427792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9acc8aa-87f0-442b-9325-d051ea0f5d0e · outbound

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

Intent Factored Generation: Unleashing the Diversity in Your Language Model Direct preference optimization: Your language model is secretly a reward model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.432527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec2af6cb-b8cc-468b-98ec-7dafcabab294 · outbound

This paper cites BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation.

Intent Factored Generation: Unleashing the Diversity in Your Language Model BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.438433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1f24584-a178-4256-8d95-67fa1e2fe531 · outbound

This paper cites Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.443980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33304642-fe48-4b19-9c76-7c808bdea87f · outbound

This paper cites Illuminating search spaces by mapping elites.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Illuminating search spaces by mapping elites

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.448996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cc1697-e44b-42c6-860a-a0e0392c48af · outbound

This paper cites CTRL: A Conditional Transformer Language Model for Controllable Generation.

Intent Factored Generation: Unleashing the Diversity in Your Language Model CTRL: A Conditional Transformer Language Model for Controllable Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.454298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e99e67e9-bf30-4cd0-8c87-6e9e21f2059f · outbound

This paper cites Guiding language model reasoning with planning tokens.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Guiding language model reasoning with planning tokens

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.459282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f34e42ee-320d-48e5-8054-59e68ea9b656 · outbound

This paper cites Progressive Generation of Long Text with Pretrained Language Models.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Progressive Generation of Long Text with Pretrained Language Models

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:47:42.829367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 210a796d-cd93-4aa8-8d1b-79111ac75111 · outbound

This paper cites Plan-And-Write: Towards Better Automatic Storytelling.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Plan-And-Write: Towards Better Automatic Storytelling

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.469512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc88785-a2b4-4837-b501-e5b91240fe08 · outbound

This paper cites Qwen2.5 Technical Report.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Qwen2.5 Technical Report

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.475406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 678ad2be-d5f3-4dd2-96cc-58f72d9e30ee · outbound

This paper cites Qwen2.5-Coder Technical Report.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Qwen2.5-Coder Technical Report

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.480295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d71ba55-8b07-4671-9daa-f5c2d78eefc1 · outbound

This paper cites ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference.

Intent Factored Generation: Unleashing the Diversity in Your Language Model ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.485750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd685d1b-1753-4c48-b292-f750a4c8afb1 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Deep reinforcement learning at the edge of the statistical precipice

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.490151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16952ecc-daca-4869-b601-c9bb3a6738d9 · outbound

This paper cites Texygen: A benchmarking platform for text generation models.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Texygen: A benchmarking platform for text generation models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.494838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 760c8363-e3d7-4f07-af83-83f853507f6f · outbound

This paper cites Perspective API : Content moderation attributes and languages, 2024.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Perspective API : Content moderation attributes and languages, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:43.401276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29be4156-64fd-40bf-b435-747c3c6c2644 · outbound

This paper cites A new generation of perspective api: Efficient multilingual character-level transformers.

Intent Factored Generation: Unleashing the Diversity in Your Language Model A new generation of perspective api: Efficient multilingual character-level transformers

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:43.385646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96d34686-545c-4429-8b9d-a349c73b9c18 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Measuring Massive Multitask Language Understanding

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.509911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8598d538-dc7e-4b00-a5e8-ab2494008d53 · outbound

This paper cites The pushshift reddit dataset.

Intent Factored Generation: Unleashing the Diversity in Your Language Model The pushshift reddit dataset

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.514452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1315a8a2-2b8f-4ef8-91a6-5dbc8f70f6db · outbound

This paper cites Beautiful soup documentation.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Beautiful soup documentation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.518395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cd65c50-2f6c-4301-8d1a-bd832c4952c2 · outbound

This paper cites Dirt cheap web-scale parallel text from the common crawl.

Intent Factored Generation: Unleashing the Diversity in Your Language Model Dirt cheap web-scale parallel text from the common crawl

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:43.344444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6523205b-edbd-4cf6-9d41-fe776b6a81a2 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Intent Factored Generation: Unleashing the Diversity in Your Language Model HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.527864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b38640-ca19-4422-a924-ac439bfd66fd · outbound

This paper cites write newline.

Intent Factored Generation: Unleashing the Diversity in Your Language Model write newline

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:42.536900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9deebdf3-bea1-4a31-a727-a298b7c1e0fd · inbound

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits cites this paper.

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits Intent Factored Generation: Unleashing the Diversity in Your Language Model

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:25:51.256881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fb4d8dff-0231-4ff9-a22a-f56c0a76e324 · inbound

Annotations Mitigate Post-Training Mode Collapse cites this paper.

Annotations Mitigate Post-Training Mode Collapse Intent Factored Generation: Unleashing the Diversity in Your Language Model

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:46:51.368422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.