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Paper Citation Record · LEDGER

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data

As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2502.08515.

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

pith.paper-citation-record.v1
2502.08515 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:52:23.297314Z

measured 30 of 30 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T17:16:17.464937Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T17:18:01.280007Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact4
  • verified fuzzy8
  • unresolved13
  • parse uncertain0
  • malformed identifier4
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be90d5ce-b715-4011-b0c8-7bc675552597 · outbound

This paper cites Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 1

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source=pdf_text observed=2026-08-08T04:52:23.165807Z digest=sha256:507dc62358c89affcdc8b41c5a68f2ddcc42a6da86a590cf5a0db044521a9f61

Observation a430374e-7085-4556-867f-6f410963a337 · outbound

This paper cites Optimizing Temperature for Language Models with Multi-Sample Inference.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Optimizing Temperature for Language Models with Multi-Sample Inference

Reference 2

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source=pdf_text observed=2026-08-08T04:52:23.171444Z digest=sha256:2640dae32f23c331b5676a0b56167a928895d03f31627dbdf4079f54658a7b60

Observation 80911952-b7c0-49b0-8c34-c5137b4b5049 · outbound

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

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Is Temperature the Creativity Parameter of Large Language Models?

Reference 3

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source=pdf_text observed=2026-08-08T04:52:23.176650Z digest=sha256:e25af5859c66fee2910a33eafc78e8b381f12ca87db1a7182f60f5e523646a2e

Observation 410a4a0e-3e05-4d2f-81be-7d0e762f0db6 · outbound

This paper cites Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing

Reference 4

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local_arxiv, observed 2026-08-08T04:52:23.702483Z

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

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Observation 977ed8e4-0a1e-4c75-b0a3-7f64d4ae735e · outbound

This paper cites Synthetic Data Generation with Large Language Models for Personalized Community Question Answering.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Synthetic Data Generation with Large Language Models for Personalized Community Question Answering

Reference 5

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source=pdf_text observed=2026-08-08T04:52:23.186777Z digest=sha256:ad6a59f8656175973465c342c0c5446bd4349a98896f94a813169fbe738d380d

Observation bb9b1498-ced3-4ae5-a797-566bbff96fb2 · outbound

This paper cites Entropy Law: The Story Behind Data Compression and LLM Performance.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Entropy Law: The Story Behind Data Compression and LLM Performance

Reference 7

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source=pdf_text observed=2026-08-08T04:52:23.191844Z digest=sha256:c677429515e9bd693d3574a2c8fabdddb26845e4213127a8ce4185ab15215231

Observation c44ce3ad-0858-4d05-8ed6-d5e2d84e29fa · outbound

This paper cites Meta AI Blog (2024).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Meta AI Blog (2024)

Reference 8

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

source=pdf_text observed=2026-08-08T04:52:23.197528Z digest=sha256:7c4a60d9492022e6fc0ec71de9f3cd0dc365a9ddd96b7a8b08adff9084685fbb

Observation 8887f304-86bf-4a7e-ba4f-ef6e412f9c86 · outbound

This paper cites an unresolved cited work.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Unresolved cited work

Reference 9

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Observation 2877b46b-925d-4a9a-9c22-5034e06ce8bc · outbound

This paper cites Ollama (2024).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Ollama (2024)

Reference 10

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source=pdf_text observed=2026-08-08T04:52:23.206810Z digest=sha256:ae703e1b75a6efd1d75d956aa1281cf9284c0d0adf1784b02644c080c236862d

Observation c607ef94-38fc-42fc-9911-29b7707d32ed · outbound

This paper cites GitHub Gist (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data GitHub Gist (2025)

Reference 11

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Observation ee49f45c-1dd3-4629-9049-ef0035a89108 · outbound

This paper cites Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration

Reference 12

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

source=pdf_text observed=2026-08-08T04:52:23.215689Z digest=sha256:170e61cde26d3c79b12da55afdcb87a75913f7302276558ab11218bb2e08f837

Observation a33eaa09-810f-4975-900f-ecc1f20e7525 · outbound

This paper cites Calibrating Language Models with Adaptive Temperature Scaling.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Calibrating Language Models with Adaptive Temperature Scaling

Reference 13

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source=pdf_text observed=2026-08-08T04:52:23.220705Z digest=sha256:ec13e58b180c9b4dc6ba45a9941c306fcbbca658f540186d5490bf7740f58301

Observation cc5d2994-0ba9-4af5-9726-a771f7cecad1 · outbound

This paper cites Token-by-Token Regeneration and Domain Biases: A Benchmark of LLMs on Advanced Mathematical Problem-Solving.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Token-by-Token Regeneration and Domain Biases: A Benchmark of LLMs on Advanced Mathematical Problem-Solving

Reference 14

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source=pdf_text observed=2026-08-08T04:52:23.225246Z digest=sha256:24c9764902eec03195a6c12b9e733552294d4ca3180ff0662daa041222c57635

Observation 0cd40476-1b16-4693-a472-1ae87dd3682b · outbound

This paper cites Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH

Reference 15

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source=pdf_text observed=2026-08-08T04:52:23.229768Z digest=sha256:1e35b3f47c087f01d557e42b6ffc5af4f8aec4c9ec85726b0c0b3dedb0ed9ea0

Observation 5f0b294b-d50a-4257-9494-10810f97f766 · outbound

This paper cites Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models

Reference 16

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local_arxiv, observed 2026-08-08T04:52:23.584668Z

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

source=pdf_text observed=2026-08-08T04:52:23.234289Z digest=sha256:1bdfa5849a6713fc986846a8a8fb976fc769aeaa6a8f3e747f47c02c3f1d975a

Observation 77b37f95-f3a7-4983-b1fc-4868b52a92ae · outbound

This paper cites HyQE: Ranking Contexts with Hypothetical Query Embeddings.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data HyQE: Ranking Contexts with Hypothetical Query Embeddings

Reference 17

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Observation 84e97a89-bf88-46a3-b70d-12f866cf84ce · outbound

This paper cites Evaluating Front-end & Back-end of Human Automation Interaction Applications \Delta-EVAL A Hypothetical Benchmark.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Evaluating Front-end & Back-end of Human Automation Interaction Applications \Delta-EVAL A Hypothetical Benchmark

Reference 18

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

source=pdf_text observed=2026-08-08T04:52:23.243614Z digest=sha256:53df9f4a4971834715689d852c84b8f5fdf4c8514be229921e43c75ee1de7943

Observation aac5801d-d3bd-4a74-904c-9d916aad140a · outbound

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

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data LLaMA: Open and Efficient Foundation Language Models

Reference 19

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source=pdf_text observed=2026-08-08T04:52:23.248359Z digest=sha256:89ca636b4122769ee67eefab48471bf090a0eb75eb396e31deaab349a3b59af1

Observation 8e087027-51ad-4c45-97de-12af57c687e5 · outbound

This paper cites The Effect of Sampling Temperature on Problem Solving in Large Language Models.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data The Effect of Sampling Temperature on Problem Solving in Large Language Models

Reference 20

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source=pdf_text observed=2026-08-08T04:52:23.252999Z digest=sha256:68f22490c9d16d6912fd824930fda88939c02e514054c6cf8ba47a35283af5b5

Observation 967047e7-7973-4f94-984b-004be288a8b4 · outbound

This paper cites Pydantic 2.10.6 (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Pydantic 2.10.6 (2025)

Reference 21

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

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Observation 4af4e669-599c-44e2-bcdb-74d037c24345 · outbound

This paper cites https://www.python.org/downloads/release/python-31015/ (Accessed February 12, 2025, 9:47 AM).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data https://www.python.org/downloads/release/python-31015/ (Accessed February 12, 2025, 9:47 AM)

Reference 22

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source=pdf_text observed=2026-08-08T04:52:23.262124Z digest=sha256:b6898d4fa114f43902452f3f03d4bf3e758afde7d7eb306f6a4c0b62c0a06078

Observation 5b2ae626-8fd9-4ea7-b59d-fb35fe28a503 · outbound

This paper cites LangChain 0.3.18 (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data LangChain 0.3.18 (2025)

Reference 23

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Observation 1b8bd6d5-9f4b-4de4-a138-8caea7938bdd · outbound

This paper cites LangChain Core: ChatPromptTemplate Documentation (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data LangChain Core: ChatPromptTemplate Documentation (2025)

Reference 24

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source=pdf_text observed=2026-08-08T04:52:23.270918Z digest=sha256:b1706ba4635af922ad6bf08ce9fe5f5f0de9e554913b3257d3fbee60a8d47b07

Observation ab470df3-bcc0-4578-abf0-7f789d7c4c04 · outbound

This paper cites an unresolved cited work.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Unresolved cited work

Reference 25

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doi_truncated, observed 2026-08-08T04:52:23.332378Z

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source=pdf_text observed=2026-08-08T04:52:23.275095Z digest=sha256:97c781ea4f6d8f6907559a67ebaf180902ff2d1738f2d0b3f17c865081e6ed23

Observation 1138979e-f933-400c-a354-273813a9560b · outbound

This paper cites H., & Wallis, W.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data H., & Wallis, W

Reference 26

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source=pdf_text observed=2026-08-08T04:52:23.279738Z digest=sha256:55f73c4909851d90902b09e3ae66cf867e80dbaa49ccbd175d0906fa70681d3c

Observation 327a94cd-6942-48fb-b783-06a5dc683ada · outbound

This paper cites Pandas 2.2.3 (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Pandas 2.2.3 (2025)

Reference 27

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source=pdf_text observed=2026-08-08T04:52:23.283978Z digest=sha256:a856297fdc50e246c804a1f0a2d4f40f199b9e7bd2f9bc70ae280aba2b24ce84

Observation bb0e6d75-a08c-4e91-a54c-71e3c52482b6 · outbound

This paper cites SciPy 1.15.1 (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data SciPy 1.15.1 (2025)

Reference 28

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source=pdf_text observed=2026-08-08T04:52:23.288286Z digest=sha256:729b5515951ebc64b3f1b5037f69c641e76671f571d4586453d60ca6b05181d4

Observation 71be2c54-3dd7-4c72-a676-960adc0f21aa · outbound

This paper cites Matplotlib 3.10.0 (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Matplotlib 3.10.0 (2025)

Reference 29

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source=pdf_text observed=2026-08-08T04:52:23.292895Z digest=sha256:1f3b396bdc37639fd8b5ca72083246e64f2b86be8adda123a5ad6058eff18dd3

Observation bf7a6fe9-b6a7-4db7-8952-0a078f062ba2 · outbound

This paper cites Seaborn 0.13.2 (2025).

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Seaborn 0.13.2 (2025)

Reference 30

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

source=pdf_text observed=2026-08-08T04:52:23.297314Z digest=sha256:d4fc7b40e075eb3ae7196285527f3f9a512c1c1d13a88e2f6c16183ac5e81f1b

Pith citing papers

Observation adc1fee0-9437-4bc1-8a4a-cde13e734162 · inbound

Quantifying Trust: Financial Risk Management for Trustworthy AI Agents cites this paper.

Quantifying Trust: Financial Risk Management for Trustworthy AI Agents The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data

Reference 14

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arxiv_id, observed 2026-05-13T17:18:01.281553Z

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source=pdf_text observed=2026-05-13T17:16:17.464937Z digest=sha256:c992f6cde5c2d8e9909236ff48dc809c7538022b9151b3b977a0db014aa04c0e