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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 8 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 2026-08-08T06:32:00.761636+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=sha256:2d6ec7b7fa5dbec126b8f1731be673394d706948b6decd406867f8e2b0897a01

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:b0b72320985ecf35bfaabd7d1fe8b4136e26882059db9535245a097e9e440b42

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:c512dcf17105b30a892819d0f0818e5dc9e31c47e42c5c058be9721c2b1d43f3

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

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.

source=pdf_text observed=2026-08-08T04:52:23.181845Z digest=sha256:ddc79f47e61f0e789cf3c3c0ffa586a35d673c9675a288bf00fd0c9615d18cbb

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=sha256:4c962581a40c9c749b24338cfa7efb3273ff8c24ef3eb5307eb05afded57e37a

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:d6698c2d7483bcdef5d93de6003661ed975b6c4a4a878e239222ae6156d3f5ed

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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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.

source=pdf_text observed=2026-08-08T04:52:23.197528Z digest=sha256:a928aafd8fd4b5e5bc9c56ad098dc01f449f919a821c563840b012df81cefac1

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.202206Z digest=sha256:ce78dbf2b37a0805e44fd1cc8df2832523336cb54d95dd8871fe3d496a40bf4e

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.206810Z digest=sha256:96e1b33d31ae3cdca4788877e7af7c961d8f5888dd508449e39eddff49b77d1b

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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source=pdf_text observed=2026-08-08T04:52:23.211230Z digest=sha256:df40e1e8e4e934d32f9ee9582d889bdb570bf345c86ce87e25cd7b5e07f89835

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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local_arxiv, observed 2026-08-08T04:52:23.650012Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.215689Z digest=sha256:8692e61868e085d154a22d6cb20418c46fc31ed5065d8afe2f389613058effd7

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=sha256:1f78c1929d233e598229fc34260f97355a36ab47953108ba4da7923b35c02e9f

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=sha256:2a5194bf8d649d410774f79fe1a56e3c555e6298c4a0c06eff1ac7865d45b6b2

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:ec94ce325d52ae6ee088acf04c075f1df0ad69ee3cf5bfe5f09b6dc2a73e71f6

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.234289Z digest=sha256:3d8a060b9b59534eca0e85a2564e8f2fdf6b260ea1f719efd3af0e3642a771d0

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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source=pdf_text observed=2026-08-08T04:52:23.239044Z digest=sha256:28376c78121059f122c5814b1bc52339df500c162ea516790285bad1ff2f2966

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.243614Z digest=sha256:25c991c2ae9296e5f1920ca1e9016d7dd846709baa46c85b5d8fa293e200d480

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:beea4c06e6f04efc1fe39553256f558e274624e09823ef6fde45081627014a3b

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=sha256:46f533fa1b3081654e529c1725a8b9078b9cb8d0f26fd6c110afeddf1e5e2964

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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source=pdf_text observed=2026-08-08T04:52:23.257763Z digest=sha256:4139171d107bcb4dd19aa5be73b537a6caae07644d677bbc816077b4c1a0f8b5

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=sha256:004f7ead3bcb2c2fcece3a8b9dad9018234059d1047dbe291958f395880646b6

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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source=pdf_text observed=2026-08-08T04:52:23.266549Z digest=sha256:1aa937f67a1f8fd120c70e6bffe05d067007c8afa25dee3477c901869142d7c3

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=sha256:11b1ccd92fadaaae0b2af07b94b1ac4fe66f5ff08b756a2ba0a7cb9acc3accaf

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:c6e4ce8194eb8b8cb4170a96199f9ef90b9866f34891e55b7dc3a272f981ffb5

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=sha256:70127bfd1e8ef5f461d127ffcbdd7fb484911c195a9b1cb6992bf3c24d43ddfc

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:a311475d617d913bea38258a6d63166ebbf613c7d6eccc38e93671b215f98143

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.288286Z digest=sha256:4bde25233f72928cc13c795c0ef08d1c0870d4315abda3db766c8ebf8b6abf4e

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=sha256:3e91d9d7b9eb938280f089d77ce757680d26cfb270e72290f3ae396faf0853c7

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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raw_fallback, observed 2026-08-08T04:52:23.760530Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T04:52:23.297314Z digest=sha256:bc64566867c0f1c1096124803caa2eaa5a17c69423841b9507b850dea1b3bfb0

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T17:16:17.464937Z digest=sha256:0e5828229fa470ce0ff5abb8051cf2b637d421a9015fa83a46e02a70bac34e83