Paper Citation Record · LEDGER

Base Models Beat Aligned Models at Randomness and Creativity

As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 15 inbound Pith citation observations for arXiv:2505.00047.

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

pith.paper-citation-record.v1
2505.00047 v2

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:10:56.304247Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:21:41.732145Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:47:41.530108Z

Reference resolution

55 of 55 outbound references displayed

verified exact2
verified fuzzy18
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 708ad45d-d6cc-4ce8-bf61-e959f0e0b251 · outbound

This paper cites Playing repeated games with Large Language Models.

Base Models Beat Aligned Models at Randomness and Creativity Playing repeated games with Large Language Models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d35e9df2-0531-4c3e-b8a0-dda9471dceba · outbound

This paper cites Homogenization effects of large language models on human creative ideation.

Base Models Beat Aligned Models at Randomness and Creativity Homogenization effects of large language models on human creative ideation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.045122Z

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Observation b1c1363b-6ea2-4ee2-abb4-2929bb9d2ed0 · outbound

This paper cites The Claude 3 model family: Opus , Sonnet , Haiku.

Base Models Beat Aligned Models at Randomness and Creativity The Claude 3 model family: Opus , Sonnet , Haiku

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.427677Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0932c144-f96a-4523-98b1-2ea7dc40ddfa · outbound

This paper cites Bigelow, Ekdeep Singh Lubana, Robert P.

Base Models Beat Aligned Models at Randomness and Creativity Bigelow, Ekdeep Singh Lubana, Robert P

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.412284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6629dd05-177a-442f-a2a5-453605d7e436 · outbound

This paper cites an unresolved cited work.

Base Models Beat Aligned Models at Randomness and Creativity Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.060363Z

Source-reported events for the cited work

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Observation 1c9ec26b-b7f9-427a-98ea-e596177328e6 · outbound

This paper cites Exploring precision and recall to assess the quality and diversity of LLMs.

Base Models Beat Aligned Models at Randomness and Creativity Exploring precision and recall to assess the quality and diversity of LLMs

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.397568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a83ef079-3fb5-4293-9b27-aec9fbfa511c · outbound

This paper cites Playing games with GPT : What can we learn about a large language model from canonical strategic games? SSRN Electronic Journal, 2023.

Base Models Beat Aligned Models at Randomness and Creativity Playing games with GPT : What can we learn about a large language model from canonical strategic games? SSRN Electronic Journal, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.382856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1d3320d7-2fcd-4385-bff1-73a80c7496f9 · outbound

This paper cites Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers.

Base Models Beat Aligned Models at Randomness and Creativity Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.075486Z

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Observation e7f06846-7a66-42d8-9022-b2fd590486e6 · outbound

This paper cites The use of maximum likelihood estimates in ^2 tests for goodness of fit.

Base Models Beat Aligned Models at Randomness and Creativity The use of maximum likelihood estimates in ^2 tests for goodness of fit

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.368203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a61028d-8341-470b-9805-09537f2fb852 · outbound

This paper cites A coefficient of agreement for nominal scales.

Base Models Beat Aligned Models at Randomness and Creativity A coefficient of agreement for nominal scales

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.085194Z

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Observation f20e2e47-2572-497d-8c39-5e3e0ec8a70d · outbound

This paper cites The Llama 3 Herd of Models.

Base Models Beat Aligned Models at Randomness and Creativity The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.089816Z

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Observation 222da710-39ec-4f86-a33c-591dd392e498 · outbound

This paper cites Nudging: Inference-time Alignment of LLMs via Guided Decoding.

Base Models Beat Aligned Models at Randomness and Creativity Nudging: Inference-time Alignment of LLMs via Guided Decoding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.095131Z

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Observation 170ac7b7-8712-4e17-9be4-e8bd46ad0836 · outbound

This paper cites Fienberg and Colin Martindale.

Base Models Beat Aligned Models at Randomness and Creativity Fienberg and Colin Martindale

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.353540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79e52f38-e2eb-414e-a030-4be0f464e56c · outbound

This paper cites Open LLM Leaderboard v2.

Base Models Beat Aligned Models at Randomness and Creativity Open LLM Leaderboard v2

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.338794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd3354be-fe75-4305-a6ac-23fd8fd24a14 · outbound

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

Base Models Beat Aligned Models at Randomness and Creativity Gemini: A Family of Highly Capable Multimodal Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.110913Z

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Observation fa25d10b-875c-44f8-a9aa-02c5f5d4b2f7 · outbound

This paper cites A confederacy of models: a comprehensive evaluation of LLM s on creative writing.

Base Models Beat Aligned Models at Randomness and Creativity A confederacy of models: a comprehensive evaluation of LLM s on creative writing

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.117005Z

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Observation fd1db822-c49f-4d6c-ab5b-b52ba1ade214 · outbound

This paper cites Instruction Following without Instruction Tuning.

Base Models Beat Aligned Models at Randomness and Creativity Instruction Following without Instruction Tuning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.121943Z

Source-reported events for the cited work

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Observation 41377c06-0729-4433-b355-210c3b113d67 · outbound

This paper cites Can LLM s generate random numbers? Evaluating LLM sampling in controlled domains.

Base Models Beat Aligned Models at Randomness and Creativity Can LLM s generate random numbers? Evaluating LLM sampling in controlled domains

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.323663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 909f7643-f3e4-4de3-bf99-261b4a6b68ca · outbound

This paper cites Instructed to bias: Instruction-tuned language models exhibit emergent cognitive bias.

Base Models Beat Aligned Models at Randomness and Creativity Instructed to bias: Instruction-tuned language models exhibit emergent cognitive bias

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.133547Z

Source-reported events for the cited work

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Observation 6bec7a55-5ff1-4cf1-91f4-262391db0c6b · outbound

This paper cites McNamara, and Deming Chen.

Base Models Beat Aligned Models at Randomness and Creativity McNamara, and Deming Chen

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.138150Z

Source-reported events for the cited work

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Observation 73b71e39-17bb-4991-bfdb-e17e211a7a42 · outbound

This paper cites Capturing failures of large language models via human cognitive biases.

Base Models Beat Aligned Models at Randomness and Creativity Capturing failures of large language models via human cognitive biases

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.308309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 824a58ad-4fc2-45dc-bf5f-380fb5f0abf3 · outbound

This paper cites Scaling Laws for Neural Language Models.

Base Models Beat Aligned Models at Randomness and Creativity Scaling Laws for Neural Language Models

Reference 22

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Observation 446f1ab9-6b19-4333-a390-20a7d345fd06 · outbound

This paper cites I am code: An artificial intelligence speaks.

Base Models Beat Aligned Models at Randomness and Creativity I am code: An artificial intelligence speaks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.293331Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b101af5c-0eaf-41d0-ac0d-3c39cdb04c79 · outbound

This paper cites That other guy.

Base Models Beat Aligned Models at Randomness and Creativity That other guy

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.277061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4afa23c9-0dbc-4115-933f-3f5e42aff0c2 · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

Base Models Beat Aligned Models at Randomness and Creativity Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.161671Z

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Observation ef62d485-c61d-4f47-a287-d123af4f56da · outbound

This paper cites Understanding the effects of RLHF on LLM generalisation and diversity.

Base Models Beat Aligned Models at Randomness and Creativity Understanding the effects of RLHF on LLM generalisation and diversity

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.260916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c6f2a58-5b44-4e8e-92bd-a4ce61463902 · outbound

This paper cites How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips.

Base Models Beat Aligned Models at Randomness and Creativity How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.171002Z

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Observation d0d54299-54ea-4a27-b8d3-4e85192d1f86 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Base Models Beat Aligned Models at Randomness and Creativity Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.175577Z

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Observation 73509cf9-4ade-4f62-a16d-81f8f6ce17ac · outbound

This paper cites Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling.

Base Models Beat Aligned Models at Randomness and Creativity Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling

Reference 29

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Observation 12e73ef4-0399-4756-a55b-424cdbea3502 · outbound

This paper cites The unlocking spell on base LLM s: Rethinking alignment via in-context learning.

Base Models Beat Aligned Models at Randomness and Creativity The unlocking spell on base LLM s: Rethinking alignment via in-context learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.246198Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a060d78d-8b9d-4bb4-a57f-2ef22cb5a3ab · outbound

This paper cites Griffiths.

Base Models Beat Aligned Models at Randomness and Creativity Griffiths

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.231351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 730c2e3d-32d4-4745-b16c-fcdd7516a742 · outbound

This paper cites AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text.

Base Models Beat Aligned Models at Randomness and Creativity AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text

Reference 32

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Observation d9e0fd8a-b128-43be-90cc-71045cc01578 · outbound

This paper cites Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve.

Base Models Beat Aligned Models at Randomness and Creativity Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

Reference 33

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Observation 2010b451-00ec-46a8-a558-d7ef57d96554 · outbound

This paper cites Benchmarking Distributional Alignment of Large Language Models.

Base Models Beat Aligned Models at Randomness and Creativity Benchmarking Distributional Alignment of Large Language Models

Reference 34

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Observation 7b418be0-67eb-4f72-a9fa-4b4537be7527 · outbound

This paper cites Why is this number everywhere? https://www.youtube.com/watch?v=d6iQrh2TK98, 2024.

Base Models Beat Aligned Models at Randomness and Creativity Why is this number everywhere? https://www.youtube.com/watch?v=d6iQrh2TK98, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.216169Z

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Observation 377d8538-f33c-44ee-9e7d-532b8736cedd · outbound

This paper cites One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity.

Base Models Beat Aligned Models at Randomness and Creativity One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.214260Z

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Observation 389a3058-1681-455d-9253-225818875c10 · outbound

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Base Models Beat Aligned Models at Randomness and Creativity Unresolved cited work

Reference 37

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2151e7fd-f128-4bc4-9577-4118564954b3 · outbound

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

Base Models Beat Aligned Models at Randomness and Creativity Training language models to follow instructions with human feedback

Reference 38

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Observation aad3f0c2-7542-4d0d-aaab-28dbda5505aa · outbound

This paper cites Does Writing with Language Models Reduce Content Diversity?.

Base Models Beat Aligned Models at Randomness and Creativity Does Writing with Language Models Reduce Content Diversity?

Reference 39

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Observation 92bf5016-4972-4811-b079-b3f83d39eaf9 · outbound

This paper cites What are the odds? Language models are capable of probabilistic reasoning.

Base Models Beat Aligned Models at Randomness and Creativity What are the odds? Language models are capable of probabilistic reasoning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.176044Z

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Observation ad7d215a-173d-452d-a8ba-6934aadc303c · outbound

This paper cites Qwen2.5 Technical Report.

Base Models Beat Aligned Models at Randomness and Creativity Qwen2.5 Technical Report

Reference 41

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Observation 4c234463-a0b4-46f9-968d-9d805248bbd8 · outbound

This paper cites Whose opinions do language models reflect? In Proceedings of the 40th International Conference on Machine Learning, ICML'23.

Base Models Beat Aligned Models at Randomness and Creativity Whose opinions do language models reflect? In Proceedings of the 40th International Conference on Machine Learning, ICML'23

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.161099Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6628146-e059-40bb-8195-1aa3eb58b989 · outbound

This paper cites Analysing humanly generated random number sequences: A pattern-based approach.

Base Models Beat Aligned Models at Randomness and Creativity Analysing humanly generated random number sequences: A pattern-based approach

Reference 43

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Observation e1fa08dd-6a92-4553-9c20-bc0496021951 · outbound

This paper cites Evaluating the diversity and quality of llm generated content, 2025.

Base Models Beat Aligned Models at Randomness and Creativity Evaluating the diversity and quality of llm generated content, 2025

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.252960Z

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Observation 2a584e21-b52f-4c47-87f7-1cfc4d0a35da · outbound

This paper cites Large language models playing mixed strategy nash equilibrium games.

Base Models Beat Aligned Models at Randomness and Creativity Large language models playing mixed strategy nash equilibrium games

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:10:57.146747Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4b3432cd-6a9d-4e0d-9f3d-10c1d33f09b9 · outbound

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Base Models Beat Aligned Models at Randomness and Creativity Unresolved cited work

Reference 46

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Observation 904b8b03-dacb-477d-86a5-bd44d12ffd8d · outbound

This paper cites The good, the bad, and the greedy: Evaluation of LLM s should not ignore non-determinism.

Base Models Beat Aligned Models at Randomness and Creativity The good, the bad, and the greedy: Evaluation of LLM s should not ignore non-determinism

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T05:10:56.266203Z

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Observation 244160b3-9244-4521-8e15-e020793dc15a · outbound

This paper cites Spearman.

Base Models Beat Aligned Models at Randomness and Creativity Spearman

Reference 48

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Observation efe1c7c2-1dda-4543-acc5-32445536cec4 · outbound

This paper cites The relative yields of heavy hadrons as function of transverse momentum at LHC experiments.

Base Models Beat Aligned Models at Randomness and Creativity The relative yields of heavy hadrons as function of transverse momentum at LHC experiments

Reference 49

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Observation 421f27d9-4b54-4afc-b2ec-92fbb42f4777 · outbound

This paper cites von Neumann and O.

Base Models Beat Aligned Models at Randomness and Creativity von Neumann and O

Reference 50

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Observation d3c12c57-6a01-412b-b2a8-2dc4bec8befa · outbound

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Base Models Beat Aligned Models at Randomness and Creativity Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:10:57.112752Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42465889-9034-4f5c-948d-970054b2a291 · outbound

This paper cites write newline.

Base Models Beat Aligned Models at Randomness and Creativity write newline

Reference 52

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Observation e88d627a-b26a-44b8-a97f-40fe015b5a4d · outbound

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Base Models Beat Aligned Models at Randomness and Creativity @esa (Ref

Reference 53

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Observation fcb6279d-2eba-4f85-ac66-5c922b389d57 · outbound

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Base Models Beat Aligned Models at Randomness and Creativity Unresolved cited work

Reference 54

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Observation eb17868e-6895-4b62-a311-4597c5f45690 · outbound

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Base Models Beat Aligned Models at Randomness and Creativity Unresolved cited work

Reference 55

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Pith citing papers

Observation fe47ffdc-66f5-4879-8e9c-6fde23034d61 · inbound

Effective Reinforcement Learning for Reasoning in Language Models cites this paper.

Effective Reinforcement Learning for Reasoning in Language Models Base Models Beat Aligned Models at Randomness and Creativity

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T14:56:18.386633Z

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Observation ea4ef6c8-cf92-4d34-bb53-3365c52bbbdf · inbound

Get Experience from Practice: LLM Agents with Record & Replay cites this paper.

Get Experience from Practice: LLM Agents with Record & Replay Base Models Beat Aligned Models at Randomness and Creativity

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:30.481737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 296f2663-eb36-48ae-a6cf-d0242145ba0e · inbound

When Two LLMs Debate, Both Think They'll Win cites this paper.

When Two LLMs Debate, Both Think They'll Win Base Models Beat Aligned Models at Randomness and Creativity

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:14.491454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb8110d9-8cdc-4c85-9942-4308c53275ce · inbound

Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity cites this paper.

Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity Base Models Beat Aligned Models at Randomness and Creativity

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T13:23:15.773796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57d53a2d-d128-49d7-ab88-e7d34ea5cd93 · inbound

The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety cites this paper.

The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety Base Models Beat Aligned Models at Randomness and Creativity

Reference 136

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:53:11.558569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8eef223-f0d5-4742-9e28-2f34c4023cbd · inbound

The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety cites this paper.

The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety Base Models Beat Aligned Models at Randomness and Creativity

Reference 136

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:10:24.907523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b30a9207-71b9-4601-8061-bc8ed59fc562 · inbound

The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety cites this paper.

The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety Base Models Beat Aligned Models at Randomness and Creativity

Reference 135

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:29.007410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49a342f1-b9c3-4977-a900-f41c057ad8b7 · inbound

Annotations Mitigate Post-Training Mode Collapse cites this paper.

Annotations Mitigate Post-Training Mode Collapse Base Models Beat Aligned Models at Randomness and Creativity

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:46:51.955719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0300a2d2-00e0-47f8-9b65-ece74a6a8a09 · inbound

Unlocking LLM Creativity in Science through Analogical Reasoning cites this paper.

Unlocking LLM Creativity in Science through Analogical Reasoning Base Models Beat Aligned Models at Randomness and Creativity

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:57:05.581311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 39fd9c9e-b1a0-4e81-bff1-995a15f55501 · inbound

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection cites this paper.

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection Base Models Beat Aligned Models at Randomness and Creativity

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:03:29.893389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation de69a4aa-ae4d-456e-8aaa-8f36c070ffaa · inbound

Fine-Tuning Improves Information Conveyance in Language Models cites this paper.

Fine-Tuning Improves Information Conveyance in Language Models Base Models Beat Aligned Models at Randomness and Creativity

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:02:46.661372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bc7304dd-2a27-4115-bbbc-685510b96852 · inbound

IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs cites this paper.

IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs Base Models Beat Aligned Models at Randomness and Creativity

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T18:42:28.914736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f4428ae-3bee-415a-8112-d117cae3e0b6 · inbound

AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable cites this paper.

AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable Base Models Beat Aligned Models at Randomness and Creativity

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:47:41.531784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4f3649f-2ab9-4ae9-8eec-fc1902e19516 · inbound

Towards Physical Intuitions for Alignment Dynamics: A Case Study With Randomness Crystallization cites this paper.

Towards Physical Intuitions for Alignment Dynamics: A Case Study With Randomness Crystallization Base Models Beat Aligned Models at Randomness and Creativity

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:44:19.138941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40a31ae8-7561-499b-8e16-712c9ee991fe · inbound

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity cites this paper.

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity Base Models Beat Aligned Models at Randomness and Creativity

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T04:21:41.732145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.