Paper Citation Record · LEDGER

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 7 inbound Pith citation observations for arXiv:2502.01618.

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

pith.paper-citation-record.v1
2502.01618 v5

Coverage vector

measured 50 of 50 reference resolution

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One-hop event checks from named stored sources.

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measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T08:07:45.281283Z

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 6cf1ad45-c8e6-4f75-9a4d-41512460d493 · outbound

This paper cites Aimo validation aime dataset.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Aimo validation aime dataset

Reference 1

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Observation 31548382-63a9-4055-9e5a-05157d634b19 · outbound

This paper cites Particle Markov Chain Monte Carlo Methods.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Particle Markov Chain Monte Carlo Methods

Reference 2

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Observation 246aa963-c717-4fe3-8d79-403f31695bd5 · outbound

This paper cites Scaling test-time compute with open models, 2024.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Scaling test-time compute with open models, 2024

Reference 3

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Observation 738b7722-00aa-4a9c-9e4a-deaa13c7eb61 · outbound

This paper cites Le, Christopher Ré, and Azalia Mirhoseini.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Le, Christopher Ré, and Azalia Mirhoseini

Reference 4

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This paper cites Boltzmann exploration done right.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Boltzmann exploration done right

Reference 5

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Observation 25d444e2-e10a-4664-a7d4-6efc69916b98 · outbound

This paper cites Process reinforcement through implicit rewards, 2025.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Process reinforcement through implicit rewards, 2025

Reference 6

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Observation 3dc6ba93-63eb-41d7-b6b2-bbdc0970c850 · outbound

This paper cites Sequential Monte Carlo Methods for Dynamic Sys- tems: Journal of the American Statistical Association: V ol 93, No 443.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Sequential Monte Carlo Methods for Dynamic Sys- tems: Journal of the American Statistical Association: V ol 93, No 443

Reference 7

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This paper cites Step-by-step reasoning for math problems via twisted sequential monte carlo, 2024.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Step-by-step reasoning for math problems via twisted sequential monte carlo, 2024

Reference 8

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Observation 8352d0c6-6a12-4ae5-b727-3ee85f24fabf · outbound

This paper cites Tenenbaum, Vikash K.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Tenenbaum, Vikash K

Reference 9

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Observation 65ea630c-6b11-4702-9926-65cae2a7b741 · outbound

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Unresolved cited work

Reference 10

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Observation a0e3e554-aae0-43ff-834a-9263594f981b · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking, January 2025.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking, January 2025

Reference 11

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Observation 1e3684b5-b334-44c3-9459-1e311ad348b8 · outbound

This paper cites Financebench: A new benchmark for financial question answering, 2023.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Financebench: A new benchmark for financial question answering, 2023

Reference 12

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This paper cites Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

Reference 13

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This paper cites Algorithms for multi-armed bandit problems.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Algorithms for multi-armed bandit problems

Reference 14

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Observation 606e6a64-7722-47e8-80d1-f5bf1745655c · outbound

This paper cites Lew, Tan Zhi-Xuan, Gabriel Grand, and Vikash K.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Lew, Tan Zhi-Xuan, Gabriel Grand, and Vikash K

Reference 15

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Observation bdffaacf-5bd5-43cf-a170-b40dc608b19e · outbound

This paper cites Let’s verify step by step, 2023.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Let’s verify step by step, 2023

Reference 16

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This paper cites Let’s Verify Step by Step, May 2023.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Let’s Verify Step by Step, May 2023

Reference 17

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Lew, Tim Vieira, and Timothy J

Reference 18

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This paper cites Cambridge university press, 2003.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Cambridge university press, 2003

Reference 19

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This paper cites Numglue: A suite of fundamental yet challenging mathematical reasoning tasks.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Numglue: A suite of fundamental yet challenging mathematical reasoning tasks

Reference 20

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Unresolved cited work

Reference 21

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Observation 56ffbf9b-fd15-4f8f-b735-a77fe4d51089 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters, August 2024.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters, August 2024

Reference 22

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This paper cites Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024

Reference 23

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This paper cites Swendsen and Jian-Sheng Wang.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Swendsen and Jian-Sheng Wang

Reference 24

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Bayesian Filtering and Smoothing

Reference 25

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Unresolved cited work

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This paper cites Self-consistency improves chain of thought reasoning in language models, 2023.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Self-consistency improves chain of thought reasoning in language models, 2023

Reference 27

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This paper cites An implementation of generative prm.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods An implementation of generative prm

Reference 28

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

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 29

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This paper cites Advancing llm reasoning generalists with preference trees, 2024.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Advancing llm reasoning generalists with preference trees, 2024

Reference 30

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Observation 779de308-875c-4c20-893a-0bdb876919a0 · outbound

This paper cites The Lessons of Developing Process Reward Models in Mathematical Reasoning.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 31

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Probabilistic inference in language models via twisted sequential monte carlo, 2024

Reference 33

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This paper cites Language agent tree search unifies reasoning acting and planning in language models, 2024.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Language agent tree search unifies reasoning acting and planning in language models, 2024

Reference 34

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Observation 63c25e6e-04e6-42f9-997e-a5ea7773fb83 · outbound

This paper cites Please see the Evaluation section for our empirical results.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Please see the Evaluation section for our empirical results

Reference 35

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This paper cites Limitations.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Limitations

Reference 36

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This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 37

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods We also provide key details into the hyperparameter selection and ablation process, which significantly helps reproducability

Reference 38

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This paper cites How- ever, we will completely open source our code upon acceptance of the paper to encourage as many people as possible to use our work.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods How- ever, we will completely open source our code upon acceptance of the paper to encourage as many people as possible to use our work

Reference 39

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods We list exactly which models and versions we used as generator models

Reference 40

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Unresolved cited work

Reference 41

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Unresolved cited work

Reference 42

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Observation 34b075fe-e292-4ef8-b697-e4e0e7c166dd · outbound

This paper cites Our research does not have negative societal consequences, nor does it involve human subjects.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Our research does not have negative societal consequences, nor does it involve human subjects

Reference 43

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This paper cites We discuss the positive impacts of inference scaling, as it opens up higher level language model performance to those who are only able to access smaller models.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods We discuss the positive impacts of inference scaling, as it opens up higher level language model performance to those who are only able to access smaller models

Reference 44

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Observation cbf935d6-9b49-43c5-b2e3-0ce9aa5d224a · outbound

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Instead, we only use off-the-shelf open source models, and therefore there are no possibilities of misuse

Reference 45

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Therefore, all creators of the original models are credited in this work

Reference 46

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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Justification: We do not release any new assets in this paper - instead, we discuss how to enhance the performance of already existing open-sourced models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:53:26.347055Z

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 6bb54aaf-27b9-4583-8f3f-53d788819ea2 · outbound

This paper cites Guidelines: 26 • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Guidelines: 26 • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:53:26.334857Z

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 baaaed20-58e6-4731-bdcb-4dee1a803cf3 · outbound

This paper cites Justification: Our work does not include any human subjects, and we did not need IRB approvals.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Justification: Our work does not include any human subjects, and we did not need IRB approvals

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:53:26.302721Z

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 4d48cda5-96af-418f-9092-aa029bb5e4e8 · outbound

This paper cites Answer: [NA] Justification: LLM usage did not impact the core methodology, scientific rigorousness, or originality of the research.

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods Answer: [NA] Justification: LLM usage did not impact the core methodology, scientific rigorousness, or originality of the research

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:53:26.221809Z

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.

Pith citing papers

Observation a83d1d9b-98fd-429e-8173-cf0deb8bcb9d · inbound

Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo cites this paper.

Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T12:21:21.635257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c75a4f7a-0f72-48d6-84ff-dbe03275e144 · inbound

Soft Best-of-n Sampling for Model Alignment cites this paper.

Soft Best-of-n Sampling for Model Alignment Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T00:05:48.297295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da84079e-e322-4881-b5e9-fcb073f7d014 · inbound

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning cites this paper.

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:01:38.371687Z

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 e8aaa572-1324-4352-99f9-19c56d0c3dbc · inbound

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement cites this paper.

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:02:58.822888Z

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 1717e218-1d08-4c5f-84ab-a7999bc19126 · inbound

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling cites this paper.

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:30:59.602288Z

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 6738b39e-23a9-4f27-a64f-510004607c15 · inbound

TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation cites this paper.

TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:21:30.832746Z

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 320fd871-47a7-4018-ab0e-1d13044146ac · inbound

The Power of Test-Time Training for Approximate Sampling cites this paper.

The Power of Test-Time Training for Approximate Sampling Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:07:45.282752Z

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.