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Paper Citation Record · LEDGER

Bag of Tricks for Inference-time Computation of LLM Reasoning

As of 9 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 7 inbound Pith citation observations for arXiv:2502.07191.

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

pith.paper-citation-record.v1
2502.07191 v4

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:35:24.602784Z

measured 31 of 31 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:41:48.356267Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:51:37.638767Z

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e92021b1-0c6e-4369-8774-ec43f7b0ccf9 · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

Bag of Tricks for Inference-time Computation of LLM Reasoning Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 3

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source=pdf_text observed=2026-08-08T13:35:24.496163Z digest=sha256:c004068364a784e8ff9f30a87b1bcc062d672b9d3e4942b81ab6381cc548ba04

Observation a4a0bb73-97e3-4d22-8083-82e790f1986d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Bag of Tricks for Inference-time Computation of LLM Reasoning Distilling the Knowledge in a Neural Network

Reference 5

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source=pdf_text observed=2026-08-08T13:35:24.506958Z digest=sha256:2a44588cb2d97161be6b3ae37691c05cabd5c2476453bf01a78d763972008c8b

Observation 0ee1a889-b74a-42fe-bec0-1dc94751e5a9 · outbound

This paper cites Crafting papers on machine learning.

Bag of Tricks for Inference-time Computation of LLM Reasoning Crafting papers on machine learning

Reference 9

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source=pdf_text observed=2026-08-08T13:35:24.527343Z digest=sha256:83529e0e3e2e97932481b1817722d5100e2698850d4082779e0d494e8c2d7b17

Observation 99c6c1d3-98f2-44ab-aeff-8bfbc7d737c0 · outbound

This paper cites RegMix: Data Mixture as Regression for Language Model Pre-training.

Bag of Tricks for Inference-time Computation of LLM Reasoning RegMix: Data Mixture as Regression for Language Model Pre-training

Reference 11

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source=pdf_text observed=2026-08-08T13:35:24.536704Z digest=sha256:12c09d440e4298bfe9ef7943da82cc3fcbe0086e41dc2dcc4be82b96b979ebca

Observation ef1b2113-6115-4230-9403-cf188911653c · outbound

This paper cites R., Smith, C., Das, R.

Bag of Tricks for Inference-time Computation of LLM Reasoning R., Smith, C., Das, R

Reference 12

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source=pdf_text observed=2026-08-08T13:35:24.541930Z digest=sha256:4548144fac9d0925bd87d84188fdaa22797662b39a77ca44eaf5167dd5b9a587

Observation 4c0c8652-dd1f-4eaa-bec0-0b42a7717613 · outbound

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

Bag of Tricks for Inference-time Computation of LLM Reasoning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 14

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source=pdf_text observed=2026-08-08T13:35:24.551894Z digest=sha256:0169be97d841b2233b28de1c0b904568d32b21ab73389eaa7fa69cb0b5735eaf

Observation 5e2757f4-1766-416d-8c6f-3bf8f7f6bdfc · outbound

This paper cites Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing.

Bag of Tricks for Inference-time Computation of LLM Reasoning Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Reference 15

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source=pdf_text observed=2026-08-08T13:35:24.557180Z digest=sha256:0f79284a5c1d095562bae81381ae29ab3a9576625c972cc44fe2e11731a27bf9

Observation ee4d89ca-474f-448c-b6c4-f9fc2a16800b · outbound

This paper cites Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning.

Bag of Tricks for Inference-time Computation of LLM Reasoning Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

Reference 16

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source=pdf_text observed=2026-08-08T13:35:24.562156Z digest=sha256:c13096298ebf955fd28868091b5041ba9998df171f6092c17acf3c78125594eb

Observation d773cd15-ee19-4623-b036-d85c4df23f7b · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Bag of Tricks for Inference-time Computation of LLM Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 17

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source=pdf_text observed=2026-08-08T13:35:24.567419Z digest=sha256:49713c46e636ead1f033d16db0cbbe3c6bfcfb9ef513cfd68321a29354c0e28b

Observation bd15d04d-b6c1-475f-b6ea-de8791566fb2 · outbound

This paper cites Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS.

Bag of Tricks for Inference-time Computation of LLM Reasoning Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS

Reference 18

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source=pdf_text observed=2026-08-08T13:35:24.572530Z digest=sha256:28cb6b48e9cf75b449dfe1f173744be67da379d294116ed40af1cbe1414470e6

Observation 0894cf0f-cfe4-410f-ae57-b87498700303 · outbound

This paper cites Qwen2.5 Technical Report.

Bag of Tricks for Inference-time Computation of LLM Reasoning Qwen2.5 Technical Report

Reference 19

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source=pdf_text observed=2026-08-08T13:35:24.577566Z digest=sha256:3561f88d9d6e159e1c61a6d4a265ba615b64ee9264872c6f952dacfbe0f9df09

Observation 23e91757-6591-443e-858b-3273ae3a8daf · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

Bag of Tricks for Inference-time Computation of LLM Reasoning Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 20

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source=pdf_text observed=2026-08-08T13:35:24.582690Z digest=sha256:b7d50a7fe91c117653107811ea18da08ac1b3b96e18ba49f833c5c916a9d99c8

Observation 94c97b9b-3d97-43ea-8941-dd95da637590 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Bag of Tricks for Inference-time Computation of LLM Reasoning Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 22

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source=pdf_text observed=2026-08-08T13:35:24.592761Z digest=sha256:20ea35cd4ceb1ee7b795a5326152dc07c18e93e7c0a2532a397fbe9e4a08495c

Observation e1ca2937-3e8a-460f-8ea5-17c85402e0ed · outbound

This paper cites an unresolved cited work.

Bag of Tricks for Inference-time Computation of LLM Reasoning Unresolved cited work

Reference 24

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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=2026-08-08T13:35:24.602784Z digest=sha256:9f3c2cab0d78d51d7b146a3ca4fa06a1589e3683c46e1c6f4f619ad0cb9187ad

Observation 30dcbeaa-34e2-444b-b769-942726510373 · outbound

This paper cites The nucleus sampling parameter, top-p, is configured at 0.9, ensuring diversity by sampling from the top 90% cumulative probability distribution of the predicted tokens.

Bag of Tricks for Inference-time Computation of LLM Reasoning The nucleus sampling parameter, top-p, is configured at 0.9, ensuring diversity by sampling from the top 90% cumulative probability distribution of the predicted tokens

Reference 32

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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=2026-08-08T13:35:24.597784Z digest=sha256:7e7526043b9d40f11e70eff44b4e08521fef060111620ea68039cc0e1ccd9b23

Observation 565eb3a4-3dfe-4625-8f9c-22a310fb75a3 · outbound

This paper cites JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework.

Bag of Tricks for Inference-time Computation of LLM Reasoning JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework

Reference 2000

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source=pdf_text observed=2026-08-08T13:35:24.532055Z digest=sha256:58311ce14c676f40c90399c8d0e77a845b5ad4be8e95b0bed25012c08527eb5a

Observation a653b208-4f72-422d-bd22-6db94307fa64 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Bag of Tricks for Inference-time Computation of LLM Reasoning The Curious Case of Neural Text Degeneration

Reference 2015

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source=pdf_text observed=2026-08-08T13:35:24.511954Z digest=sha256:b91d33696ed960bd8b1768c05d080bd5905ccdbcf08bf922826b8b286a68c939

Observation 69c79b46-a522-4db9-9ae0-550626519b65 · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

Bag of Tricks for Inference-time Computation of LLM Reasoning Measuring and Narrowing the Compositionality Gap in Language Models

Reference 2018

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source=pdf_text observed=2026-08-08T13:35:24.546662Z digest=sha256:d8ed36b84d7066a0887b2b818703ee65347758c302a76ff32d29a7a3e5f1bca5

Observation 4560d6ca-94fd-4148-953d-33d0981a0c71 · outbound

This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.

Bag of Tricks for Inference-time Computation of LLM Reasoning Large Language Models Cannot Self-Correct Reasoning Yet

Reference 2019

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source=pdf_text observed=2026-08-08T13:35:24.517119Z digest=sha256:0ed756416d9c7e1b8cddbbbf4d878232cfe5ef0d53b1bb87a011039ad61d246a

Observation 7c0b7ab8-5d88-4681-b080-3560da303199 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Bag of Tricks for Inference-time Computation of LLM Reasoning Training Verifiers to Solve Math Word Problems

Reference 2021

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source=pdf_text observed=2026-08-08T13:35:24.490797Z digest=sha256:764d28d8c6cdd74cb07dd0dece88444e7147c65353759df5e3ec55e1b984c3fc

Observation abb5feb2-c728-4a78-bfe8-5356ca268bdb · outbound

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

Bag of Tricks for Inference-time Computation of LLM Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 2022

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source=pdf_text observed=2026-08-08T13:35:24.501724Z digest=sha256:df6d83be2647e5416ca36aad974e148be248c020e457f671e72576297797ac93

Observation 7e2924b7-2b03-4fd0-8971-92b046ed4f4d · outbound

This paper cites Mistral 7B.

Bag of Tricks for Inference-time Computation of LLM Reasoning Mistral 7B

Reference 2023

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source=pdf_text observed=2026-08-08T13:35:24.522438Z digest=sha256:b56aa3b2b77409891750e509020833ed0d881124d6ca46e46d857ff210d00c32

Observation 39528a95-91ea-43c2-9c92-7c574016c962 · outbound

This paper cites Bootstrapping Language Models with DPO Implicit Rewards.

Bag of Tricks for Inference-time Computation of LLM Reasoning Bootstrapping Language Models with DPO Implicit Rewards

Reference 2024

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source=pdf_text observed=2026-08-08T13:35:24.484692Z digest=sha256:e9f06bd2955f6a6dd4930063f318973cef12ec2c4683297222d3aa576cc43b79

Observation 19e090aa-f719-42dd-b06d-e90cac7ae0d3 · outbound

This paper cites ProcessBench: Identifying Process Errors in Mathematical Reasoning.

Bag of Tricks for Inference-time Computation of LLM Reasoning ProcessBench: Identifying Process Errors in Mathematical Reasoning

Reference 2025

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source=pdf_text observed=2026-08-08T13:35:24.587877Z digest=sha256:212f3ff714b90c38d9012fc008892a1688a8ce87c4f20bffc3ab0c064abfd5e1

Pith citing papers

Observation 8ed9c7f9-82cf-45bd-b6f3-3fa3c430ab3a · inbound

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models cites this paper.

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 109

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arxiv_id, observed 2026-05-14T01:29:57.353886Z

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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=2026-05-14T01:29:56.480020Z digest=sha256:5381fc1a30547cc22127d06af04d5f8cc17badd9f7207118cadfc51e16f03684

Observation 1b1cf5e4-e5a4-4d35-a9d9-c41b52ebb3d2 · inbound

LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models cites this paper.

LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 58

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arxiv_id, observed 2026-05-22T13:51:37.641806Z

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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=2026-05-22T13:47:51.436258Z digest=sha256:5864769a5d7884874a78c770d28babe677b0d13a461de6457ef82e9043656528

Observation e893122c-ad37-4a27-a137-ea9315d31a5f · inbound

Scaling Test-time Compute for LLM Agents cites this paper.

Scaling Test-time Compute for LLM Agents Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 19

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source=pdf_text observed=2026-08-07T00:41:48.356267Z digest=sha256:2a268d7cd3218c4c12606dcc9c6dd24c66c22a2412614e0f510f5b7677568fba

Observation 74ec772a-2bb1-4b6f-a274-0bc206788545 · inbound

Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models cites this paper.

Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 45

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arxiv_id, observed 2026-05-19T07:32:08.697743Z

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.

source=pdf_text observed=2026-05-19T07:31:54.509251Z digest=sha256:aea0a7b9a53753fe64345341e1643a0dfd29c850a55f1e9f6126a6a7d516967a

Observation 47e98b99-51a3-44e3-ab3e-df60221fae85 · inbound

Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness cites this paper.

Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 32

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arxiv_id, observed 2026-05-18T17:31:41.589235Z

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.

source=pdf_text observed=2026-05-18T17:31:28.644151Z digest=sha256:190563955e0d06fde8d038c748e16076a2e1b764c6fb3511b42e813d0a936e2a

Observation 29b3a139-f2d7-423b-b09e-88be80449586 · inbound

Empirical Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments cites this paper.

Empirical Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 68

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source=pdf_text observed=2026-08-02T23:52:47.066976Z digest=sha256:ea913f3ad7659e36391333476188ee42a37f7075e45a66da151ba82eabdb0932

Observation 19f72b48-05a2-4e67-bcd0-e4cf3023e31c · inbound

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost cites this paper.

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost Bag of Tricks for Inference-time Computation of LLM Reasoning

Reference 173

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arxiv_id, observed 2026-05-11T20:06:09.943825Z

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source=arxiv_source observed=2026-05-08T10:19:08.451445Z digest=sha256:a2b22cb1fff55ef7224b8cf79c6494b09bead5e0d0caacd50489e4051bac8db6