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Paper Citation Record · LEDGER

Iterative Deepening Sampling as Efficient Test-Time Scaling

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 4 inbound Pith citation observations for arXiv:2502.05449.

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

pith.paper-citation-record.v1
2502.05449 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:23:26.679768Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:58:27.489929Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:40:41.888381Z

Reference resolution

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f306317-31aa-4e63-a0e1-c14f30a893f2 · outbound

This paper cites Phi-4 Technical Report.

Iterative Deepening Sampling as Efficient Test-Time Scaling Phi-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-08T19:23:26.225408Z digest=sha256:3a351ae36441f784d8ff850f886890e05e1400a1ac8c041be7b51d809caf6a2f

Observation 8534e3c7-27d6-40f0-9adc-d2a2bc851856 · outbound

This paper cites CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing.

Iterative Deepening Sampling as Efficient Test-Time Scaling CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Reference 7

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source=pdf_text observed=2026-08-08T19:23:26.300496Z digest=sha256:c43a5440d579873cd9f295823c2b9726ce287748e58723df12782f7a6ba52587

Observation d9fb27c5-3ba9-4b3c-a5b9-5fc998f8172d · outbound

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

Iterative Deepening Sampling as Efficient Test-Time Scaling rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 8

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source=pdf_text observed=2026-08-08T19:23:26.305225Z digest=sha256:dae2b648ed9085249bc122b3147dd639458f2a4ac85f075a0f1d5a2f925a462c

Observation 0d9bbac5-c843-4e4d-8581-e45a4babaf23 · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

Iterative Deepening Sampling as Efficient Test-Time Scaling Reasoning with Language Model is Planning with World Model

Reference 9

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source=pdf_text observed=2026-08-08T19:23:26.407410Z digest=sha256:e0a6bcdf223ea683880f18bbef31b5c156c0cbd643f53739ef57f823866278c0

Observation 2c7e76ae-d76d-4b6d-b37f-56a87001c344 · outbound

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

Iterative Deepening Sampling as Efficient Test-Time Scaling Large Language Models Cannot Self-Correct Reasoning Yet

Reference 10

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source=pdf_text observed=2026-08-08T19:23:26.459491Z digest=sha256:6766736879ce4182307784fb74be373d8ada78c577b1f7f5fac2f628bd560b0d

Observation 061c1b28-cf6b-4c55-893e-19b4692aa84a · outbound

This paper cites O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?.

Iterative Deepening Sampling as Efficient Test-Time Scaling O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

Reference 11

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source=pdf_text observed=2026-08-08T19:23:26.463205Z digest=sha256:2c5c0b4a55a1eb8d2a82f446e19544df5817c10823a2769e3d87fc10f8e18391

Observation f8353a63-32f0-4b53-937c-b80b9281e030 · outbound

This paper cites OpenAI o1 System Card.

Iterative Deepening Sampling as Efficient Test-Time Scaling OpenAI o1 System Card

Reference 12

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source=pdf_text observed=2026-08-08T19:23:26.467995Z digest=sha256:64349f4cb6cb049ecc726eb62ed4596272f56c3b7ee949f5d3f784ffb7f9f77b

Observation 20fb32f0-d7df-4e6e-bba7-eebf365e0b47 · outbound

This paper cites Let's Verify Step by Step.

Iterative Deepening Sampling as Efficient Test-Time Scaling Let's Verify Step by Step

Reference 13

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source=pdf_text observed=2026-08-08T19:23:26.472548Z digest=sha256:4bbdb76e7a6055549f1ed2a7527a0cf0dc7005eb78793e1282ddf2f44679c1d7

Observation 694b5fa9-d7b8-4431-8935-9c5c175328de · outbound

This paper cites Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding.

Iterative Deepening Sampling as Efficient Test-Time Scaling Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding

Reference 14

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source=pdf_text observed=2026-08-08T19:23:26.477154Z digest=sha256:07e74a0b49865717b5498f1368cfc2f22efb0837f580eff254388a3124a34c4d

Observation 2812767a-59a3-40a0-bdd3-4cb9571ef80f · outbound

This paper cites V ., Patel, A., Adlakha, V ., Aghajohari, M., BehnamGhader, P., Bhatia, M., Khandelwal, A., Kraft, A., Krojer, B., L`u, X.

Iterative Deepening Sampling as Efficient Test-Time Scaling V ., Patel, A., Adlakha, V ., Aghajohari, M., BehnamGhader, P., Bhatia, M., Khandelwal, A., Kraft, A., Krojer, B., L`u, X

Reference 15

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source=pdf_text observed=2026-08-08T19:23:26.481446Z digest=sha256:b4a6d090189cb724cd34ef209b342e28e1ef6e6144664c2758e9b1551bfb51e6

Observation 64e2d68f-2521-42a6-8b6e-fdf272c43642 · outbound

This paper cites s1: Simple test-time scaling.

Iterative Deepening Sampling as Efficient Test-Time Scaling s1: Simple test-time scaling

Reference 16

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source=pdf_text observed=2026-08-08T19:23:26.486210Z digest=sha256:b2638141548e91458b420416d51f7c3969dd800b2443cceaf47cbf3cbff944ef

Observation 8f15e729-070c-482a-a992-d11b32dffbf2 · outbound

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

Iterative Deepening Sampling as Efficient Test-Time Scaling Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 17

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source=pdf_text observed=2026-08-08T19:23:26.490001Z digest=sha256:56fe51516d3a98b3b6e2b60f8553d324fd149ffaabf31186d51afc1118fa38d4

Observation e6ad192f-0599-483d-999e-8250c61e21f5 · outbound

This paper cites Fast Best-of-N Decoding via Speculative Rejection.

Iterative Deepening Sampling as Efficient Test-Time Scaling Fast Best-of-N Decoding via Speculative Rejection

Reference 18

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source=pdf_text observed=2026-08-08T19:23:26.494254Z digest=sha256:f7021dde092e0b45a1d046379fb1e1be35a64d2cd44204a99f037e69da9a4ae9

Observation f5d2c2d7-13e9-4188-b4e8-92ef5ead0439 · outbound

This paper cites On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models.

Iterative Deepening Sampling as Efficient Test-Time Scaling On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models

Reference 19

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source=pdf_text observed=2026-08-08T19:23:26.537298Z digest=sha256:fa3cf1bc093259576056214831eba2424e794405d1af465210a5966fff5de7e6

Observation 15c831c6-742f-4d5a-b80f-194e6211e26c · outbound

This paper cites Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals.

Iterative Deepening Sampling as Efficient Test-Time Scaling Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 20

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source=pdf_text observed=2026-08-08T19:23:26.596654Z digest=sha256:eb4d37ec1d4bfdbd82701b5d98310d56b0e686e403d930620b5595c5527fc601

Observation 8340434e-fe6f-4a0a-b3ed-f2631ad74a61 · outbound

This paper cites Chain-of-Thought Reasoning Without Prompting.

Iterative Deepening Sampling as Efficient Test-Time Scaling Chain-of-Thought Reasoning Without Prompting

Reference 21

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source=pdf_text observed=2026-08-08T19:23:26.649729Z digest=sha256:d25fa082c07db9bbda42c0e9e5c7217c9078d98701abee3d40531b7a68dbf4df

Observation f301cddc-bc5a-4ddb-b0f9-5aacbd8f5363 · outbound

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

Iterative Deepening Sampling as Efficient Test-Time Scaling Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 22

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source=pdf_text observed=2026-08-08T19:23:26.654411Z digest=sha256:4a002e52431a2599d5dad15ab3abf2e9db7ef0426535725e5266ba69422788bf

Observation 9b05b5d7-10b5-4be4-b02b-bf5b52ae3a55 · outbound

This paper cites Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective.

Iterative Deepening Sampling as Efficient Test-Time Scaling Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

Reference 24

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source=pdf_text observed=2026-08-08T19:23:26.662779Z digest=sha256:1a046304cdaa408e58a4ef3c92973d90c5aa7049e47fe3e983f1779507f0a47b

Observation 59fb88ec-0b79-46a6-bdbf-8e746a0419f3 · outbound

This paper cites Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B.

Iterative Deepening Sampling as Efficient Test-Time Scaling Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

Reference 25

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source=pdf_text observed=2026-08-08T19:23:26.666844Z digest=sha256:0fbb42325a069e5702225e1afe1e78886bf9426086d585be8b00419c7f1a1120

Observation 6ed22220-ff2d-49c2-936c-5b22c09d76bb · outbound

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

Iterative Deepening Sampling as Efficient Test-Time Scaling The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 26

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source=pdf_text observed=2026-08-08T19:23:26.670683Z digest=sha256:3e2f0d0d9ce80029609cd3d228ca62bcadb15aa05dc3f0ea7b8a5bca6b073d90

Observation df0aedc1-2d1d-49f2-b7b6-eea041a7b389 · outbound

This paper cites Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.

Iterative Deepening Sampling as Efficient Test-Time Scaling Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Reference 27

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source=pdf_text observed=2026-08-08T19:23:26.675851Z digest=sha256:ce18b9f43b8008a1731386de6dc4f1a56a3507b6f4201591eab0106b10b2adb2

Observation bb835118-62d6-47e1-9634-e318baa76cb0 · outbound

This paper cites As a re- sult, most standard non-mathematical datasets are not well aligned with the objectives of this study.

Iterative Deepening Sampling as Efficient Test-Time Scaling As a re- sult, most standard non-mathematical datasets are not well aligned with the objectives of this study

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-08T19:23:26.679768Z digest=sha256:7140c96d06d9543b5001fa7e70f7c7a66f7f26f7fb3580e09103f5ae3fa19523

Observation cea037dc-7b08-46f4-8717-cedc7b7d32eb · outbound

This paper cites AlphaMath Almost Zero: Process Supervision without Process.

Iterative Deepening Sampling as Efficient Test-Time Scaling AlphaMath Almost Zero: Process Supervision without Process

Reference 463

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source=pdf_text observed=2026-08-08T19:23:26.279454Z digest=sha256:e36667b49a3d63cf19b23c9e677a8e2ec9ffe8c9b5f7abe74ebceec8ae56488c

Observation fd05ab1f-b1c7-452e-a023-64c57dacae3b · outbound

This paper cites RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents.

Iterative Deepening Sampling as Efficient Test-Time Scaling RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 2021

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source=pdf_text observed=2026-08-08T19:23:26.286580Z digest=sha256:09c5d9ebd5087fcce869db27e88ec2b3cc778af548328c1be0e4e00680509e0f

Observation a7172746-87d7-40c3-b7fd-2a5d87048863 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Iterative Deepening Sampling as Efficient Test-Time Scaling MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 2022

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source=pdf_text observed=2026-08-08T19:23:26.658678Z digest=sha256:9dc7537f805f83983f035b7821806125f043bf5253221c2f3cdaff974f6ba61a

Observation 00fdb688-d4b2-4cbf-8f2c-63fa32f4f3dd · outbound

This paper cites When is Tree Search Useful for LLM Planning? It Depends on the Discriminator.

Iterative Deepening Sampling as Efficient Test-Time Scaling When is Tree Search Useful for LLM Planning? It Depends on the Discriminator

Reference 2023

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source=pdf_text observed=2026-08-08T19:23:26.291156Z digest=sha256:494ff0b5a2dfef97ec69cc2c8f7728ac089246a53376f21a8df7e8671f233e9b

Observation fbdb90fd-1f53-45b1-be04-5b62826ea277 · outbound

This paper cites Step-level value preference optimization for mathematical reasoning.

Iterative Deepening Sampling as Efficient Test-Time Scaling Step-level value preference optimization for mathematical reasoning

Reference 2024

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source=pdf_text observed=2026-08-08T19:23:26.273352Z digest=sha256:bfc45a1d4e3cbb6b1398d2480c00396ae4bc8e5e57a7060a19733f7841b07893

Observation 910d3997-4738-4fba-b24b-34351b4fddb5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Iterative Deepening Sampling as Efficient Test-Time Scaling DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

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source=pdf_text observed=2026-08-08T19:23:26.295697Z digest=sha256:63ff8d40d3f295cd28ecfa10d674eefb6191f44f2ca6eb4f708a1bd3def97801

Pith citing papers

Observation 835ce9f5-f6ff-4bba-8d26-548b1f1557a5 · inbound

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models cites this paper.

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models Iterative Deepening Sampling as Efficient Test-Time Scaling

Reference 102

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-12T08:40:40.910461Z digest=sha256:ae8a67a5b60eb3ef19f8f838190883303fdbba331ded909759c283f3c458ce34

Observation 2befea03-f4ce-48e2-96c8-8f7467fc3b89 · inbound

SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning cites this paper.

SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning Iterative Deepening Sampling as Efficient Test-Time Scaling

Reference 4

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source=arxiv_source observed=2026-08-15T20:58:27.489929Z digest=sha256:b751511bb3a8d31764224f445beb80dda53aeeb2cb02e8069093435baf09a4e7

Observation b8ece92b-c42e-482c-b094-1ba049dab7c5 · inbound

Corrector Sampling in Language Models cites this paper.

Corrector Sampling in Language Models Iterative Deepening Sampling as Efficient Test-Time Scaling

Reference 5

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source=pdf_text observed=2026-08-07T06:05:45.782782Z digest=sha256:c129d1b4b85b5c11e784939f54c02f1a0ae40abf5c7bfbbad94faa3ac3683d75

Observation 45f3e798-c635-4d6e-b844-b006fe003d35 · inbound

Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs cites this paper.

Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs Iterative Deepening Sampling as Efficient Test-Time Scaling

Reference 40

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source=pdf_text observed=2026-08-06T20:43:11.199591Z digest=sha256:c16645c6b21d5d5751f291cf15076190b9ca1f79ae634941122555276b98504a