Pith. sign in

Paper Citation Record · LEDGER

Iterative Deepening Sampling as Efficient Test-Time Scaling

As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 3 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 31 of 31 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:05:45.782782Z

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.225408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.225408Z digest=sha256:7ee76cfd95cefb6e13cc32f7162b24809cab1e65ff551975ad7cdda12efc3a9b

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.300496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.300496Z digest=sha256:a286e1c7844e1d322ced2e5dbf8f1ff6f7ac4172f0a76c190b1c2c6758928c23

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.305225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.305225Z digest=sha256:051e59bd5474bc87a8a3516bcfa471b667b9ed07e5b323255ae97ee74a3a7485

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.407410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.407410Z digest=sha256:2de214e75f8ed4f7d853ca7a98d4ed9c36a06d8908e551cb6318d46de3439d83

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.459491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.459491Z digest=sha256:67ac123139a696a41965cd27b2a365897dcbefda9425b85177e01231011b9d62

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.463205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.463205Z digest=sha256:ed0a258967525f02869e28036db7a8da2fcc0f5d6b96938022a0a2aa15083ec7

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.467995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.467995Z digest=sha256:79916057d5410a8b148956d1cc49950129cdcfec68746e504e7e42af719ff7e3

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.472548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.472548Z digest=sha256:d5de759cc418985839096e961559c7679e0768a559ad40ca7e7a94e1ede9138b

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.477154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.477154Z digest=sha256:aa9d0b8aafb8e31a65161f98c83aed335d4f59ab8e93ed64995bb6714bff7321

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.481446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.481446Z digest=sha256:a415c4e5e30522020077be6509585cace100db5af4fe8dba51d9722b8219ee02

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.486210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.486210Z digest=sha256:b2d3ddb0648e7bcdedd9fdc7c04195097f2fadd440e3d4aa605a6b4f8200e929

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.490001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.490001Z digest=sha256:716a24d065042257a78cf2e9e5fa3ff75c5d76e41d9dd01012a21b443b48ae6e

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.494254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.494254Z digest=sha256:673cf99588b3546888363887066fe6d5777ac53dd716c77f78a01e7ad493d06f

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.537298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.537298Z digest=sha256:2ab429153d01067843747439c024f40871e504628f8e6fe5cfc5197fc728093f

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.596654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.596654Z digest=sha256:0559ef6acf57c66a5b52daa13efac3827845a888068e10cca60cf90ee178cfcd

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.649729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.649729Z digest=sha256:81c959c4109c9e8ad500126083b5c1e60c7cbad3cbc8d256c27352634da21ff8

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.654411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.654411Z digest=sha256:cb0f585554889ae36b237027657c23ef2dc5cbbcd55cd797da6d452ee8fb9c6b

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.662779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.662779Z digest=sha256:ab6e4ea68168e80d1d7b724cd4559b6da42741e82a8195811fda486e95164c4f

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.666844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.666844Z digest=sha256:5ef5b8e47730b533a611d3f91e6235e2a835cc1402e1db4b746bc99ee33471c3

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.670683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.670683Z digest=sha256:7c5be748a39a516bb74224c91cce426b065030c4eeb6e32dba38407637c73c70

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.675851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.675851Z digest=sha256:694e47cae2d63abaa9b284e8433faafbf36e3d41bff76f84836bc5c2dfb06faf

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:23:27.248904Z

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=2026-08-08T19:23:26.679768Z digest=sha256:a6e2be8a7182f5d91128f84ea96cd649acdcbc3195036dcddc8f49d896470778

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.279454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.279454Z digest=sha256:4c1f713a22b583a489d003ad48b7af653f750e2320fb46a6c32acbb5567a2feb

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.286580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.286580Z digest=sha256:f090d0a43d30e4477ec47523accae86ca9d985637f0d972ec8a561068b17c103

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.658678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.658678Z digest=sha256:89c8f3ebd6e5f1b7ec2068483848076628347c889c95bdef03f5cb7e5a46c189

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.291156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.291156Z digest=sha256:4c613682af477829b0465dc9b313b7ea96f0e6322c5c84549b36dd5cc1ba2559

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.273352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.273352Z digest=sha256:be708fc349096060557a3c53aa1e4fc63ddbaa3d3ce6637f19ab7ff6c8139786

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

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.295697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.295697Z digest=sha256:93a780d053ece2d50dd7d93ada9b665db9d4626e0a6b7fe1ef9caf15cff941e6

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

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:40:41.891609Z

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=2026-05-12T08:40:40.910461Z digest=sha256:591f5a5a0c59c678954b262949eadfe11703b538590fb5ee3f251f89604f4a13

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

Resolution
unresolved
no resolver link, observed 2026-08-07T06:05:45.782782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:05:45.782782Z digest=sha256:b8afd7ad3480278666205115df9e5637244769094b0afbb44e78c3039382f8e6

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

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:11.199591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:11.199591Z digest=sha256:a23311e707162729f4dc8e04471ea7c5e86c691061c0cefae290b5cdc2a10fdd