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Paper Citation Record · LEDGER

Test-Time Scaling with Reflective Generative Model

As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.01951.

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

pith.paper-citation-record.v1
2507.01951 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:46:44.808617Z

measured 22 of 22 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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  • verified fuzzy3
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 649a06c3-d709-49f4-8d49-07364382960c · outbound

This paper cites Accessed: 2024-12-20.

Test-Time Scaling with Reflective Generative Model Accessed: 2024-12-20

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-06T20:46:45.017812Z

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=2026-08-06T20:46:44.756380Z digest=sha256:c6de76489c13c057075649d70af5cabafda0dc31a88035eabe98adc108f37aa5

Observation 8226f95e-f046-4cbf-919f-72c19f342603 · outbound

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

Test-Time Scaling with Reflective Generative Model rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:44.759235Z digest=sha256:aa0c9e40e967e2f4f11e27816151c5cf8c59f8155fc87a032ee2f04911d45678

Observation ffd2d00c-1b1d-470f-a630-a5176093bc0e · outbound

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

Test-Time Scaling with Reflective Generative Model DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

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source=pdf_text observed=2026-08-06T20:46:44.762329Z digest=sha256:75f7b3f216d25a28cc58e3c3511030839c8312b8c66869b5a078e32a31479a06

Observation 8385cb18-d209-40df-84d9-676f3c50eba2 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Test-Time Scaling with Reflective Generative Model LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 7

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source=pdf_text observed=2026-08-06T20:46:44.767588Z digest=sha256:79a96fb9d0d2b1383ce881f135d04bfaa88ec24db6b3b7a639131a4d803316d7

Observation 6cb25bc9-63f5-4393-a4bc-dee21072b0d2 · outbound

This paper cites The Impact of Reasoning Step Length on Large Language Models.

Test-Time Scaling with Reflective Generative Model The Impact of Reasoning Step Length on Large Language Models

Reference 8

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source=pdf_text observed=2026-08-06T20:46:44.770899Z digest=sha256:84e4cf9890f955537c7b0eec1ec3f18f1c20721256e61e8a3e9b9beca57f06d5

Observation 351255ef-6d68-4d7a-9219-6e99ab6ba5a9 · outbound

This paper cites Accessed: 2025-02-18.

Test-Time Scaling with Reflective Generative Model Accessed: 2025-02-18

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T20:46:45.009803Z

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=2026-08-06T20:46:44.773451Z digest=sha256:b8c0058c8d07e6b7b9b0fe826cfb9b24e2d35496b8b9233eabd38f9c342bf5aa

Observation 960841b0-a8eb-43b4-ae90-d6e7ef6b0273 · outbound

This paper cites LIMR: Less is More for RL Scaling.

Test-Time Scaling with Reflective Generative Model LIMR: Less is More for RL Scaling

Reference 10

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source=pdf_text observed=2026-08-06T20:46:44.775814Z digest=sha256:f483e8e00be4b56a3ca637f1996194e92c1148ee5b5a1e39d0fb545bfe760c6c

Observation 47967841-d013-48fe-a5c4-a9853eeb27be · outbound

This paper cites Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling.

Test-Time Scaling with Reflective Generative Model Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling

Reference 11

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source=pdf_text observed=2026-08-06T20:46:44.778485Z digest=sha256:24de1d1076da71eb5d0934f566534f4d7b6681c84ca8fd28a129b459b717fe42

Observation 7ebdc463-bd03-4a77-8dc5-8396593fd947 · outbound

This paper cites Improve Mathematical Reasoning in Language Models by Automated Process Supervision.

Test-Time Scaling with Reflective Generative Model Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 12

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no resolver link, observed 2026-08-06T20:46:44.780934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:44.780934Z digest=sha256:861fd9c948d1dc593c2291564d9f439069aaabc74ba03c8865d18887f71df06b

Observation 1845ae5b-eaf4-43c4-a421-8b0b14f25fd2 · outbound

This paper cites Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning.

Test-Time Scaling with Reflective Generative Model Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:44.783885Z digest=sha256:ab64f754b81c4b4a749c0162b9908970b67153fa722db90b1ae8ce7631a5d547

Observation d3880fce-8e76-4f62-a8e7-1d6e0efecb7b · outbound

This paper cites s1: Simple test-time scaling.

Test-Time Scaling with Reflective Generative Model s1: Simple test-time scaling

Reference 14

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no resolver link, observed 2026-08-06T20:46:44.786503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:44.786503Z digest=sha256:7afc3266312dc0081a53efe64101caa9f685118ee11259e8a5c9f3b9a6c92b64

Observation 2e4d49a9-e75a-4825-9437-4a33a5296366 · outbound

This paper cites Accessed: 2025-04-13.

Test-Time Scaling with Reflective Generative Model Accessed: 2025-04-13

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:46:45.001249Z

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=2026-08-06T20:46:44.789082Z digest=sha256:f84f6af352853b7e74fa0cf135280631d8fba71fa0e393ebfcf5ebb095e61f2f

Observation a4aead36-ea7f-4f24-8df1-5142667e29da · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Test-Time Scaling with Reflective Generative Model DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 16

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source=pdf_text observed=2026-08-06T20:46:44.791690Z digest=sha256:e9e09bfd9bc88705bfe42aab71dcf1a4d4b66c761d8f1cea1526ed5130fc63a7

Observation 92296b9a-402d-4b5e-8175-2c9887416d9d · outbound

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

Test-Time Scaling with Reflective Generative Model Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 17

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source=pdf_text observed=2026-08-06T20:46:44.794776Z digest=sha256:8363055e9a1f178e100394fd7569c8106293ea8de58081ffe733c906cb3263b1

Observation d3461d8a-0174-4f13-a6fc-b94a0a92ee26 · outbound

This paper cites AURORA:Automated Training Framework of Universal Process Reward Models via Ensemble Prompting and Reverse Verification.

Test-Time Scaling with Reflective Generative Model AURORA:Automated Training Framework of Universal Process Reward Models via Ensemble Prompting and Reverse Verification

Reference 18

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no resolver link, observed 2026-08-06T20:46:44.797312Z

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source=pdf_text observed=2026-08-06T20:46:44.797312Z digest=sha256:1051703970be2bd472a8eb09bb68645c70ec7cb4ea0df14c386f27a0fdd5bb13

Observation f5d6b8b6-6b14-4d25-831d-644c5316d7c7 · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

Test-Time Scaling with Reflective Generative Model Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 19

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no resolver link, observed 2026-08-06T20:46:44.799931Z

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source=pdf_text observed=2026-08-06T20:46:44.799931Z digest=sha256:10e537317f68680cf27e860dd163b9fbfd1d11b19882eb4cad00ab8fa669d39b

Observation 85042b7b-9067-4a7b-8151-9a268dc0ed74 · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

Test-Time Scaling with Reflective Generative Model Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 20

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source=pdf_text observed=2026-08-06T20:46:44.802656Z digest=sha256:d39ed0ab90c96cf041bd425ab1228d24723548342ad77372230dffe04e68388e

Observation 54db9098-0616-4541-9ef3-e296ed949322 · outbound

This paper cites Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?.

Test-Time Scaling with Reflective Generative Model Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?

Reference 21

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source=pdf_text observed=2026-08-06T20:46:44.805597Z digest=sha256:6d3e46dec975203b9a982c3ee2cfe871079ec8b41227138ce5a04883592efef6

Observation b537c84a-e98d-491f-97a5-ad21595fab3e · outbound

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

Test-Time Scaling with Reflective Generative Model The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 22

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no resolver link, observed 2026-08-06T20:46:44.808617Z

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source=pdf_text observed=2026-08-06T20:46:44.808617Z digest=sha256:481776e7ac3d5673ace8ac40e87a5f0f60574f59172283294fc07b400a4d5621

Observation efa51012-8f6d-4d4e-9668-632be0b1bbdb · outbound

This paper cites OpenAI o1 System Card.

Test-Time Scaling with Reflective Generative Model OpenAI o1 System Card

Reference 2023

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source=pdf_text observed=2026-08-06T20:46:44.765061Z digest=sha256:bc7ff548391f1a881d39da4e9cd6b6c5de06f26ca8f6261bb8056682ad651efa

Observation e9214b3c-3bd2-4c2a-b98c-8965c9052472 · outbound

This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

Test-Time Scaling with Reflective Generative Model Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 2024

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no resolver link, observed 2026-08-06T20:46:44.752442Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:44.752442Z digest=sha256:c48e29a13055f709cdeb1731631541a0033d46f6e5ab1fff4d4100d43a2b8696

Observation b532d1d6-c398-4e00-81bd-ba0d9d30f022 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Test-Time Scaling with Reflective Generative Model Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2025

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:44.748925Z digest=sha256:366ecd29b6ff6dffb5fcc100d710a7d697e760a05d2e7968971f0b84532b1392

Pith citing papers

No inbound Pith citation observations are available.