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Paper Citation Record · LEDGER

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge

As of 18 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 1 inbound Pith citation observation for arXiv:2505.11875.

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

pith.paper-citation-record.v1
2505.11875 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:50:10.809590Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:51:56.556213Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:54:01.037769Z

Reference resolution

72 of 72 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved64
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ac5df32-1d18-42bc-a1be-30d764a83e2a · outbound

This paper cites Critique-out-Loud Reward Models.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Critique-out-Loud Reward Models

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:50:10.436494Z digest=sha256:093732b3141b5be5a5c6bf58d60639e268b9388720755557823b5db36663355a

Observation b0790348-3993-483a-ae30-0118665643a4 · outbound

This paper cites Chain-of-Thought Reasoning In The Wild Is Not Always Faithful.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

Reference 2

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source=arxiv_source observed=2026-08-15T20:50:10.444035Z digest=sha256:cd95ea393fcdbf5c10c499133e9f25c9dc8ab234938a3e4aedf112ae152201c6

Observation 651d2625-62df-4db1-a08b-cae1ffdd979b · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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source=arxiv_source observed=2026-08-15T20:50:10.449658Z digest=sha256:d4260cfa5331d82061652edf5f7c41325a850da14884d4f4c910c5ed5a0820b4

Observation 88d6640e-8e54-4ec7-9bc1-7af1024a6ddc · outbound

This paper cites Rank analysis of incomplete block designs: I.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Rank analysis of incomplete block designs: I

Reference 4

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source=arxiv_source observed=2026-08-15T20:50:10.455694Z digest=sha256:355dc01b62cd38b23cd0d8a5b60ead2e4ef60252053fb549e1c70380d4abc514

Observation b8e11bc1-ab12-473a-9276-a6e1e919ceab · outbound

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

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

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source=arxiv_source observed=2026-08-15T20:50:10.461007Z digest=sha256:8c3ef2d8ee0a05442deaef4f5249b6c07e200ad7409953363244048035a4adcc

Observation 14106471-aeec-4471-b4e2-9063e597965b · outbound

This paper cites Internlm2 technical report, 2024.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Internlm2 technical report, 2024

Reference 6

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:50:10.466439Z digest=sha256:9394de25e86a4a80112f4c1dbc94f64e3538e6528bcfe773ff719812d59dd20b

Observation c1ac62e6-1f14-4b5f-8fa8-28400518939f · outbound

This paper cites RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

Reference 7

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source=arxiv_source observed=2026-08-15T20:50:10.472032Z digest=sha256:b36d3b45e44b3bacc970df9d25a2c885d57ec9b99174226b1b4c452851943d0c

Observation 7f265e26-4bd5-407f-ac99-635c2e12cce4 · outbound

This paper cites CodeMonkeys: Scaling Test-Time Compute for Software Engineering.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge CodeMonkeys: Scaling Test-Time Compute for Software Engineering

Reference 8

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source=arxiv_source observed=2026-08-15T20:50:10.477308Z digest=sha256:e660a0b056bbdb59e03f7ad224cc5f5bb736b261cecbbab2dab6c2945c9f43e6

Observation 59b2a579-93bb-4804-8422-89810d1e3aed · outbound

This paper cites Scaling laws for reward model overoptimization.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Scaling laws for reward model overoptimization

Reference 9

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source=arxiv_source observed=2026-08-15T20:50:10.482771Z digest=sha256:873d15738814eac9ead3af1041cc79ab8708892e7e410da35013f2b3b2b31f62

Observation 7d0b3ea7-ab84-403d-a478-a23e1727bb7c · outbound

This paper cites Gemini 2.0 flash thinking mode.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Gemini 2.0 flash thinking mode

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.487974Z digest=sha256:0766c0e87d8b430076151b74392a903f30e2539d06e543dccfcd74f628c5fcbd

Observation 8a8d7b61-c5e5-4153-b04f-10555938505e · outbound

This paper cites The Llama 3 Herd of Models.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge The Llama 3 Herd of Models

Reference 11

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source=arxiv_source observed=2026-08-15T20:50:10.493461Z digest=sha256:4ea944e3116d235d6094aab75140f53a4ec11ff1ff150502994961a03b196232

Observation d9a0143e-6e3a-4eca-87a3-14a6d2fac6e0 · outbound

This paper cites A Survey on LLM-as-a-Judge.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge A Survey on LLM-as-a-Judge

Reference 12

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source=arxiv_source observed=2026-08-15T20:50:10.499201Z digest=sha256:c5a67b64981e1f824ee715d0d7df14b0dd44f381d76e0d0661cc5dd147d9bff3

Observation b173fb99-234d-4b52-ac4b-186ad1b9730b · outbound

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

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

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source=arxiv_source observed=2026-08-15T20:50:10.504807Z digest=sha256:c4c35383b9fbd268b57b1879b2428d00c47da7d0688ec0ade47a1e807aecb9d3

Observation f1e6d08d-c4a8-4340-bfa6-6e477506c399 · outbound

This paper cites WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs

Reference 14

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source=arxiv_source observed=2026-08-15T20:50:10.509542Z digest=sha256:f649800f528de3a4872a42c976cd04453aa009f00575b5a7eea715c5080a7fe8

Observation 3517ec8c-e637-46c0-8a66-19dd9032f937 · outbound

This paper cites Metrics for Explainable AI: Challenges and Prospects.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Metrics for Explainable AI: Challenges and Prospects

Reference 15

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source=arxiv_source observed=2026-08-15T20:50:10.514533Z digest=sha256:656dd7ffd0b8164ccba0328b2daabbc176e513d25e4dc3cf6b071708c9f14d4a

Observation cd9810c7-f516-48af-ba87-11efe248e784 · outbound

This paper cites Human Feedback is not Gold Standard.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Human Feedback is not Gold Standard

Reference 16

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source=arxiv_source observed=2026-08-15T20:50:10.519624Z digest=sha256:4aa777c9985c8de53cc7b5fd58b36998e74512b6e92fad7ddf18135961f5bf45

Observation 6f680da6-fc13-4fd1-afb2-0df8f6d4a6f3 · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 17

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source=arxiv_source observed=2026-08-15T20:50:10.524402Z digest=sha256:82b177b2bc96bf38bbd2f1b38f77af16b3075b2b388c7efe430af747e926a375

Observation fc4e5462-60a0-42b2-a694-27af085da23e · outbound

This paper cites open-r1/openr1-math-220k.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge open-r1/openr1-math-220k

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.529827Z digest=sha256:03afd5a914df2824f52a228c0b6de8b4a8efe020a460c294c34b84cae24c3ec4

Observation 06262224-13c3-4f23-8a03-21b71a1463e5 · outbound

This paper cites PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference

Reference 19

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source=arxiv_source observed=2026-08-15T20:50:10.534659Z digest=sha256:13db8a95fab6603fa9bae9d43472883dc3ac13d1bb09778842f91f832ef976e9

Observation c51ce50a-8322-42fd-a578-22b3ed8fc3f7 · outbound

This paper cites On scalable oversight with weak llms judging strong llms.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge On scalable oversight with weak llms judging strong llms

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.539974Z digest=sha256:794e1d854dc0fd75795e0e2c276ee312074c9ab69028d247d7c4506375d96649

Observation ab2762c8-24bd-4359-9e61-9eb3ecee8bea · outbound

This paper cites Evaluating Robustness of Reward Models for Mathematical Reasoning.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Evaluating Robustness of Reward Models for Mathematical Reasoning

Reference 21

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source=arxiv_source observed=2026-08-15T20:50:10.544791Z digest=sha256:304c92e75f1d8577d830b804e3688f88ae6283337e53ff10d13ffe44d5fbe86c

Observation fcc46d3b-de50-4ce1-9122-010879d858a3 · outbound

This paper cites The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

Reference 22

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source=arxiv_source observed=2026-08-15T20:50:10.549841Z digest=sha256:58e8969c20043aed4a48e22069d8864371821f8daf77b9bbd0dc87fda1e50454

Observation 5077b2c0-f17a-4ef2-aec8-ca995dad0fb5 · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge RewardBench: Evaluating Reward Models for Language Modeling

Reference 23

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source=arxiv_source observed=2026-08-15T20:50:10.554990Z digest=sha256:f8ae881d74d5b716e45f25f14cd3e585bc3f57b3c38b27da2cce1c0acaac5d89

Observation d7f8acd1-148d-4dd3-86b6-a248966a084b · outbound

This paper cites From generation to judgment: Opportunities and challenges of llm-as-a-judge.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge From generation to judgment: Opportunities and challenges of llm-as-a-judge

Reference 24

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source=arxiv_source observed=2026-08-15T20:50:10.559959Z digest=sha256:b23ad0c9787e636a140330925c0463a4358e064b00eee3d332c46712500af77d

Observation b134077e-e4b5-48b2-90a9-8c8a634e2ff6 · outbound

This paper cites Generative Judge for Evaluating Alignment.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Generative Judge for Evaluating Alignment

Reference 25

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source=arxiv_source observed=2026-08-15T20:50:10.564644Z digest=sha256:a4cf051d9cd75a588b89db3b872b54740dae7510ddd8a11ae25f72f0eb398676

Observation 8d988751-2e87-461e-844d-882b2cf70639 · outbound

This paper cites Let's verify step by step.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Let's verify step by step

Reference 26

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source=arxiv_source observed=2026-08-15T20:50:10.570180Z digest=sha256:d76fb34ca89f2e3f3d2edc18c508548be009a83780e9dcb1243919dab97cead9

Observation 717df3ac-9d5c-4bfb-a2cd-60bbc516554e · outbound

This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 27

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source=arxiv_source observed=2026-08-15T20:50:10.575982Z digest=sha256:278593cf98ebd8ec11fc055a882cd9f2c1c7764bc7511731738ca2843d8c7e77

Observation fac3f3d2-a97d-49a0-947e-cbbe3f21fa50 · outbound

This paper cites Video-T1: Test-Time Scaling for Video Generation.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Video-T1: Test-Time Scaling for Video Generation

Reference 28

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source=arxiv_source observed=2026-08-15T20:50:10.580904Z digest=sha256:888e64ccd5e9d636925d71cbc6db7f79109a2d0f9a1c8f621e2b35458e45be8d

Observation 74c64b38-a827-492c-a85e-aa5eb19e2e68 · outbound

This paper cites Learning Code Preference via Synthetic Evolution.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Learning Code Preference via Synthetic Evolution

Reference 29

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source=arxiv_source observed=2026-08-15T20:50:10.586287Z digest=sha256:35a5656eece400e4ba33d52f87b67f66ade355487ad99a52a118528bec0085b0

Observation 18096810-0622-4985-94e7-6b9917ed69c8 · outbound

This paper cites Inference-time scaling for generalist reward modeling.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Inference-time scaling for generalist reward modeling

Reference 30

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source=arxiv_source observed=2026-08-15T20:50:10.591957Z digest=sha256:35e061fa29d6d4f52af8ed0db8d2d260423ea4269db9307ed19c2c489cdbc96b

Observation a53a6a3f-e3e1-4310-80bd-5f6d3cccfb02 · outbound

This paper cites Principal components analysis (pca).

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Principal components analysis (pca)

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.596839Z digest=sha256:5a0e461b85d7b2f9d2a797d17f202293d3a59ccbbb18ff355910e62126e58779

Observation 9c42e0fb-1f97-4bed-86b9-82d50605798e · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Self-refine: Iterative refinement with self-feedback

Reference 32

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source=arxiv_source observed=2026-08-15T20:50:10.601696Z digest=sha256:ba6bd9db57606f70e644dc42e2184e74b15d5a0828cab0840079c3571d16dddd

Observation ee70976a-dda7-4ce8-83d6-b3643658689c · outbound

This paper cites LLM Critics Help Catch LLM Bugs.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge LLM Critics Help Catch LLM Bugs

Reference 33

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source=arxiv_source observed=2026-08-15T20:50:10.606695Z digest=sha256:21caecd5a728c47d75b4250089d5a15ef52d33566d3408a566c6b151c83579ce

Observation f4c9b583-8b7b-48a9-b3b6-37edf0b766e9 · outbound

This paper cites s1: Simple test-time scaling.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge s1: Simple test-time scaling

Reference 34

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source=arxiv_source observed=2026-08-15T20:50:10.612040Z digest=sha256:e136a022712ae94e1873d0aa882ce2374b5eed6ada8aeda4267e8f7d339e8827

Observation 2534edf0-a286-426d-9b53-1d05f9dfa127 · outbound

This paper cites Learning to reason with llms.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Learning to reason with llms

Reference 35

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raw_fallback, observed 2026-08-15T20:50:12.101504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.617303Z digest=sha256:0db5f81282d68dcff3846121f4341280772fa5a7dc86ce98cea32e5db41a3b35

Observation 74bf2cca-d301-45a0-91cb-cf6d7ee3cc84 · outbound

This paper cites Training language models to follow instructions with human feedback.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Training language models to follow instructions with human feedback

Reference 36

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source=arxiv_source observed=2026-08-15T20:50:10.622075Z digest=sha256:15f7ff0522f8de5cd805a445eae2276bb1bbe2fab0b8a2d36092e5056dd6fae0

Observation 9032f033-466d-4408-bd5f-3777967ffaeb · outbound

This paper cites OffsetBias: Leveraging Debiased Data for Tuning Evaluators.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge OffsetBias: Leveraging Debiased Data for Tuning Evaluators

Reference 37

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source=arxiv_source observed=2026-08-15T20:50:10.626884Z digest=sha256:781facda2f2dfa1f792815cf6fa1705a505f396b05d38efb4a62b1367004c4d8

Observation 06f743cf-9983-4d01-b785-b09d267b93fe · outbound

This paper cites Codeforces cots.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Codeforces cots

Reference 38

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source=arxiv_source observed=2026-08-15T20:50:10.632128Z digest=sha256:d3f03f346c20686e4ef14962c707e8766fd78fb2560fce83cc3084c4b596bb60

Observation 2dc70ec6-264d-45fc-83fe-70a0567d2b40 · outbound

This paper cites Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge

Reference 39

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source=arxiv_source observed=2026-08-15T20:50:10.636826Z digest=sha256:804582a1f0fb729bb22c4ad6d182880301c1fdc0631cdcaff5e06a7603e7a83b

Observation a3910a0e-0383-4bdb-9fc9-e02f56917c99 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 40

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source=arxiv_source observed=2026-08-15T20:50:10.642154Z digest=sha256:4553ab5a46b4fea8b99de98783793bb827a9e0d2169f7134346e3d9ba9fa8470

Observation ab972d01-0af6-4a51-9f44-4d5429532e87 · outbound

This paper cites Proximal Policy Optimization Algorithms.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Proximal Policy Optimization Algorithms

Reference 41

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source=arxiv_source observed=2026-08-15T20:50:10.647716Z digest=sha256:ebc7c2e00d1db145a729ce763a41c9d2e52f66a9f7b11dc5be25572462f91734

Observation 10defcfb-39b0-4479-b126-91e9e4654881 · outbound

This paper cites Rethinking Reflection in Pre-Training.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Rethinking Reflection in Pre-Training

Reference 42

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source=arxiv_source observed=2026-08-15T20:50:10.656123Z digest=sha256:e3a99e51bb17f7b609968753e47866c8e5f4bcefd361aa5f2dbbb2cfeaf8ca06

Observation 5265dbef-10ca-4f34-bacf-f96f10d32b45 · outbound

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

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 43

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source=arxiv_source observed=2026-08-15T20:50:10.661362Z digest=sha256:1722a61d222738fc9ef9dd058ec1877e6693456996f0851abe7c3d331cd58c1b

Observation d7437445-4e5c-4204-b640-ffcd69daff0d · outbound

This paper cites Skywork critic model series.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Skywork critic model series

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-15T20:50:12.063946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.666183Z digest=sha256:24de69091075973cead89c249378034e892575c29ca22ad03cb7d7a25c7dcdad

Observation 0ccec354-002b-4a03-bac9-76eda568aa24 · outbound

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

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 45

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source=arxiv_source observed=2026-08-15T20:50:10.671068Z digest=sha256:76d5087fe0ff689f7f33fb99615697a97b624a3514dac96aa62797ac178a5393

Observation 2eb1173f-22a9-49de-920e-8a77f649ecf5 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 46

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source=arxiv_source observed=2026-08-15T20:50:10.676036Z digest=sha256:40ff928b1da28228b8832e4715d90757e2eaadcf363ba9916af24155dfa1321f

Observation 7332358e-dafe-41d3-8598-01038e3d75a3 · outbound

This paper cites OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

Reference 47

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source=arxiv_source observed=2026-08-15T20:50:10.680809Z digest=sha256:18b6bae2c5efc9193dac5b31905bc7fd41a687de96c3fab1c5b5e2c63853f5d9

Observation 1d273eb4-3e0e-4360-aefa-53deaf8f8177 · outbound

This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers

Reference 48

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source=arxiv_source observed=2026-08-15T20:50:10.685925Z digest=sha256:48be697e1e7cb6d6fedac8680ffe1818327fd84c408eb4e63bd78d71760b4939

Observation d1a924e3-b953-46bb-b261-45d2f70bc1f7 · outbound

This paper cites PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Reference 49

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source=arxiv_source observed=2026-08-15T20:50:10.690874Z digest=sha256:9521715551b95c64ac77517263e6524fc016077d26729c3d3e87386356c7583e

Observation 8f717129-d5ef-4671-9290-c40b8efb86ff · outbound

This paper cites HelpSteer2: Open-source dataset for training top-performing reward models.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge HelpSteer2: Open-source dataset for training top-performing reward models

Reference 50

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source=arxiv_source observed=2026-08-15T20:50:10.696490Z digest=sha256:af9dad8948a601881b0eed78c2cb3c1225bc4327b2e0ee83bb5c4cba7a95ec4e

Observation d2b6d259-7d69-40d3-8045-0e66ca820046 · outbound

This paper cites CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences

Reference 51

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source=arxiv_source observed=2026-08-15T20:50:10.701477Z digest=sha256:6d8260ebfd57a9281abe2502a2b3664e611ca36a3f66b6988b2f7ee66d80e065

Observation 5b070bb3-c9d3-4433-99a9-60e9b48b9bce · outbound

This paper cites Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge

Reference 52

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source=arxiv_source observed=2026-08-15T20:50:10.706682Z digest=sha256:64b0d33c27317852353a3be0ecdf0e7b0123d3c72ceab79f5d314a3c477b4370

Observation 74837265-47e4-4644-a030-e78669015e47 · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 53

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source=arxiv_source observed=2026-08-15T20:50:10.711821Z digest=sha256:0867a75cf427999fff6dd547027ac532b2880358aaaa6dc31d0bb6a9875b42e1

Observation 2dfe73a8-18ed-42ac-ae77-95ff14473511 · outbound

This paper cites Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint, 2024.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint, 2024

Reference 54

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source=arxiv_source observed=2026-08-15T20:50:10.716804Z digest=sha256:0df014c8ac7e62674bdad247d8e12c6a84e0235818b165ff2f4585c1e40d4917

Observation 68bc86be-7a87-4501-9799-e0d7efb070ce · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 55

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source=arxiv_source observed=2026-08-15T20:50:10.721529Z digest=sha256:3776262cf7efad3465909e7f9ace10669dce5830714e2cb087c49e3d6167eb9b

Observation e8da5a41-b423-4431-af5e-73ffb094deec · outbound

This paper cites Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Reference 56

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source=arxiv_source observed=2026-08-15T20:50:10.727349Z digest=sha256:6332ead11702ee6a1928d57f2f5d8ecd8075a3a7be6a6a7b3a4e74c6a27ea675

Observation 27fb38c9-5622-4ad3-8c07-db05c783cf0c · outbound

This paper cites Qwen2.5 Technical Report.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Qwen2.5 Technical Report

Reference 57

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:50:10.732498Z digest=sha256:567b732f2f76e52a859578969fb6fa01c88cc459387f3872639fa8dab1cbc42f

Observation da640ddb-bfa1-46a9-9be8-d2e574d7a34f · outbound

This paper cites Mastering complex control in moba games with deep reinforcement learning.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Mastering complex control in moba games with deep reinforcement learning

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-15T20:50:12.023617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.737309Z digest=sha256:d091549dd775555a9e4daab5d8f9ec4f53587dfde2b003ebd29f502cfd58b699

Observation 2420f695-7e96-4b88-8ad8-1164498e6519 · outbound

This paper cites Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis

Reference 59

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source=arxiv_source observed=2026-08-15T20:50:10.742029Z digest=sha256:8be9006b0d7f43de39f036105926ab37e91b436ef2a3ac76a74b2d4f696308fb

Observation f6e5db68-d593-4fe2-8b62-5192716d26df · outbound

This paper cites Improving Reward Models with Synthetic Critiques.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Improving Reward Models with Synthetic Critiques

Reference 60

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source=arxiv_source observed=2026-08-15T20:50:10.747217Z digest=sha256:6c7e9188563178a885e796a62bdbda9aa79e0579b79c68289133aa77f14b8e9c

Observation fb263d60-1f64-4aa3-bf27-ec3c70ea846a · outbound

This paper cites Improve LLM-as-a-Judge Ability as a General Ability.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Improve LLM-as-a-Judge Ability as a General Ability

Reference 61

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source=arxiv_source observed=2026-08-15T20:50:10.752460Z digest=sha256:4edd6d3dba92a2f2f645f324c3da2434844707200b91ce0cfe6de901ddf6d813

Observation 13be2a22-8da4-46b1-9941-d6d1fb329489 · outbound

This paper cites Self-Generated Critiques Boost Reward Modeling for Language Models.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Self-Generated Critiques Boost Reward Modeling for Language Models

Reference 62

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source=arxiv_source observed=2026-08-15T20:50:10.757228Z digest=sha256:70728366944d27d2b143462270032e3dd61942ac70156b39c0580e5f8e2efb7e

Observation d38de029-615c-4ca0-aa7e-4a6ba0707f3f · outbound

This paper cites Z1: Efficient Test-time Scaling with Code.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Z1: Efficient Test-time Scaling with Code

Reference 63

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source=arxiv_source observed=2026-08-15T20:50:10.762040Z digest=sha256:a582cbb197cb9d6a04236196afebeefa22230ab6cadf35d7b18640221732f2dd

Observation 2b61f84a-557a-49f5-8e22-56a820d7d41e · outbound

This paper cites LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning

Reference 64

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:50:10.767033Z digest=sha256:0ba98d4382ba122bab2332ce994dba323c9acb0df24c06bdc015c184b193a10f

Observation cf0ccd04-6bfa-4465-af41-e84551740a41 · outbound

This paper cites Openprm: Building open-domain process-based reward models with preference trees.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Openprm: Building open-domain process-based reward models with preference trees

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-15T20:50:12.006003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T20:50:10.772283Z digest=sha256:5f4bfac2bdfc900cb36d30956d599a9904365551064ae3b9cd0f708ad79bf15a

Observation 99e9c94b-caf6-494d-960e-65dde388b2a1 · outbound

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

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 66

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source=arxiv_source observed=2026-08-15T20:50:10.777366Z digest=sha256:50a58d50de014e7caf092df6cd2fb1da2b7d3ec75bc65628b7c575d812928165

Observation 75580b05-339e-4015-b754-d08d2082f886 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 67

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source=arxiv_source observed=2026-08-15T20:50:10.782262Z digest=sha256:d990d3381806bb50d93ee054d2b47e7c0787484ed2c228e3d18941731788c040

Observation 3142b804-82d7-4317-a93c-a1dbceeb2f23 · outbound

This paper cites JudgeLM: Fine-tuned Large Language Models are Scalable Judges.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge JudgeLM: Fine-tuned Large Language Models are Scalable Judges

Reference 68

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source=arxiv_source observed=2026-08-15T20:50:10.787220Z digest=sha256:bf9393d81f09fedd6e5ada876a2515e53b4ae4871344aa3f12479c45acf24f6b

Observation a68f265e-105c-4f61-8486-e1a9b497d9b1 · outbound

This paper cites write newline.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge write newline

Reference 69

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source=arxiv_source observed=2026-08-15T20:50:10.792368Z digest=sha256:305c5878d098b99e01c94264f96cba41d1d3347a553162e633e04a9a1c63f29e

Observation e2fcb828-fd43-413a-97e8-d86becababed · outbound

This paper cites @esa (Ref.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge @esa (Ref

Reference 70

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source=arxiv_source observed=2026-08-15T20:50:10.798582Z digest=sha256:68ebdd9c289bae922454808a43b01ca7fd740daf14360ed9b45575b49f2e4e05

Observation 789001e0-dfb7-45ef-828a-a5dc19afdac5 · outbound

This paper cites an unresolved cited work.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge Unresolved cited work

Reference 71

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source=arxiv_source observed=2026-08-15T20:50:10.804046Z digest=sha256:491eed29b028b5b6d2f83feb780be4cb80c5462c4ee0249535666b8dcf129211

Observation 2482c9fe-d73e-4b1d-bbef-857ee8f47697 · outbound

This paper cites wait" step across four benchmark tasks. Because the proportion of responses altered after adding each.

J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge wait" step across four benchmark tasks. Because the proportion of responses altered after adding each

Reference 72

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source=arxiv_source observed=2026-08-15T20:50:10.809590Z digest=sha256:9d7932172e1b8ec59138038d691641cb6f7f66844e0f8b5cff53d1617eed79e8

Pith citing papers

Observation 800902ac-7871-4cda-8b64-b03a5c706977 · inbound

The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering cites this paper.

The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge

Reference 3

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arxiv_id, observed 2026-05-21T06:54:01.039517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-21T06:51:56.556213Z digest=sha256:63922e8fe2d625d4518d02c94a7d59e603b2b4d0716889eb28449a7e5bc97fed