Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization | Paper Citation Record

Observation 3b9bd789-7001-4963-b271-8347766d6235 · outbound

This paper cites 2025 AIME I.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization 2025 AIME I

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.458277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 975477b9-5ab6-40b6-9ce5-ad386c6a5db5 · outbound

This paper cites 2025 AIME II.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization 2025 AIME II

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.445903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7b5e2fe3-233c-4983-aa81-969f2ff30301 · outbound

This paper cites Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.015915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7e8726b4-8f8e-416a-8c88-02b301d7e3c0 · outbound

This paper cites Math- arena: Evaluating llms on uncontaminated math competitions.Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmark.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Math- arena: Evaluating llms on uncontaminated math competitions.Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmark

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.462425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 83cdb0f9-6b8b-40a4-968a-6688b1af6000 · outbound

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

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:42:23.872588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6b0a833e-7b78-401d-86c1-ae13f9f2261a · outbound

This paper cites Sets: Leveraging self-verification and self-correction for improved test-time scaling.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Sets: Leveraging self-verification and self-correction for improved test-time scaling

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.075496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e7575d70-7073-4a97-bc66-9180532a7ddb · outbound

This paper cites Universal self-consistency for large language models.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Universal self-consistency for large language models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.454179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a263e3a2-7197-484a-b618-34f5399f4c81 · outbound

This paper cites Deep reinforcement learning from human preferences.Advances in neural information processing systems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Deep reinforcement learning from human preferences.Advances in neural information processing systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.478460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation daad86e0-b398-4e51-b6a4-2823a8385ed7 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Training Verifiers to Solve Math Word Problems

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:11:10.099870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b24f3a32-603c-46c0-a886-4c96ec48140d · outbound

This paper cites Deepseek-v4: Towards highly efficient million-token context intelligence.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Deepseek-v4: Towards highly efficient million-token context intelligence

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.474706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2d26d82a-5491-4537-b6d2-11bbe9bfa49e · outbound

This paper cites Llm self- correction with decrim: Decompose, critique, and refine for enhanced following of instructions with multiple constraints.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Llm self- correction with decrim: Decompose, critique, and refine for enhanced following of instructions with multiple constraints

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.470235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 812e7a41-0456-4bb6-a3c1-5ad2247d8e11 · outbound

This paper cites Deep think with confidence.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Deep think with confidence

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.441334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2871ba63-4520-4d55-8a4b-444df7004048 · outbound

This paper cites Critic: Large language models can self-correct with tool-interactive critiquing.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Critic: Large language models can self-correct with tool-interactive critiquing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.450360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation df697ced-4469-475a-b75b-ae74755ca92e · outbound

This paper cites CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:57:16.443569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0e784383-6fe6-4af0-a91d-d28cc5435d5f · outbound

This paper cites A survey on llm-as-a-judge.The Innovation.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization A survey on llm-as-a-judge.The Innovation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.409206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 96d8941a-fc7c-4add-a985-1ebe60d38423 · outbound

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

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:11:10.108461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 05e36a7f-37ab-490d-ae86-a07668be8b1f · outbound

This paper cites Reward reasoning models.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Reward reasoning models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.419138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9f2e287-6ac2-41c1-ac3b-133aecf4179a · outbound

This paper cites Reason- ing with language model is planning with world model.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Reason- ing with language model is planning with world model

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.432547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 81c6c895-ee8c-4add-b604-abcf670b31d7 · outbound

This paper cites Is best-of-n the best of them? coverage, scaling, and optimality in inference-time alignment.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Is best-of-n the best of them? coverage, scaling, and optimality in inference-time alignment

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.403414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9860502-bbae-446d-801c-784521b25c4d · outbound

This paper cites Regularized best-of-n sampling with minimum bayes risk objective for language model alignment.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Regularized best-of-n sampling with minimum bayes risk objective for language model alignment

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.423640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5abfc04e-d477-4564-b6e6-0c5dc32891f0 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Language Models (Mostly) Know What They Know

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:11:10.043435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 73eb18b2-b284-48fa-9dc1-071a639a6b87 · outbound

This paper cites Scalable best-of-n selection for large language models via self-certainty.arXiv preprint arXiv:2502.18581.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Scalable best-of-n selection for large language models via self-certainty.arXiv preprint arXiv:2502.18581

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.069715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 244d85e3-69c2-482c-93d6-f8a7eff5cdbb · outbound

This paper cites Semantic self-consistency: Enhancing language model reasoning via semantic weighting.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Semantic self-consistency: Enhancing language model reasoning via semantic weighting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.428129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation af7af028-58b8-4be8-98df-a4f4d46ac147 · outbound

This paper cites Evolving Deeper LLM Thinking.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Evolving Deeper LLM Thinking

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.125405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 080c704e-a2f7-4787-8a07-4e603d2d10b5 · outbound

This paper cites PairJudge RM: Perform Best-of-N Sampling with Knockout Tournament.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization PairJudge RM: Perform Best-of-N Sampling with Knockout Tournament

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:11:10.035241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6cf95e83-e715-4802-9cef-1f79675aa1bc · outbound

This paper cites Large Language Model Guided Tree-of-Thought.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Large Language Model Guided Tree-of-Thought

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:11:10.029151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 314e3eba-84cc-4a78-8531-86014e3e7136 · outbound

This paper cites Ising formulations of many np problems.Frontiers in physics, 2:5.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Ising formulations of many np problems.Frontiers in physics, 2:5

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.414533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6210e271-5ecf-428b-97eb-0c3f8951f61b · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.Advances in Neural Information Processing Systems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Self-refine: Iterative refinement with self-feedback.Advances in Neural Information Processing Systems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.437253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a71a3db0-7228-4ee0-8f92-a3485096e791 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b model card.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization gpt-oss-120b & gpt-oss-20b model card

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.533001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1ea82064-8149-44f7-b6a3-324b269bb46d · outbound

This paper cites Introducing gpt-5.2.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Introducing gpt-5.2

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.466225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b505b744-a797-4ec5-aab4-c2cd6ee7c55f · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Training language models to follow instructions with human feedback.Advances in neural information processing systems

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.584060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5412e60e-fce1-4043-9ed3-acc81ceb3001 · outbound

This paper cites Recursive introspection: Teaching language model agents how to self-improve.Advances in Neural Information Processing Systems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Recursive introspection: Teaching language model agents how to self-improve.Advances in Neural Information Processing Systems

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.581737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bccc5afd-c380-4ae6-a417-cc209229d9d7 · outbound

This paper cites Qwen3.5: Towards native multimodal agents, February 2026.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Qwen3.5: Towards native multimodal agents, February 2026

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.527707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7283fbd7-a99a-4f77-a876-dea7c199811a · outbound

This paper cites How do large language monkeys get their power (laws)? InForty-second International Conference on Machine Learning.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization How do large language monkeys get their power (laws)? InForty-second International Conference on Machine Learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.576762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d0818ba1-3d1f-44e4-9582-a2098e6d683f · outbound

This paper cites Reasoning under uncertainty: Efficient llm inference via unsupervised confidence dilution and convergent adaptive sampling.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Reasoning under uncertainty: Efficient llm inference via unsupervised confidence dilution and convergent adaptive sampling

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.579293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9b72b873-ab79-47c4-92dc-dc6e8f1c92a4 · outbound

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

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:11:10.117756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b099622-5125-47ad-bac6-ecfc9ccacbb7 · outbound

This paper cites Confidence improves self-consistency in llms.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Confidence improves self-consistency in llms

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.049794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 67a7e7cc-387f-4289-84fc-3072d023f034 · outbound

This paper cites Logical reasoning with outcome reward models for test-time scaling.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Logical reasoning with outcome reward models for test-time scaling

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:32:35.390794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 36513918-d324-4e9b-a897-30e81ce48803 · outbound

This paper cites Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.553290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7a79df50-db0e-450d-a07e-5142c66ab264 · outbound

This paper cites Reasoning aware self-consistency: Leveraging reasoning paths for efficient llm sampling.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Reasoning aware self-consistency: Leveraging reasoning paths for efficient llm sampling

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.555871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 98839d39-e8fa-4df3-9449-084b9964748f · outbound

This paper cites Alphazero-like tree-search can guide large language model decoding and training.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Alphazero-like tree-search can guide large language model decoding and training

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.560791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 61bc7dbe-39c4-45ab-91b5-4d0e39f2f2f0 · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Self-consistency improves chain of thought reasoning in language models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.558269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f670f659-81e0-4a38-b83a-15632de609ca · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Chain-of-thought prompting elicits reasoning in large language models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.563231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5112d422-8fda-4bb3-9c39-19949eb09f31 · outbound

This paper cites From decoding to meta-generation: Inference-time algorithms for large language models.Transactions on Machine Learning Research.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization From decoding to meta-generation: Inference-time algorithms for large language models.Transactions on Machine Learning Research

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.573973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1db9cb1e-9368-474a-8f13-ab83de4bb394 · outbound

This paper cites Large language models are better reasoners with self-verification.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Large language models are better reasoners with self-verification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.565708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 66fde16e-e2de-4e70-9a60-7440070013b8 · outbound

This paper cites Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.541634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a6b2aa94-bad2-436c-b412-878214bbefa5 · outbound

This paper cites Qwen3 Technical Report.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Qwen3 Technical Report

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:11:10.058108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 104523aa-118e-4d1f-b4f4-4b459330b927 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Ad- vances in neural information processing systems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Tree of thoughts: Deliberate problem solving with large language models.Ad- vances in neural information processing systems

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.544033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation de664b60-a000-4bd2-999a-2c955f7dd1f6 · outbound

This paper cites Confidence-aware reasoning: Optimizing self-guided thinking trajectories in large reasoning models.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Confidence-aware reasoning: Optimizing self-guided thinking trajectories in large reasoning models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.546290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d4459b9d-16ab-41e0-a292-2e6344420bba · outbound

This paper cites Sample, scrutinize and scale: Effective inference-time search by scaling verification.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Sample, scrutinize and scale: Effective inference-time search by scaling verification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.548678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bcc81349-1b2f-4142-a114-cacd5db33c87 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information processing systems.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information processing systems

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.533278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3cbb67a0-3ae3-4e45-9bc2-7c07e0fb92cb · outbound

This paper cites Evaluating judges as evaluators: The jetts benchmark of llm-as-judges as test-time scaling evaluators.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Evaluating judges as evaluators: The jetts benchmark of llm-as-judges as test-time scaling evaluators

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.536169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d4edf031-30aa-48ef-91f0-ee2fd33df0cc · outbound

This paper cites Accu- racy(%)/$Cost.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Accu- racy(%)/$Cost

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-05-26T15:27:57.538978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e31d48d2-9b67-4433-90e8-e70b547875a8 · outbound

This paper cites Reasoning: \n\n.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Reasoning: \n\n

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.550843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b79d106-bdc3-4aff-9476-1b18842b188c · outbound

This paper cites an unresolved cited work.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Unresolved cited work

Reference 56

Resolution: parse uncertain; raw_fallback, observed 2026-05-26T15:27:57.571131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4470fedc-27eb-4190-bbc9-060958bea3dd · outbound

This paper cites Reasoning: \n\n.

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization Reasoning: \n\n

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:27:57.568359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers