Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-08T08:19:09.437464Z
Paper Citation Record · LEDGER
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
As of 10 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 1 inbound Pith citation observation for arXiv:2604.23333.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T06:48:41.331963Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
94 of 94 outbound references displayed
- verified exact40
- verified fuzzy43
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch6
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fd1272f0-df01-4d45-bd9a-e081c8a22cd4 · outbound
This paper cites On-policy distillation of language models: Learning from self-generated mistakes.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning On-policy distillation of language models: Learning from self-generated mistakes
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.604886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 62a90c69-16d0-4d4a-a952-d2b07ca5febd · outbound
This paper cites Barber, R.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Barber, R
Reference 2
- Resolution
- metadata mismatch
- doi, observed 2026-05-08T22:34:28.718729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3b4e2a25-f8b2-4cb2-8cec-dd36660a7133 · outbound
This paper cites Conformal risk control.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Conformal risk control
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.632806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 225b676f-c243-467c-9aae-729aa5860759 · outbound
This paper cites Reconsidering LLM uncertainty estimation methods in the wild.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Reconsidering LLM uncertainty estimation methods in the wild
Reference 4
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.669854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b1cecae0-2a6c-4066-9c2f-4771d76c1a43 · outbound
This paper cites Linguistic calibration of long-form generations.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Linguistic calibration of long-form generations
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.599634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c6414d67-98ad-45a9-b3ca-bde9d0fccd34 · outbound
This paper cites Rewarding doubt: A reinforcement learning approach to calibrated confidence expression of large language models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Rewarding doubt: A reinforcement learning approach to calibrated confidence expression of large language models
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.628017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fff23457-9f47-4199-a675-a01faf932cb1 · outbound
This paper cites Bereket and J.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Bereket and J
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.302769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 04ee208a-c0da-4018-878d-53383f949a62 · outbound
This paper cites Do Androids Know They’re Only Dreaming of Electric Sheep?.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Do Androids Know They’re Only Dreaming of Electric Sheep?
Reference 8
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.714791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e45f9f31-1f6b-4c2a-9566-a3afb12a81c8 · outbound
This paper cites Uncertain Natural Language Inference.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Uncertain Natural Language Inference
Reference 9
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.677536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 194d0830-dfe6-4e88-8a46-9f14ad0fc725 · outbound
This paper cites A close look into the calibration of pre-trained language models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning A close look into the calibration of pre-trained language models
Reference 10
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.673639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation acad2e9a-1d91-407a-b682-c58cdc0cd67f · outbound
This paper cites Mind the confidence gap: Overconfidence, calibration, and distractor effects in large language models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Mind the confidence gap: Overconfidence, calibration, and distractor effects in large language models
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.625395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ba73bd9c-b312-46b3-a77a-b3986201bb76 · outbound
This paper cites Evaluating language models as risk scores.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Evaluating language models as risk scores
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.597097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8118ff22-c021-447c-a758-fbe4a6bca34c · outbound
This paper cites arXiv preprint arXiv:2603.09309 (2026).
Process Supervision of Confidence Margin for Calibrated LLM Reasoning arXiv preprint arXiv:2603.09309 (2026)
Reference 13
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T20:41:12.062181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a0df8c10-e56c-4d0c-9234-65c0e2dc5bd2 · outbound
This paper cites Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty
Reference 14
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T00:00:22.282912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 4274fba9-8265-405c-9bd1-0cd480105a2c · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.070150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d128a3c7-bd13-45a0-a906-8638ac224943 · outbound
This paper cites Calibration of pre-trained transformers.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Calibration of pre-trained transformers
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.602127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5c09aea8-7652-4c17-a01f-69ddf6ec0e34 · outbound
This paper cites Detecting hallucinations in large language models using semantic entropy.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Detecting hallucinations in large language models using semantic entropy
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.607466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation af936a36-0466-4beb-b767-25f7106813be · outbound
This paper cites Deep Think with Confidence.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Deep Think with Confidence
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T11:30:21.518780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c9324866-5083-4271-b0cc-967a7d707468 · outbound
This paper cites Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.225348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 43982279-c7db-46c1-8238-4f257e4d80a2 · outbound
This paper cites On calibration of modern neural networks.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning On calibration of modern neural networks
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.630777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d904375c-697d-4c78-bfac-a464c7c4f4d1 · outbound
This paper cites Language model cascades: Token-level uncertainty and beyond.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Language model cascades: Token-level uncertainty and beyond
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.589798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c4b1cfdc-ba02-415c-bf80-965992023719 · outbound
This paper cites O lympiad B ench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning O lympiad B ench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
Reference 22
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.661171Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f5ec1206-ee0a-46ff-bea3-076db50247b8 · outbound
This paper cites Measuring mathematical problem solving with the MATH dataset.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Measuring mathematical problem solving with the MATH dataset
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.592482Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 23fb203e-c842-48a3-bbe3-dc713987d2b4 · outbound
This paper cites Efficient Test-Time Scaling via Self-Calibration.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Efficient Test-Time Scaling via Self-Calibration
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.347697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 97eb9b03-c697-44ec-bbf5-e44f25368210 · outbound
This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Reference 25
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.706923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 57394729-3a4e-46a8-96a9-9142b2c53f53 · outbound
This paper cites Reinforcement Learning via Self-Distillation.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Reinforcement Learning via Self-Distillation
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T04:29:18.795354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 209dc3a1-c0ce-493a-8d7e-bd530bf3520e · outbound
This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Livecodebench: Holistic and contamination free evaluation of large language models for code
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.594691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a4d3476e-ea41-4c22-b44f-09024919246c · outbound
This paper cites Calibrating zero-shot cross-lingual (un-) structured predictions.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Calibrating zero-shot cross-lingual (un-) structured predictions
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.609903Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 272d0462-2136-49b8-8009-f159c4627da5 · outbound
This paper cites Addressing the Binning Problem in Calibration Assessment through Scalar Annotations.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Addressing the Binning Problem in Calibration Assessment through Scalar Annotations
Reference 29
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.665676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b26b2712-3bb0-462a-9470-2e43a2113618 · outbound
This paper cites Conformal linguistic calibration: Trading-off between factuality and specificity.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Conformal linguistic calibration: Trading-off between factuality and specificity
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.623048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 17ab1cfd-b331-4c58-9cc3-cd6521422b36 · outbound
This paper cites Is that your final answer? test-time scaling improves selective question answering.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Is that your final answer? test-time scaling improves selective question answering
Reference 31
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.724549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ce38668a-50ac-4cdb-bc70-758d102a26fa · outbound
This paper cites Why Language Models Hallucinate.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Why Language Models Hallucinate
Reference 32
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T12:32:40.683342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8ac5acb3-0999-47cf-91ee-67809c2ad9e8 · outbound
This paper cites Scalable best-of-n selection for large language models via self-certainty.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Scalable best-of-n selection for large language models via self-certainty
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.614155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 16f1ae38-4509-4703-ba91-7747fa83ceb7 · outbound
This paper cites Large language models must be taught to know what they don't know.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Large language models must be taught to know what they don't know
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.616558Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bcee4fd1-bbbe-4aed-b8b1-c3d7b6ecc4da · outbound
This paper cites Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-27T03:05:05.764939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 97afc72d-bee6-4933-96a9-1af084c9ac6d · outbound
This paper cites Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T00:52:02.773759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1dee103a-586c-4888-8e3a-421aa1345968 · outbound
This paper cites Semantic entropy probes: Robust and cheap hallucination detection in LLM s.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Semantic entropy probes: Robust and cheap hallucination detection in LLM s
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.637437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 09986e8b-bda9-4e4b-a042-03608dbf0347 · outbound
This paper cites Think with moderation: Reasoning models and confidence calibration in the climate domain.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Think with moderation: Reasoning models and confidence calibration in the climate domain
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.576742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b1f51ffa-1a68-4259-bf99-b87b2264c5e8 · outbound
This paper cites Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.635129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 77afc915-3c1a-4fc3-8d64-d3b92a4b9313 · outbound
This paper cites Taming overconfidence in LLM s: Reward calibration in RLHF.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Taming overconfidence in LLM s: Reward calibration in RLHF
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.579581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 082160ae-529d-4a88-befb-decc08150de8 · outbound
This paper cites Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search
Reference 41
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T20:41:12.130965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation dd8714b0-c0d3-408b-babd-f7873d1e2c3b · outbound
This paper cites Conftuner: Training large language models to express their confidence verbally.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Conftuner: Training large language models to express their confidence verbally
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.582151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2d407fa2-085d-4b4d-97f8-0d7402e37f9c · outbound
This paper cites Let's verify step by step.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Let's verify step by step
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.584677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c87b0da9-7f0d-400a-9f90-cd8ba3d6a707 · outbound
This paper cites C 2gspg: Confidence- calibrated group sequence policy gradient towards self- aware reasoning.arXiv preprint arXiv:2509.23129.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning C 2gspg: Confidence- calibrated group sequence policy gradient towards self- aware reasoning.arXiv preprint arXiv:2509.23129
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.314348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3058fae0-faa6-4bc3-b902-88b091090a92 · outbound
This paper cites C\ 2\ GSPG : Confidence-calibrated group sequence policy gradient towards self-aware reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning C\ 2\ GSPG : Confidence-calibrated group sequence policy gradient towards self-aware reasoning
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.618713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e3f73d66-006e-4162-b625-92efe674a175 · outbound
This paper cites Logiqa: a challenge dataset for machine reading comprehension with logical reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Logiqa: a challenge dataset for machine reading comprehension with logical reasoning
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.620953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 94d6c7db-a1ad-42e8-a479-bf9da999bdf4 · outbound
This paper cites Wong, Lidia S.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Wong, Lidia S
Reference 47
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.702470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f0c7e4a7-4d68-46ec-bdaa-445d282696b7 · outbound
This paper cites Uncertainty quantification and confidence calibration in large language models: A survey.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Uncertainty quantification and confidence calibration in large language models: A survey
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-08T22:34:28.695062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c7a4a1e5-b2da-4a83-acb3-e32c4c20add6 · outbound
This paper cites Your pre-trained LLM is secretly an unsupervised confidence calibrator.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Your pre-trained LLM is secretly an unsupervised confidence calibrator
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.571118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b11ff9b4-14cd-4bf4-af4a-9dc9f798cb6b · outbound
This paper cites Improve mathematical reasoning in language models with automated process supervision, 2025 b.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Improve mathematical reasoning in language models with automated process supervision, 2025 b
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.573802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9cce0c0d-49b6-4f68-acbd-e568ed75d00c · outbound
This paper cites Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.120451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 56b1c3ce-564e-4dd5-8e15-9a1589a609e0 · outbound
This paper cites Reasoning about uncertainty: Do reasoning models know when they don’t know? In Findings of the Association for Computational Linguistics: EACL 2026, pp.\ 3408--3458.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Reasoning about uncertainty: Do reasoning models know when they don’t know? In Findings of the Association for Computational Linguistics: EACL 2026, pp.\ 3408--3458
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.587369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 042d080c-1e64-46f7-b973-e6b939b4385f · outbound
This paper cites Closing the confidence-faithfulness gap in large language models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Closing the confidence-faithfulness gap in large language models
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.139298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6e9acc8b-3d9e-49ec-9a54-1c0f3714c54c · outbound
This paper cites S1: Simple test-time scaling.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning S1: Simple test-time scaling
Reference 54
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.698725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation afc0299a-d722-4046-8cff-7c92ce147dc4 · outbound
This paper cites When do LLM s need retrieval augmentation? mitigating LLM s' overconfidence helps retrieval augmentation.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning When do LLM s need retrieval augmentation? mitigating LLM s' overconfidence helps retrieval augmentation
Reference 55
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.721488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3f7fcfc4-3b30-4d0c-aa81-12789e3a1092 · outbound
This paper cites OpenAI o1 System Card.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning OpenAI o1 System Card
Reference 56
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.150846Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fc5695f9-3798-490d-b24a-2275385ab356 · outbound
This paper cites Obtaining well calibrated probabilities using bayesian binning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Obtaining well calibrated probabilities using bayesian binning
Reference 57
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.650353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 836030a4-e0c3-4e41-a2fd-dfa71c0c7075 · outbound
This paper cites Optimizing anytime reasoning via budget relative policy optimization.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Optimizing anytime reasoning via budget relative policy optimization
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.568389Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f000ed63-aa98-4381-b60f-c7546516f89d · outbound
This paper cites Demystifying reasoning dynamics with mutual information: Thinking tokens are information peaks in LLM reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Demystifying reasoning dynamics with mutual information: Thinking tokens are information peaks in LLM reasoning
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.612001Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 63f45eef-7739-40cf-ae97-8b8b72d513f3 · outbound
This paper cites an unresolved cited work.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T17:07:40.639483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation dfd38fd5-50d0-4a01-b2cc-a2b5b3009daa · outbound
This paper cites Jaakkola, and Regina Barzilay.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Jaakkola, and Regina Barzilay
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.667772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 27357831-5c28-49cc-9e59-230ea2d037d3 · outbound
This paper cites an unresolved cited work.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Unresolved cited work
Reference 62
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T17:07:40.669826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation aa1f0fe6-6909-4013-956e-2eb5adae305f · outbound
This paper cites Proximal Policy Optimization Algorithms.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Proximal Policy Optimization Algorithms
Reference 63
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.187773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b2eedcdf-8de0-4752-a704-3c85b4f9af32 · outbound
This paper cites Rewarding progress: Scaling automated process verifiers for LLM reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Rewarding progress: Scaling automated process verifiers for LLM reasoning
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.672070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2607e0b4-fb40-4f1a-995e-5357094e4e07 · outbound
This paper cites A tutorial on conformal prediction.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning A tutorial on conformal prediction
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.674355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ce80752b-fd38-483a-8c1c-b25113cae32e · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 66
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.211505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d8352934-9bbe-4f7d-9cee-2fec2acacbbd · outbound
This paper cites Wornell, and Soumya Ghosh.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Wornell, and Soumya Ghosh
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.665602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 705a36ed-57d3-4547-8498-a3a00013c4c4 · outbound
This paper cites Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.676674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 20160f7c-1ef9-4585-bc4a-6178ee6e8d61 · outbound
This paper cites Alison Noble.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Alison Noble
Reference 69
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.681327Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e499919b-990c-4cd5-a36b-7b3259358bef · outbound
This paper cites doi: 10.18653/v1/2023.emnlp-main.330.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning doi: 10.18653/v1/2023.emnlp-main.330
Reference 70
- Resolution
- metadata mismatch
- doi, observed 2026-05-08T22:34:28.689505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3d18eb14-21ed-43f4-a7a2-9535c6f8dc6f · outbound
This paper cites Calibrating Verbalized Probabilities for Large Language Models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Calibrating Verbalized Probabilities for Large Language Models
Reference 71
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.166451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f29053d1-b153-4b8f-9b29-2e95abf12c62 · outbound
This paper cites Always tell me the odds: Fine-grained conditional probability estimation.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Always tell me the odds: Fine-grained conditional probability estimation
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.678770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 70421b68-46a3-48ac-a7a3-2a368cf2f1de · outbound
This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Reference 73
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T22:34:16.071337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 00c0c9c8-38b9-4b76-bea2-d5827b685676 · outbound
This paper cites Calibrating verbalized confidence with self-generated distractors.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Calibrating verbalized confidence with self-generated distractors
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.680939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 836ac16e-1846-40c7-85a1-89adc4d56b14 · outbound
This paper cites Conformal Thinking: Risk Control for Reasoning on a Compute Budget.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Conformal Thinking: Risk Control for Reasoning on a Compute Budget
Reference 75
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:43:11.628743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9cc0b33f-ee76-43b3-a0f3-b0f029817e6a · outbound
This paper cites C on U : Conformal uncertainty in large language models with correctness coverage guarantees.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning C on U : Conformal uncertainty in large language models with correctness coverage guarantees
Reference 76
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.642387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 61e175ff-4b2e-480e-a8d2-7c638f20682a · outbound
This paper cites Thought calibration: Efficient and confident test-time scaling.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Thought calibration: Efficient and confident test-time scaling
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.663418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 785f3ed1-e96b-4b08-a9f1-d00e2cc542af · outbound
This paper cites Can LLM s express their uncertainty? an empirical evaluation of confidence elicitation in LLM s.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Can LLM s express their uncertainty? an empirical evaluation of confidence elicitation in LLM s
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.658902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 95aa9c4f-3655-48ee-9ff5-28c2c7d55180 · outbound
This paper cites Beyond correctness: Harmonizing process and outcome rewards through rl training.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Beyond correctness: Harmonizing process and outcome rewards through rl training
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.656468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 166d5225-c877-4956-a144-ed5e3ffc80db · outbound
This paper cites LLM Probability Concentration: How Alignment Shrinks the Generative Horizon.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning LLM Probability Concentration: How Alignment Shrinks the Generative Horizon
Reference 80
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.261594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 45eb69cf-7a3a-41b9-8ba4-87f4241e0d73 · outbound
This paper cites Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?
Reference 81
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.655110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 4bcc19fd-c171-4037-ac66-9ee1108858cb · outbound
This paper cites OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning
Reference 82
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:12.177273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b7dca2aa-de39-4794-9998-c7cb861d1a73 · outbound
This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Reference 83
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.159349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d8c2b988-e882-4782-9176-d703685d228b · outbound
This paper cites Reasoning models know when they re right: Probing hidden states for self-verification.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Reasoning models know when they re right: Probing hidden states for self-verification
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.660953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation eae8f818-50af-405d-a294-635d846bbf80 · outbound
This paper cites GRPO - LEAD : A difficulty-aware reinforcement learning approach for concise mathematical reasoning in language models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning GRPO - LEAD : A difficulty-aware reinforcement learning approach for concise mathematical reasoning in language models
Reference 85
- Resolution
- verified exact
- doi, observed 2026-05-08T22:34:28.685372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ac930bb2-b14d-4541-b5b8-aea21ce3fd93 · outbound
This paper cites an unresolved cited work.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Unresolved cited work
Reference 86
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T17:07:40.648955Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a154b8bd-8a7b-44ad-9d78-cee204d0317e · outbound
This paper cites The Lessons of Developing Process Reward Models in Mathematical Reasoning.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning The Lessons of Developing Process Reward Models in Mathematical Reasoning
Reference 87
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T13:43:43.298530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8ba7f292-78cd-4085-b09f-68b8f4eebc34 · outbound
This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
Reference 88
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:54:31.187112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 45abc3c0-fcdc-42c2-8ebc-8520eb949ac3 · outbound
This paper cites Calibrate before use: Improving few-shot performance of language models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Calibrate before use: Improving few-shot performance of language models
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.643897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5fdcce4e-9326-46b4-bab9-8b0875ca0526 · outbound
This paper cites A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models
Reference 90
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T20:41:12.237411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b1227ec7-72df-4e76-9641-87207889a73f · outbound
This paper cites write newline.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning write newline
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.646716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bd5e107c-3794-471b-8f10-38d9c9321a18 · outbound
This paper cites @esa (Ref.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning @esa (Ref
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T17:07:40.651575Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e5932a13-9554-468e-b6c1-c8f485b73438 · outbound
This paper cites an unresolved cited work.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Unresolved cited work
Reference 93
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T17:07:40.654239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a834bab9-d393-4e5d-bafc-03898712ac4d · outbound
This paper cites an unresolved cited work.
Process Supervision of Confidence Margin for Calibrated LLM Reasoning Unresolved cited work
Reference 94
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T17:07:40.641516Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
Observation bdca9a63-aad3-4285-995c-87118c8e3c97 · inbound
SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute cites this paper.
SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute Process Supervision of Confidence Margin for Calibrated LLM Reasoning
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T06:48:41.331963Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.