·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
MiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts4h◆IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training4h◆Asymmetries in Spontaneous and Instructed Deception4h◆AI Should Not Only Be Helpful. It Should Be Contingent. Artificial Intimacy, Sycophancy, and the Future of Social Learning4h◆OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets4h◆SCAFFOLD: A Large-Scale Structured Dataset of Computer Science Research Figures with Diagram QA and Chain-of-Thought Reasoning Traces4h◆UI-Venus-2 Technical Report4h◆EULER: Exploring Underused Links with Evidence-Checked Return for Multi-Agent Mathematical Discovery4h◆When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation4h◆Different representation learning objectives recover distinct latent structures from the same psychometric data4h◆SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning4h◆Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models4h◆Dependency-Aware Chain-of-Thought Compression for Financial Reasoning4h◆SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation4h◆Validity-Aware Jailbreak Evaluation for Large Language Models4h◆Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs4h◆Consistency Without Alignment: Item-Sensitive Language Models Indistinguishable From Random4h◆Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs4h◆A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies4h◆SOVER: Formal Certification of Optimization Reformulations via LLM-Assisted SMT Verification4h◆MiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts4h◆IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training4h◆Asymmetries in Spontaneous and Instructed Deception4h◆AI Should Not Only Be Helpful. It Should Be Contingent. Artificial Intimacy, Sycophancy, and the Future of Social Learning4h◆OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets4h◆SCAFFOLD: A Large-Scale Structured Dataset of Computer Science Research Figures with Diagram QA and Chain-of-Thought Reasoning Traces4h◆UI-Venus-2 Technical Report4h◆EULER: Exploring Underused Links with Evidence-Checked Return for Multi-Agent Mathematical Discovery4h◆When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation4h◆Different representation learning objectives recover distinct latent structures from the same psychometric data4h◆SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning4h◆Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models4h◆Dependency-Aware Chain-of-Thought Compression for Financial Reasoning4h◆SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generation4h◆Validity-Aware Jailbreak Evaluation for Large Language Models4h◆Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs4h◆Consistency Without Alignment: Item-Sensitive Language Models Indistinguishable From Random4h◆Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs4h◆A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies4h◆SOVER: Formal Certification of Optimization Reformulations via LLM-Assisted SMT Verification4h◆
News/Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models
arxiv
PublishedJuly 27, 2026 at 4:00 AM
—neutral

Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.22098v1 Announce Type: cross Abstract: Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often include noisy steps

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivMiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts4harxivIMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training4harxivAsymmetries in Spontaneous and Instructed Deception4harxivAI Should Not Only Be Helpful. It Should Be Contingent. Artificial Intimacy, Sycophancy, and the Future of Social Learning4h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews