·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Introducing Cosmos 3 Edge50m◆YouTube clarifies policies around AI slop and upsetting videos1h◆China delivers a one-two punch to America’s AI dominance6h◆AI is more likely than humans to form biases when hiring8h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal12h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents12h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment12h◆Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length12h◆Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications12h◆DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales12h◆Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors12h◆CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data12h◆cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data12h◆Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI12h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?12h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes12h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems12h◆A Formally Grounded ODRL Evaluator: Implementation and Comparison12h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents12h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections12h◆Introducing Cosmos 3 Edge50m◆YouTube clarifies policies around AI slop and upsetting videos1h◆China delivers a one-two punch to America’s AI dominance6h◆AI is more likely than humans to form biases when hiring8h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal12h◆SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents12h◆PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment12h◆Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length12h◆Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications12h◆DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales12h◆Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors12h◆CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data12h◆cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data12h◆Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI12h◆Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?12h◆Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes12h◆From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems12h◆A Formally Grounded ODRL Evaluator: Implementation and Comparison12h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents12h◆EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections12h◆
News/From Drift to Coherence: Stabilizing Beliefs in LLMs
arxiv
PublishedJune 19, 2026 at 4:00 AM
—neutral

From Drift to Coherence: Stabilizing Beliefs in LLMs

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

arXiv:2606.17832v2 Announce Type: replace Abstract: Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-context learning settings. W

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
arxivBeyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal12harxivSkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents12harxivPolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment12harxivLatency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length12h
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