·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Bridging the Gap: Converting Read Text to Conversational Dialogue1h◆Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1h◆RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1h◆Witness Overlap: Directional Provenance Inside Open-Weight Model Families1h◆From Migration to Calibration: Aligning Agents with Deployment Requirements1h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science1h◆Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration1h◆KV$^2$: A Self-Refining KV Cache1h◆Video-Time Priors for EEG-fNIRS Emotion Regression on Familiar Videos1h◆Asynchrony Is Nearly Free for Evolution Strategies on Long-Horizon LLM Agentic Tasks1h◆OTel: Open Telco AI Datasets, Benchmarks, and Models1h◆SCOPE: Certified Theorem Proving with a Language Model as the Policy Planner1h◆ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding1h◆AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery1h◆RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations1h◆LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints1h◆ReTerm: Recovery Training for Self-Improving Terminal Agents1h◆What Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports1h◆Co-Evolving Acting and Forecasting for Language Agents1h◆Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models1h◆Bridging the Gap: Converting Read Text to Conversational Dialogue1h◆Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1h◆RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1h◆Witness Overlap: Directional Provenance Inside Open-Weight Model Families1h◆From Migration to Calibration: Aligning Agents with Deployment Requirements1h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science1h◆Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration1h◆KV$^2$: A Self-Refining KV Cache1h◆Video-Time Priors for EEG-fNIRS Emotion Regression on Familiar Videos1h◆Asynchrony Is Nearly Free for Evolution Strategies on Long-Horizon LLM Agentic Tasks1h◆OTel: Open Telco AI Datasets, Benchmarks, and Models1h◆SCOPE: Certified Theorem Proving with a Language Model as the Policy Planner1h◆ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding1h◆AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery1h◆RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations1h◆LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints1h◆ReTerm: Recovery Training for Self-Improving Terminal Agents1h◆What Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports1h◆Co-Evolving Acting and Forecasting for Language Agents1h◆Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models1h◆
News/Sharp Non-Asymptotic Analysis of the Penalized Challenger in $\beta$-EB-TCI for Bernoulli Bandits
arxiv
PublishedOctober 3, 2026 at 4:00 AM

Sharp Non-Asymptotic Analysis of the Penalized Challenger in $\beta$-EB-TCI for Bernoulli Bandits

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

arXiv:2610.01951v1 Announce Type: new Abstract: Top-two algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well understood. We study this problem for Bernoulli bandits through $\beta$-EB-TCI, the empirical-best top-t

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

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
arxivBridging the Gap: Converting Read Text to Conversational Dialogue1harxivTruth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1harxivRICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1harxivWitness Overlap: Directional Provenance Inside Open-Weight Model Families1h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews