·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response1h◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training1h◆Large language model and reinforcement learning based autonomous construction and progress updates1h◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring1h◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution1h◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing1h◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning1h◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System1h◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability1h◆Physical AI: Concepts, Techniques, and Challenges1h◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems1h◆A general-purpose System One decision model for clinical records1h◆EDMA: Entropy-Driven Multimodal Answering1h◆Learned Control Variates for Rare Grid-Outage Estimation at Electric Vehicle Charging Stations1h◆TEX: Test-Time Scaling Testing Agents via Execution-based Cross-Validation1h◆When PTQ Calibration Helps or Hurts: Dose-Driven Collapse and Validation-Gated Abstention in Quantized Text Embedders1h◆Unified Sharp Bounds on Probabilities of Causation1h◆ProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions1h◆Harness Self-Evolution for Long-Horizon Terminal Agents1h◆Ask to Distill: Learning to Synthesize Data for On-Policy Distillation1h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response1h◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training1h◆Large language model and reinforcement learning based autonomous construction and progress updates1h◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring1h◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution1h◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing1h◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning1h◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System1h◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability1h◆Physical AI: Concepts, Techniques, and Challenges1h◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems1h◆A general-purpose System One decision model for clinical records1h◆EDMA: Entropy-Driven Multimodal Answering1h◆Learned Control Variates for Rare Grid-Outage Estimation at Electric Vehicle Charging Stations1h◆TEX: Test-Time Scaling Testing Agents via Execution-based Cross-Validation1h◆When PTQ Calibration Helps or Hurts: Dose-Driven Collapse and Validation-Gated Abstention in Quantized Text Embedders1h◆Unified Sharp Bounds on Probabilities of Causation1h◆ProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions1h◆Harness Self-Evolution for Long-Horizon Terminal Agents1h◆Ask to Distill: Learning to Synthesize Data for On-Policy Distillation1h◆
News/Learning in Matching Games with Bandit Feedback
arxiv
PublishedJune 17, 2026 at 4:00 AM
—neutral

Learning in Matching Games with Bandit Feedback

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

arXiv:2506.03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match. Specifically, we consider a setting in which matched agents engage in zero-sum games with initially unknown payoff

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 →
Tags
04
#machine-learning#game-theory#algorithm#research

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#game-theory#algorithm#research

Related coverage

More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response1harxivNavalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training1harxivLarge language model and reinforcement learning based autonomous construction and progress updates1harxivMedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring1h
The Bubble Brief
MON · WED · FRI

Read machine-learning 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