·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3h◆A Survey on the Verification of Reinforcement Learning Policies3h◆Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3h◆SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h◆RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents3h◆LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models3h◆When to Plan: Learning to Select Between Reactive Control and Deliberative Planning3h◆SEER: Supervised Learning to Control Energetic Reasoning3h◆FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics3h◆Just A Rather Very Intelligent Spoken Agent3h◆A Research Prototype for Closed-Loop Generative Design of Customized Foot Orthoses via Semantic-Physics Alignment3h◆TopoTuner: Topological Finetuning of Large Language Models3h◆Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection3h◆DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening3h◆Tractable Query Answering under Epistemic Confidentiality Policies in DL Ontologies (extended version)3h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts3h◆Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning3h◆RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning3h◆FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images3h◆From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data3h◆Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3h◆A Survey on the Verification of Reinforcement Learning Policies3h◆Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3h◆SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h◆RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents3h◆LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models3h◆When to Plan: Learning to Select Between Reactive Control and Deliberative Planning3h◆SEER: Supervised Learning to Control Energetic Reasoning3h◆FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics3h◆Just A Rather Very Intelligent Spoken Agent3h◆A Research Prototype for Closed-Loop Generative Design of Customized Foot Orthoses via Semantic-Physics Alignment3h◆TopoTuner: Topological Finetuning of Large Language Models3h◆Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection3h◆DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening3h◆Tractable Query Answering under Epistemic Confidentiality Policies in DL Ontologies (extended version)3h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts3h◆Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning3h◆RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning3h◆FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images3h◆From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data3h◆
News/Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation
arxiv
PublishedJuly 13, 2026 at 4:00 AM
—neutral

Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

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

arXiv:2506.03062v2 Announce Type: replace Abstract: A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, e

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
arxivShapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3harxivA Survey on the Verification of Reinforcement Learning Policies3harxivSymbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3harxivSelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h
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