·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
New York becomes the first state to enact a data center moratorium2h◆FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks7h◆RL Forgets! Towards Continual Policy Optimization7h◆Learnable Mixed Nash Equilibria are Collectively Rational7h◆Oracle-Efficient Combinatorial Semi-Bandits7h◆SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification7h◆Efficiently Adapting Spoken Language Models for the Singaporean Context7h◆Global Merger-Arbitrage Forecasting with Language Models7h◆Dzongkha Next Word Prediction System7h◆Production and Perception in LLMs: A Token Probability Approach7h◆Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature7h◆Manifold Constrained Tabular Deep Neural Networks7h◆Scaffold splits hide structural-frontier failures in ADMET models7h◆The Spectral Structure of Latent Treatment Effects7h◆Omni-Decision: A Progressive Evidence-State Agent System for Omni-Modal QA7h◆Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns7h◆Think Through a Bottleneck: Hourglass Reasoning for Rigorous Induction7h◆Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models7h◆Efficient Online Proportional Sampling with Applications to Smoothed Online Learning7h◆CDFM: Towards a General-Purpose Causal Discovery Foundation Model7h◆New York becomes the first state to enact a data center moratorium2h◆FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks7h◆RL Forgets! Towards Continual Policy Optimization7h◆Learnable Mixed Nash Equilibria are Collectively Rational7h◆Oracle-Efficient Combinatorial Semi-Bandits7h◆SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification7h◆Efficiently Adapting Spoken Language Models for the Singaporean Context7h◆Global Merger-Arbitrage Forecasting with Language Models7h◆Dzongkha Next Word Prediction System7h◆Production and Perception in LLMs: A Token Probability Approach7h◆Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature7h◆Manifold Constrained Tabular Deep Neural Networks7h◆Scaffold splits hide structural-frontier failures in ADMET models7h◆The Spectral Structure of Latent Treatment Effects7h◆Omni-Decision: A Progressive Evidence-State Agent System for Omni-Modal QA7h◆Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns7h◆Think Through a Bottleneck: Hourglass Reasoning for Rigorous Induction7h◆Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models7h◆Efficient Online Proportional Sampling with Applications to Smoothed Online Learning7h◆CDFM: Towards a General-Purpose Causal Discovery Foundation Model7h◆
News/KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling
arxiv
PublishedJuly 13, 2026 at 4:00 AM
—neutral

KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling

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

arXiv:2607.09153v1 Announce Type: new Abstract: Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent systems. However, existing PRMs are text-based: they re-encode the enti

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
arxivFAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks7harxivRL Forgets! Towards Continual Policy Optimization7harxivLearnable Mixed Nash Equilibria are Collectively Rational7harxivOracle-Efficient Combinatorial Semi-Bandits7h
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