·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
L2G-SR: Learning to Guide LLM-based Symbolic Regression7h◆When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence7h◆ARIA: Runtime Inspection and Amendment for Multi-Agent Systems7h◆Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows7h◆CleanRM: Reward Modeling from Noisy Feedback with a Few Clean Feedback for RLHF7h◆CoTS: Competitive Token Selection for Latent Reasoning in Large Language Models7h◆Can Visual Generation Expand What Unified Multimodal Models Know?7h◆Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths7h◆Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization7h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science7h◆LLMR: Knowledge Distillation with a Large Language Model-Induced Reward7h◆PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design7h◆Training Reasoning Models to Generate Diverse Scientific Ideas7h◆OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling7h◆MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning7h◆Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth7h◆Sliced Wasserstein Sketches with Error Certificates7h◆ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training7h◆Approaching Backpropagation Accuracy with Weight Perturbation on MNIST7h◆Linear Independence of Polynomial Compositions with Bounded Input Degree7h◆L2G-SR: Learning to Guide LLM-based Symbolic Regression7h◆When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence7h◆ARIA: Runtime Inspection and Amendment for Multi-Agent Systems7h◆Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows7h◆CleanRM: Reward Modeling from Noisy Feedback with a Few Clean Feedback for RLHF7h◆CoTS: Competitive Token Selection for Latent Reasoning in Large Language Models7h◆Can Visual Generation Expand What Unified Multimodal Models Know?7h◆Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths7h◆Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization7h◆CompMat-Bench: Benchmarking AI Agents for Computational Materials Science7h◆LLMR: Knowledge Distillation with a Large Language Model-Induced Reward7h◆PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design7h◆Training Reasoning Models to Generate Diverse Scientific Ideas7h◆OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling7h◆MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning7h◆Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth7h◆Sliced Wasserstein Sketches with Error Certificates7h◆ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training7h◆Approaching Backpropagation Accuracy with Weight Perturbation on MNIST7h◆Linear Independence of Polynomial Compositions with Bounded Input Degree7h◆
News/OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
arxiv
PublishedSeptember 1, 2026 at 4:00 AM

OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models

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

arXiv:2603.23938v2 Announce Type: replace Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we introduce OmniACBench, a benchmark for evaluating context-grounded

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
arxivL2G-SR: Learning to Guide LLM-based Symbolic Regression7harxivWhen Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence7harxivARIA: Runtime Inspection and Amendment for Multi-Agent Systems7harxivKolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows7h
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