·
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/GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
arxiv
PublishedJune 16, 2026 at 4:00 AM

GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness

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

arXiv:2606.14865v1 Announce Type: cross Abstract: Adversarial Training (AT) improves neural network robustness, but most methods train a fixed parameter space from the start. This paper asks whether the order in which parameters become optimizable can affect the final robust solution, even when the

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