·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
OpenAI says Hugging Face was breached by its own pre-release models11m◆The State of Simulation for Physical AI: An Overview1h◆Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents1h◆AI and the rise of the universal entertainment app1h◆Substack adds an AI detector to help spot blogs written by no one1h◆Data centers expected to use 4x more electricity by 20353h◆Google releases three new Gemini models — but no 3.5 Pro3h◆Introducing the ChatGPT for small business program4h◆Anthropic’s $1.5 billion book piracy settlement approved by judge4h◆US threatens sanctions against Chinese AI models over IP theft5h◆Google launches a cheaper alternative to large AI security models like Mythos6h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated7h◆Halliday’s latest smart glasses feature a much-improved display8h◆America needs to stop getting shocked by Chinese AI9h◆Advancing next-gen AI with materials science innovation10h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else11h◆OpenAI and Hugging Face partner to address security incident during model evaluation14h◆Capacity and Redundancy Trade-offs in Multi-Task Learning17h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation17h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making17h◆OpenAI says Hugging Face was breached by its own pre-release models11m◆The State of Simulation for Physical AI: An Overview1h◆Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents1h◆AI and the rise of the universal entertainment app1h◆Substack adds an AI detector to help spot blogs written by no one1h◆Data centers expected to use 4x more electricity by 20353h◆Google releases three new Gemini models — but no 3.5 Pro3h◆Introducing the ChatGPT for small business program4h◆Anthropic’s $1.5 billion book piracy settlement approved by judge4h◆US threatens sanctions against Chinese AI models over IP theft5h◆Google launches a cheaper alternative to large AI security models like Mythos6h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated7h◆Halliday’s latest smart glasses feature a much-improved display8h◆America needs to stop getting shocked by Chinese AI9h◆Advancing next-gen AI with materials science innovation10h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else11h◆OpenAI and Hugging Face partner to address security incident during model evaluation14h◆Capacity and Redundancy Trade-offs in Multi-Task Learning17h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation17h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making17h◆
Tag

#mitigation

3 articles tagged #mitigation

arxivMay 29

Finding DoRI: Discovery of Retained Images in Diffusion Models

arXiv:2507.16880v3 Announce Type: replace-cross Abstract: Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potential to inadvertently memorize and replicate training data. Rec

#mitigation#memorization#diffusion-modelsRead on arxiv →
arxivMay 16bullish

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks

arXiv:2510.19973v4 Announce Type: replace-cross Abstract: The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perceive and reason over the network environment as it is. This can be achieved through agentic AI, where

LA1B2 models#6g#network-autonomy#cognitive-biasRead on arxiv →
arxivMay 16bullish

MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs

arXiv:2605.14966v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have achieved remarkable performance across diverse multimodal tasks, yet they continue to suffer from hallucinations, generating content that is inconsistent with the visual input. Prior work DHCP (Detecting Hall

MHDH2 models#hallucination#mitigation#multimodalRead on arxiv →
HomeModelsNews