·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Startup Battlefield 200 applications officially close in 3 days2h◆Google will pay SpaceX $920M per month for compute3h◆The most interesting startups right now want to get you off your phone5h◆This is your laptop… on AI6h◆New York lawmakers pass one-year ban on new data centers7h◆The token bill comes due: Inside the industry scramble to manage AI’s runaway costs8h◆The latest AI news we announced in May 20268h◆The ‘together tech’ wave might be the most intriguing startup bet of 20268h◆This AI startup says it can tell if a script will make a hit film8h◆AirTrunk commits $30B to build 5GW of AI data centers in India9h◆The Meta hack shows there’s more to AI security than Mythos13h◆Mira Murati steps back into the spotlight, carefully17h◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning18h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning18h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models18h◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents18h◆Why Muon Outperforms Adam: A Curvature Perspective18h◆Vision Hopfield Memory Networks18h◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies18h◆FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment18h◆Startup Battlefield 200 applications officially close in 3 days2h◆Google will pay SpaceX $920M per month for compute3h◆The most interesting startups right now want to get you off your phone5h◆This is your laptop… on AI6h◆New York lawmakers pass one-year ban on new data centers7h◆The token bill comes due: Inside the industry scramble to manage AI’s runaway costs8h◆The latest AI news we announced in May 20268h◆The ‘together tech’ wave might be the most intriguing startup bet of 20268h◆This AI startup says it can tell if a script will make a hit film8h◆AirTrunk commits $30B to build 5GW of AI data centers in India9h◆The Meta hack shows there’s more to AI security than Mythos13h◆Mira Murati steps back into the spotlight, carefully17h◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning18h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning18h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models18h◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents18h◆Why Muon Outperforms Adam: A Curvature Perspective18h◆Vision Hopfield Memory Networks18h◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies18h◆FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment18h◆
Tag

#attention

3 articles tagged #attention

arxivMay 16bullish

Krause Synchronization Transformers

arXiv:2602.11534v3 Announce Type: replace-cross Abstract: Self-attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When composed across depth, this interaction pattern induces strong synchronization dynamics that favor

MEQWVI3 models#transformers#attention#efficiencyRead 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 →
arxivMay 14bullish

ASAP: Amortized Doubly-Stochastic Attention via Sliced Dual Projection

arXiv:2605.12879v1 Announce Type: new Abstract: Doubly-stochastic attention has emerged as a transport-based alternative to row-softmax attention, with recent Transformer variants using it to reduce attention sinks and rank collapse while improving performance. In this family, the standard approach

SIAS2 models#transformer#attention#machine-learningRead on arxiv →
HomeModelsNews