Multimodal A

Showing 31–60 of 124
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation
    Neural Network Retrieval-Augmented Generation (RAG)
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  • arXiv cs.LG (Machine Learning) · EN Multimodal
    ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
    Computer Vision
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  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
    Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports
    Computer Vision Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence
    Inference Neural Network Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees
    AI Agents Deep Learning Neural Network Reinforcement Learning from Human Feedback (RLHF) Software Engineering
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection
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  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
    AI Agents Neural Network Reinforcement Learning Software Engineering
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Correcting What You Cannot See: Credit Assignment for Perception Distillation in Multimodal Reasoners
    Neural Network Retrieval-Augmented Generation (RAG) Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • Google DeepMind Blog · EN Multimodal extract
    Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration
    DeepMind's Gemini Robotics ER 2 adds video understanding, multi-robot teamwork
    Gemini Reinforcement Learning Robotics
    DeepMind introduced Gemini Robotics ER 2, which helps robots reason, collaborate, and solve real-world tasks. The company calls it a step change in video understanding, task orchestration, and multi-robot collaboration for embodied AI.
    Read original (Google DeepMind Blog) ↗
  • Sakana AI Blog (ja) · EN Developer Tools extract
    From Japan, Products the World Will Use: An Interview with Sakana AI's Head of Product Development
    Interview: Sakana AI's product chief on Japan-born global products
    Neural Network Reinforcement Learning
    An interview with Sakana AI's Head of Product Development on building products from Japan that the world will use. The Q&A covers the company's product philosophy and ambitions, offering a look at the strategy of a leading Japanese AI startup.
    Read original (Sakana AI Blog (ja)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?
    Machine Learning Neural Network Software Engineering
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    ObjectStream: Latent Objects as Memory Anchors for Streaming Video Understanding
    Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    Theia: Large-Scale Multimodal Captioning and Automated Validation of the Incidents1M Dataset for Data-Free Distillation
    Computer Vision Mixture of Experts (MoE) Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory
    Deep Learning Machine Learning NVIDIA Software Engineering Transformer
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents
    AI Agents Gemini GPT Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Old Tricks, New Models: How Simple Image Transformations Break Modern AI-based Content Moderation
    Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    AgenticASR: Refining Speech Recognition in Real-World Scenarios via an Agentic Approach
    Deep Learning Inference Neural Network Reinforcement Learning Speech Processing
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Where and When to Commit: Candidate-Aware Decoding for Diffusion Language Models
    Computer Vision Deep Learning Reinforcement Learning Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    RRM: Experience-Driven Reflective Retrieval Memory for Long-Horizon Multimodal Reasoning
    AI Agents Deep Learning Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    OPLD: On-Policy Latent Distillation for Multimodal Reasoning
    Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Group-Reflective Self-Distillation for Agentic Reinforcement Learning
    AI Agents Reinforcement Learning
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  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    Flux-OPD: On-Policy Distillation with Evolving Contexts
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  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    TAPO: Transition-Aware Policy Optimization for LLM Agents
    AI Agents Algorithms & Theory Inference Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    AutoSupervision: Closing the Feedback Loop in Scientific Workflows with Grounded Revision Verification
    GPT Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Semantic-Aligned Structural Abstraction for Multimodal Sentiment Analysis
    Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Infrastructure & Hardware
    Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning
    Inference Llama
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Multimodal
    Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities
    Neural Network Reinforcement Learning Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Multimodal
    DualAnchor: Preserving Language Priors and Improving Lexical Fidelity in Gloss-Free Sign Language Translation
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • Hacker News (Front Page) · EN Multimodal extract
    The coolest use for the Vision Pro
    Apollo dev Christian Selig shares his favorite use for the Vision Pro
    iOS developer Christian Selig (creator of Apollo for Reddit) blogs about what he calls the coolest use for Apple's Vision Pro. The URL slug (vision-pro-house) suggests a home- or spatial-visualization use case, but the raw excerpt is empty, so the specific application, steps, and features are unconfirmed from the source text.
    Read original (Hacker News (Front Page)) ↗