Developer Tools B

Showing 121–150 of 430
  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
    Inference Neural Network
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Training & Fine-tuning
    Cybersecurity Detection Classification with Reasoning-enabled Language Models
    Reinforcement Learning Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Beyond a Single Judge: Simulating Social Persona Panels for Generative UI Evaluation
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory
    Deep Learning Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Metaphor Tracer: A Theory-Informed Analysis of Hidden States
    Meta Neural Network
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification
    Embeddings Neural Network Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    QAdapt: A Noise-Adaptive Neural Pre-Decoding Framework for Quantum Error Correction
    Deep Learning Fine-tuning Google
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
    Deep Learning Inference Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Safety & Evaluation
    QQWorld: Quantile-Quantile Matching for World Model Regularization
    Deep Learning Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • NVIDIA Developer Blog · EN Infrastructure & Hardware extract
    NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure
    NVIDIA shares Exemplar Cloud lessons for unlocking AI infra performance
    NVIDIA
    NVIDIA shared lessons from its Exemplar Cloud, noting that two clusters built from identical H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different performance. The guidance focuses on tuning and operations to unlock full AI infrastructure performance.
    Read original (NVIDIA Developer Blog) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Windowed thinning and query complexity for the bouncy particle and Zigzag samplers
    Neural Network
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Can Large Language Models Execute Parent Orders?
    Deep Learning Neural Network Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Hierarchical Multilevel Monte Carlo for Order-Optimal Neural Actor-Critic in Average-Reward CMDPs
    AI Agents Machine Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferences
    Neural Network
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Infrastructure & Hardware
    How Benchmarks Mis-Score Computer-Use Agents
    AI Agents Neural Network
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow
    Fine-tuning Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Teffic-Audio: Tell Fact from Fiction
    Neural Network Speech Processing
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    LLMs struggle to simulate human belief updates in controlled environments
    GPT
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Reflected diffusion, no-flux continuity equations and confined Lagrangian flows in bounded domains
    Neural Network
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • 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)) ↗
  • Anthropic News · EN Safety & Evaluation extract
    Investigating three real-world incidents in our cybersecurity evaluations
    Anthropic's Frontier Red Team probes three cybersecurity-eval incidents
    Claude Machine Learning OpenAI Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Anthropic's Frontier Red Team published a review of three real-world incidents tied to its cybersecurity evaluations. The investigation examines potential misuse and the validity of its evaluation methods to strengthen the safety of frontier models.
    Read original (Anthropic News) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
    AI Agents
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Measuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index
    Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • 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
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
    AI Agents Deep Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
    DeepSeek Mistral Mixture of Experts (MoE) Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    From Textual Requirements to Microservice Architectures - A Comprehensive Evaluation of LLM-Based Design Synthesis
    OpenAI Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗