Funding & M&A C

Showing 31–60 of 138
  • arXiv cs.CL (Computation and Language) · EN Multimodal
    Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
    AI Agents Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Multimodal
    ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
    Computer Vision
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
    Meta Neural Network Reinforcement Learning
    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.AI (Artificial Intelligence) · EN New Model Releases
    A foundation model of numerical intelligence with cross-disciplinary generalization
    AI Agents
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features
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  • Data Center Dynamics · EN Funding & M&A extract
    Connected Infra Group CEO Borghei eyes tower M&A opportunities amid AI boom
    Connected Infra Group CEO eyes tower M&A as AI boom lifts sector
    Neural Network Retrieval-Augmented Generation (RAG)
    Connected Infra Group CEO Borghei is eyeing M&A opportunities in the telecom tower industry amid the AI boom, DatacenterDynamics reported. He said spectrum auctions are offering encouragement to the tower sector, signaling appetite for consolidation.
    Read original (Data Center Dynamics) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Windowed thinning and query complexity for the bouncy particle and Zigzag samplers
    Neural Network
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  • Data Center Dynamics · EN Funding & M&A extract
    ChipAgents raises additional $60m in expanded Series A round to support AI chip design platform
    ChipAgents raises additional $60m in expanded Series A for AI chip design
    AI Agents
    AI chip-design startup ChipAgents raised an additional $60m in an expanded Series A round, DatacenterDynamics reported. The company claims more than 120 chip firms have already deployed its platform, underscoring demand for AI-assisted semiconductor design.
    Read original (Data Center Dynamics) ↗
  • 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 Funding & M&A
    Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
    Inference Llama Machine Learning Meta
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • 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)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
    Meta Neural Network Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • Data Center Dynamics · EN Infrastructure & Hardware extract
    Vertiv's earnings and revenues grow, shares fall
    Vertiv grows earnings and revenue, but shares fall
    Vertiv reported higher earnings and revenue, yet its shares fell, DatacenterDynamics reported. The decline comes as AI data center investment grows increasingly volatile, with investors wary of uncertainty despite the power and cooling supplier's solid results.
    Read original (Data Center Dynamics) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Safety & Evaluation
    Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation
    Neural Network Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Causal Discovery with Inverted Self-attention for Multivariate Time Series
    Retrieval-Augmented Generation (RAG) Transformer
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Vibe-FDTR: An agent-oriented framework for reproducible frequency-domain thermoreflectance data analysis
    AI Agents Neural Network
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • IEEE Spectrum (AI section) · EN Inference & Efficiency extract
    Are AI Models Working Harder Than They Need to?
    Are AI models working harder than they need to?
    Deep Learning Google Inference Neural Network Software Engineering
    IEEE Spectrum examines how much of modern AI relies on massive amounts of multiplication. Questioning whether the neural networks behind everything from generated answers to photo organization really need all that computation, the piece explores the potential to make AI inference far more efficient.
    Read original (IEEE Spectrum (AI section)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Search Strategies for Optimal Classification and Regression Trees
    Machine Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Rethinking LLM-Judged Helpfulness as a Pedagogy Signal: A Pre-Registered Audit Across Tutor Models
    Claude GPT Reinforcement Learning Software Engineering
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Industry Adoption
    Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging
    Algorithms & Theory Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    PCAP-LM: An LLM-Native Text Representation for TLS Bulk Traffic Analysis
    Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction
    Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
    AI Agents Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Agents & Tool Use
    ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents
    AI Agents Reinforcement Learning
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  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Funding & M&A
    RepBench: Compiling Benchmarks into Capability Representations for Large Language Models
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
    Computer Vision Reinforcement Learning
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
    Read original (arXiv cs.LG (Machine Learning)) ↗