Developer Tools B

Showing 391–420 of 430
  • arXiv cs.LG (Machine Learning) · EN Safety & Evaluation
    Emergent Latent-State Computation under Stochastic Volatility
    Transformer
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Memory for Large Language Models
    Neural Network Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Safety & Evaluation
    Inspect India Evals: An Open Benchmarking Framework for Evaluating Large Language Models in the Indian Linguistic and Cultural Context
    Machine Learning Meta
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines
    Machine Learning Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management
    Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Multimodal
    Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control
    Embeddings Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors
    Inference
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Toward a systematic method for identifying language areas
    Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Inference & Efficiency
    CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention
    Inference Neural Network
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    VisualPatchWorld: Code World Models as Latent Structured Representations for Planning
    Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization
    Neural Network Retrieval-Augmented Generation (RAG) Reinforcement Learning
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  • Apple Machine Learning Research · EN Inference & Efficiency extract
    Memory Efficient Audio Synthesis with Decoupled Temporal Depth Diffusion Transformers
    Apple details memory-efficient on-device audio synthesis for Siri voices
    Quantization Speech Processing Transformer
    Apple ML published the memory-efficient audio synthesis architecture behind Siri Expressive Voices, which generate configurable speech in real time entirely on device. Powered by its AFM 3 Core Advanced on-device foundation model, a detokenizer converts semantic audio tokens into high-fidelity audio within the Apple Matrix Coprocessor (AMX) budget using a residual vector quantization (RVQ), streaming design. Details past the streaming component are truncated in the excerpt.
    Read original (Apple Machine Learning Research) ↗
  • Simon Willison's Weblog · EN New Model Releases extract
    An opinionated guide to which AI to use to do stuff
    Simon Willison on Ethan Mollick's updated guide to which AI to use
    AI Agents Claude Gemini Google GPT
    Simon Willison links to Ethan Mollick's evolving guide on which AI to use for tasks. A year ago it centered on chat (ChatGPT, Claude, Gemini); today it emphasizes agentic systems doing the equivalent of hours of human work. Gemini has dropped off Ethan's list, while modes such as ChatGPT Work/Codex and Claude Cowork/Code are explained. Note: the excerpt is truncated at the end, so later details could not be verified.
    Read original (Simon Willison's Weblog) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Towards Robust Reinforcement Learning for Small-Scale Language Model Agents
    AI Agents Fine-tuning Neural Network Reinforcement Learning
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  • arXiv cs.CL (Computation and Language) · EN Agents & Tool Use
    Addressable Recall Compaction for Long Context-Window Control in AI Agents
    AI Agents Deep Learning Retrieval-Augmented Generation (RAG) Reinforcement Learning Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models
    Reinforcement Learning
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  • Anthropic News · EN Developer Tools extract
    Our position on open-weights models
    Anthropic's Dario Amodei rejects banning Chinese open-weights models
    Anthropic Neural Network Reinforcement Learning
    In a blog post, Anthropic CEO Dario Amodei stated his position on open-weights models amid debate over Chinese ones. He cites reports that US officials may bar US firms from using Chinese open-weights models, a tech-industry letter backing open weights, and accusations that Anthropic wants a ban to protect its business. He says plainly he does not regard such bans as useful. Full text was only partially retrieved, so further details are unconfirmed.
    Read original (Anthropic News) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
    Gemini GPT Machine Learning Neural Network Software Engineering
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  • arXiv cs.LG (Machine Learning) · EN Infrastructure & Hardware
    Certified Parallel-in-Time Sinkhorn for Dynamic Entropic Optimal Transport
    Deep Learning Neural Network
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  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Learning Distributions from Multiple Data Providers
    Neural Network Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
    Computer Vision Health & Bio Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Global Convergence of DGM and PINN Algorithms for Solving Nonlinear PDEs
    Algorithms & Theory Deep Learning Machine Learning Neural Network
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  • arXiv cs.AI (Artificial Intelligence) · EN Funding & M&A
    The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation
    AI Agents Neural Network Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Training & Fine-tuning
    DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
    Machine Learning Neural Network Retrieval-Augmented Generation (RAG)
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  • IEEE Spectrum (AI section) · EN Developer Tools extract
    Why AI-Driven Cognitive Systems Are Redefining Radar and Electronic Warfare
    AI/ML cognitive radar and EW adapt to mode-agile threats
    Algorithms & Theory Machine Learning Neural Network Reinforcement Learning
    An IEEE Spectrum overview argues mode-agile emitters that shift frequencies, modulation, and hopping defeat static, library-based radar/EW systems. It outlines how AI/ML methods (ANN, DNN, fuzzy logic) enable adaptive, real-time cognitive radar/EW. Framed as a webinar preview, so implementation specifics are unconfirmed.
    Read original (IEEE Spectrum (AI section)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Multimodal
    ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
    Computer Vision Neural Network Reinforcement Learning
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  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines
    Deep Learning Inference Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Beyond Scale and Generation: Understanding Language Model-based Entity Matching
    Embeddings Fine-tuning Neural Network Reinforcement Learning
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  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Stacking the Deck: Tunable Trainability in Stacked LCUs
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  • arXiv cs.AI (Artificial Intelligence) · EN New Model Releases
    Co-Learning for Missing Arbitrary Modalities in Multi-modal Classification
    Deep Learning Inference Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗