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Showing 151–180 of 326
  • arXiv cs.AI (Artificial Intelligence) · EN Developer Tools
    Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Work
    GPT Meta Neural Network Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.AI (Artificial Intelligence) · EN Inference & Efficiency
    Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior
    Deep Learning Inference Neural Network
    Read original (arXiv cs.AI (Artificial Intelligence)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Repurposing Unified Topological Signatures for Graph Representation Learning
    Embeddings Neural Network Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Training & Fine-tuning
    Audio-Visual Turn-taking Prediction in Cocktail Party Scenarios
    Fine-tuning Reinforcement Learning Speech Processing
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Training & Fine-tuning
    CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework
    Fine-tuning Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    ThinkFlow: Self-Evolving Probabilistic Latent Memory for Lifelong Conversational Agents
    AI Agents Neural Network
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Can LLMs Follow the Pulse of a Crisis? Evaluating Crisis Sentiment in Bangladesh's July Uprising
    Deep Learning Neural Network Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress
    AI Agents Machine Learning Neural Network
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    The Role of Implicit and Explicit Demographic Signals in Large Language Model-based Student Assessment
    Meta Software Engineering
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  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Nameless Tokenization: A Lossless Tokenizer-Level Defense Against Control-Token Forgery in Open-Weight LLMs
    Neural Network
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
    Deep Learning Machine Learning Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    HUMAID-NER: A Disaster Tweet Dataset for Joint Named Entity Recognition and Event Classification via Uncertainty-Weighted Multitask Learning
    Neural Network Reinforcement Learning Transformer
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation
    Neural Network
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Inference & Efficiency
    MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillation
    Inference Reinforcement Learning Reinforcement Learning from Human Feedback (RLHF)
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    When Confidence Signals Disagree: Local and Global Confidence in Autoregressive Language Models
    Machine Learning Software Engineering
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN Developer Tools
    Causal Discovery via Transformed Low-Rank Quantile Surfaces
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.LG (Machine Learning) · EN New Model Releases
    Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
    Transformer
    Read original (arXiv cs.LG (Machine Learning)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Verbalizing Subliminal Learning Effects Using Text Optimization
    Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering over Scientific Literature
    Natural Language Processing (NLP) Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Policy & Regulation
    Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    Reduplicative constructions in Mandarin: Socio-emotional profiling through distributional semantics
    Embeddings Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Developer Tools
    A Data-free Universal Prior over Syntactic Structures
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    ImpossibleRubrics: Stress-Testing Generated Rubrics as Reward Signals
    Neural Network Reinforcement Learning Software Engineering
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN Safety & Evaluation
    Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion
    Retrieval-Augmented Generation (RAG)
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    TIAO: Token Importance-Aware Policy Optimization for Text Summarization
    GPT Retrieval-Augmented Generation (RAG) Reinforcement Learning
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • arXiv cs.CL (Computation and Language) · EN New Model Releases
    Japanese Stroke LLM Evaluation: A Conversational Benchmark for Safe Stroke Care in Japanese Using Large Language Models
    Claude
    Read original (arXiv cs.CL (Computation and Language)) ↗
  • Lobste.rs (AI tagged) · EN Developer Tools
    Planning with Agents: Divided Worlds, Boundary Objects, and Thicker Interfaces
    GitHub Next argues agent planning needs thicker, visual interfaces
    AI Agents Neural Network Reinforcement Learning
    Maggie Appleton of GitHub Next published a talk on collaborative planning with agents. She argues today's CLI plan mode buries humans in walls of text with no way to edit or debate decisions, and calls for visual, interactive and multiplayer planning interfaces.
    Read original (Lobste.rs (AI tagged)) ↗
  • ITmedia AI+ · JA Developer Tools
    【動画あり】Excel仕事がラクになるCopilot活用術、実務ですぐ使える3選 Microsoft MVP直伝
    Microsoft MVP demos three practical Copilot tricks for Excel work
    Microsoft
    Microsoft MVP Kazuaki Asada walks through three ways Microsoft 365 Copilot can speed up everyday Excel work, with live demos. The piece focuses on how to phrase instructions and how to check the results, rather than on listing features.
    Read original (ITmedia AI+) ↗
  • Simon Willison's Weblog · EN Developer Tools
    What blog posts influenced your thinking the most?
    Simon Willison names the blog posts that shaped his thinking
    Retrieval-Augmented Generation (RAG) Reinforcement Learning Software Engineering
    In a Lobste.rs thread, Simon Willison lists the posts that shaped him: Joel Spolsky's Law of Leaky Abstractions, Will Larson's argument that migrations are the only scalable fix to tech debt, and Charity Majors' Engineer/Manager Pendulum, which gave him permission to move back to an IC role.
    Read original (Simon Willison's Weblog) ↗