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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in...
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution. Although 85% of organizations...
arXiv:2605.20043v2 Announce Type: replace Abstract: We present an orthography-aware error analysis of Japanese past-tense morphological inflection, treating hiragana not merely...
arXiv:2605.23069v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their...
For years, authentication on the web followed one design assumption: a human sits behind a browser. Click a button. Fill...
The Model Context Protocol has moved from Anthropic’s internal experiment to a de facto industry standard at a speed few...
Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from...
Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from...
StepFun AI Releases Step-Audio 2 Mini: An Open-Source 8B Speech-to-Speech AI Model that Surpasses GPT-4o-Audio
STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models
Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models
Step by Step Guide to Build an End-to-End Model Optimization Pipeline with NVIDIA Model Optimizer Using FastNAS Pruning and Fine-Tuning
Steering Language Models with Weight Arithmetic
Static vs. Dynamic vs. Continuous Batching in LLM Inference
Static vs. Dynamic vs. Continuous Batching in LLM Inference
Stateful vs. Stateless Agent Design: Tradeoffs for Scalable Agentic Systems
Start Making Sense(s): A Developmental Probe of Attention Specialization Using Lexical Ambiguity
STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence
Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
