发布日期：2026-03-21
收录条目：20
1. NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities
- 来源：MarkTechPost
- 发布时间：2026-03-20 22:38 UTC
- 链接：https://www.marktechpost.com/2026/03/20/nvidia-releases-nemotron-cascade-2-an-open-30b-moe-with-3b-active-parameters-delivering-better-reasoning-and-strong-agentic-capabilities/
摘要：NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasonin
2. Trump takes another shot at dismantling state AI regulation
- 来源：The Verge AI
- 发布时间：2026-03-20 18:17 UTC
- 链接：https://www.theverge.com/ai-artificial-intelligence/898055/trump-new-ai-policy-framework
摘要：The Trump administration on Friday unveiled its new legislative blueprint for AI regulation, and the seven-point plan includes a clear message: The federal government should avoid many AI regulations beyond a set of chil
3. A Coding Implementation Showcasing ClawTeam’s Multi-Agent Swarm Orchestration with OpenAI Function Calling
- 来源：MarkTechPost
- 发布时间：2026-03-20 18:09 UTC
- 链接：https://www.marktechpost.com/2026/03/20/a-coding-implementation-showcasing-clawteams-multi-agent-swarm-orchestration-with-openai-function-calling/
摘要：In this comprehensive tutorial, we present the core architecture of ClawTeam, an open-source Agent Swarm Intelligence framework developed by HKUDS. We implement the fundamental concepts that make ClawTeam powerful: a lea
4. Google Search is now using AI to replace headlines
- 来源：The Verge AI
- 发布时间：2026-03-20 14:30 UTC
- 链接：https://www.theverge.com/tech/896490/google-replace-news-headlines-in-search-canary-coal-mine-experiment
摘要：Since roughly the turn of the millennium, Google Search has been the bedrock of the web. People loved Google's trustworthy "10 blue links" search experience and its unspoken promise: The website you click is the website
5. Amazon is making an Alexa phone
- 来源：The Verge AI
- 发布时间：2026-03-20 13:42 UTC
- 链接：https://www.theverge.com/tech/897915/amazon-transformer-alexa-phone
摘要：Over 10 years after shelving the Fire Phone, Amazon is reportedly planning to launch another smartphone, this time focused on Alexa. According to Reuters, the phone, which is code-named "Transformer," will center around
6. Why people really hate AI
- 来源：The Verge AI
- 发布时间：2026-03-20 13:27 UTC
- 链接：https://www.theverge.com/podcast/897900/ai-trust-gap-killer-app-vergecast
摘要：There's a big, and increasing, disconnect in culture right now when it comes to artificial intelligence. Companies of all shapes and sizes are hunting for places to deploy AI and can't stop talking about how this new tec
7. LlamaIndex Releases LiteParse: A CLI and TypeScript-Native Library for Spatial PDF Parsing in AI Agent Workflows
- 来源：MarkTechPost
- 发布时间：2026-03-20 06:43 UTC
- 链接：https://www.marktechpost.com/2026/03/19/llamaindex-releases-liteparse-a-cli-and-typescript-native-library-for-spatial-pdf-parsing-in-ai-agent-workflows/
摘要：In the current landscape of Retrieval-Augmented Generation (RAG), the primary bottleneck for developers is no longer the large language model (LLM) itself, but the data ingestion pipeline. For software developers, conver
8. DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18048
摘要：arXiv:2603.18048v1 Announce Type: new Abstract: Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely p
9. Continually self-improving AI
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18073
摘要：arXiv:2603.18073v1 Announce Type: new Abstract: Modern language model-based AI systems are remarkably powerful, yet their capabilities remain fundamentally capped by their human creators in three key ways. First, althoug
10. Multi-Trait Subspace Steering to Reveal the Dark Side of Human-AI Interaction
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18085
摘要：arXiv:2603.18085v1 Announce Type: new Abstract: Recent incidents have highlighted alarming cases where human-AI interactions led to negative psychological outcomes, including mental health crises and even user harm. As L
11. Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18104
摘要：arXiv:2603.18104v1 Announce Type: new Abstract: Prevailing AI training infrastructure assumes reverse-mode automatic differentiation over IEEE-754 arithmetic. The memory overhead of training relative to inference, optimi
12. Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18122
摘要：arXiv:2603.18122v1 Announce Type: new Abstract: Skele-Code is a natural-language and graph-based interface for building workflows with AI agents, designed especially for less or non-technical users. It supports increment
13. Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18166
摘要：arXiv:2603.18166v1 Announce Type: new Abstract: Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works address the problem by
14. TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18189
摘要：arXiv:2603.18189v1 Announce Type: new Abstract: Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic cha
15. Access Controlled Website Interaction for Agentic AI with Delegated Critical Tasks
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18197
摘要：arXiv:2603.18197v1 Announce Type: new Abstract: Recent studies reveal gaps in delegating critical tasks to agentic AI that accesses websites on the user's behalf, primarily due to limited access control mechanisms on web
16. A Computationally Efficient Learning of Artificial Intelligence System Reliability Considering Error Propagation
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18201
摘要：arXiv:2603.18201v1 Announce Type: new Abstract: Artificial Intelligence (AI) systems are increasingly prominent in emerging smart cities, yet their reliability remains a critical concern. These systems typically operate
17. Retrieval-Augmented LLM Agents: Learning to Learn from Experience
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18272
摘要：arXiv:2603.18272v1 Announce Type: new Abstract: While large language models (LLMs) have advanced the development of general-purpose agents, achieving robust generalization to unseen tasks remains a significant challenge.
18. EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18273
摘要：arXiv:2603.18273v1 Announce Type: new Abstract: In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educa
19. CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18290
摘要：arXiv:2603.18290v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -
20. The Validity Gap in Health AI Evaluation: A Cross-Sectional Analysis of Benchmark Composition
- 来源：arXiv cs.AI
- 发布时间：2026-03-20 04:00 UTC
- 链接：https://arxiv.org/abs/2603.18294
摘要：arXiv:2603.18294v1 Announce Type: new Abstract: Background: Clinical trials rely on transparent inclusion criteria to ensure generalizability. In contrast, benchmarks validating health-related large language models (LLMs