发布日期：2026-03-24
收录条目：20
1. Google’s new Pixel 10 ads made me go ‘Wait, WHAT are they trying to sell?’
- 来源：The Verge AI
- 发布时间：2026-03-23 19:49 UTC
- 链接：https://www.theverge.com/tech/898992/google-pixel-10-ads-moving-on-stalker-lying-beach-view-100-zoom
摘要：Ever watch a TV ad and wonder, "How did this get approved?" Today, Google has not one but two new ad spots for its six-month-old Pixel 10 phones, and… let's just say they may not come across as intended. First, there's "
2. Nvidia CEO Jensen Huang says ‘I think we’ve achieved AGI’
- 来源：The Verge AI
- 发布时间：2026-03-23 19:42 UTC
- 链接：https://www.theverge.com/ai-artificial-intelligence/899086/jensen-huang-nvidia-agi
摘要：On a Monday episode of the Lex Fridman podcast, Nvidia CEO Jensen Huang made a hot-button statement: "I think we've achieved AGI." AGI, or artificial general intelligence, is a vaguely defined term that has incited a lot
3. How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution
- 来源：MarkTechPost
- 发布时间：2026-03-23 18:33 UTC
- 链接：https://www.marktechpost.com/2026/03/23/how-to-design-a-production-ready-ai-agent-that-automates-google-colab-workflows-using-colab-mcp-mcp-tools-fastmcp-and-kernel-execution/
摘要：In this tutorial, we build an advanced, hands-on tutorial around Google’s newly released colab-mcp, an open-source MCP (Model Context Protocol) server that lets any AI agent programmatically control Google Colab notebook
4. How Reco transforms security alerts using Amazon Bedrock
- 来源：AWS ML Blog
- 发布时间：2026-03-23 16:46 UTC
- 链接：https://aws.amazon.com/blogs/machine-learning/how-reco-transforms-security-alerts-using-amazon-bedrock/
摘要：In this blog post, we show you how Reco implemented Amazon Bedrock to help transform security alerts and achieve significant improvements in incident response times.
5. Integrating Amazon Bedrock AgentCore with Slack
- 来源：AWS ML Blog
- 发布时间：2026-03-23 16:38 UTC
- 链接：https://aws.amazon.com/blogs/machine-learning/integrating-amazon-bedrock-agentcore-with-slack/
摘要：In this post, we demonstrate how to build a Slack integration using AWS Cloud Development Kit (AWS CDK). You will learn how to deploy the infrastructure with three specialized AWS Lambda functions, configure event subscr
6. Overcoming LLM hallucinations in regulated industries: Artificial Genius’s deterministic models on Amazon Nova
- 来源：AWS ML Blog
- 发布时间：2026-03-23 16:34 UTC
- 链接：https://aws.amazon.com/blogs/machine-learning/overcoming-llm-hallucinations-in-regulated-industries-artificial-geniuss-deterministic-models-on-amazon-nova/
摘要：In this post, we’re excited to showcase how AWS ISV Partner Artificial Genius is using Amazon SageMaker AI and Amazon Nova to deliver a solution that is probabilistic on input but deterministic on output, helping to enab
7. Confronting the CEO of the AI company that impersonated me
- 来源：The Verge AI
- 发布时间：2026-03-23 13:30 UTC
- 链接：https://www.theverge.com/podcast/898715/superhuman-grammarly-expert-review-shishir-mehrotra-interview-ai-impersonation
摘要：Today, I’m talking with Shishir Mehrotra, who is CEO of Superhuman — that’s the company formerly known as Grammarly, which is still its flagship product. Shishir also used to be the chief product officer at YouTube, and
8. When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19429
摘要：arXiv:2603.19429v1 Announce Type: new Abstract: Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these represe
9. Hyperagents
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19461
摘要：arXiv:2603.19461v1 Announce Type: new Abstract: Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-i
10. Teaching an Agent to Sketch One Part at a Time
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19500
摘要：arXiv:2603.19500v1 Announce Type: new Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward
11. Learning to Disprove: Formal Counterexample Generation with Large Language Models
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19514
摘要：arXiv:2603.19514v1 Announce Type: new Abstract: Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones
12. ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19515
摘要：arXiv:2603.19515v1 Announce Type: new Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on sp
13. PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19579
摘要：arXiv:2603.19579v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality
14. PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19584
摘要：arXiv:2603.19584v1 Announce Type: new Abstract: Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse-grained heuristics that ignore user ac
15. HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19639
摘要：arXiv:2603.19639v1 Announce Type: new Abstract: Although agentic workflows have demonstrated strong potential for solving complex tasks, existing automated generation methods remain inefficient and underperform, as they
16. A Subgoal-driven Framework for Improving Long-Horizon LLM Agents
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19685
摘要：arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile interfaces, operating systems, and web br
17. Stepwise: Neuro-Symbolic Proof Search for Automated Systems Verification
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19715
摘要：arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly
18. Embodied Science: Closing the Discovery Loop with Agentic Embodied AI
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19782
摘要：arXiv:2603.19782v1 Announce Type: new Abstract: Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pu
19. FormalEvolve: Neuro-Symbolic Evolutionary Search for Diverse and Prover-Effective Autoformalization
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19828
摘要：arXiv:2603.19828v1 Announce Type: new Abstract: Autoformalization aims to translate natural-language mathematics into compilable, machine-checkable statements. However, semantic consistency does not imply prover effectiv
20. Utility-Guided Agent Orchestration for Efficient LLM Tool Use
- 来源：arXiv cs.AI
- 发布时间：2026-03-23 04:00 UTC
- 链接：https://arxiv.org/abs/2603.19896
摘要：arXiv:2603.19896v1 Announce Type: new Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while fr