发布日期：2026-05-23
收录条目：20
1. Google’s AI search is so broken it can ‘disregard’ what you’re looking for
- 来源：The Verge AI
- 发布时间：2026-05-22 20:39 UTC
- 链接：https://www.theverge.com/tech/936176/google-ai-overviews-search-disregard
摘要：Google's AI Overviews are running into an interesting problem right now. Earlier on Friday, if you searched for the term "disregard," the AI Overview section would include a response like what you'd see from a more tradi
2. A Step-by-Step Coding Tutorial to Implement GBrain: The Self-Wiring Memory Layer Built by Y Combinator’s Garry Tan for AI Agents
- 来源：MarkTechPost
- 发布时间：2026-05-22 18:23 UTC
- 链接：https://www.marktechpost.com/2026/05/22/a-step-by-step-coding-tutorial-to-implement-gbrain-the-self-wiring-memory-layer-built-by-y-combinators-garry-tan-for-ai-agents/
摘要：AI agents start every session from zero — no memory of meetings, notes, or decisions. GBrain, the open-source memory layer Y Combinator's Garry Tan built to power his own OpenClaw and Hermes deployments, fixes that with
3. Catch up on the Dialogues stage at Google I/O 2026.
- 来源：Google AI Blog
- 发布时间：2026-05-22 18:00 UTC
- 链接：https://blog.google/innovation-and-ai/technology/ai/io-2026-dialogues-recap/
摘要：A recap of the 2026 I/O Dialogues, where leaders discuss the future of AI, quantum computing, robotics and creativity.
4. Elon, stop trying to make Grok happen
- 来源：The Verge AI
- 发布时间：2026-05-22 17:17 UTC
- 链接：https://www.theverge.com/ai-artificial-intelligence/936219/elon-stop-trying-to-make-grok-happen
摘要：There is a harsh truth about Elon Musk's "truth-seeking" AI chatbot Grok: It's not very good, and not many people are using it. That's the takeaway of a new Reuters report, which found that Grok barely appears in federal
5. The literary world isn’t prepared for AI
- 来源：The Verge AI
- 发布时间：2026-05-22 14:30 UTC
- 链接：https://www.theverge.com/tech/936073/ai-writing-granta-commonwealth-prize
摘要：Since 2012, the British literary magazine Granta has published the regional winners of the annual Commonwealth Short Story Prize. This year, however, there was something off about one of the selections for the prestigiou
6. Spotify says its AI remix tool is for superfans, but I’m not convinced
- 来源：The Verge AI
- 发布时间：2026-05-22 14:20 UTC
- 链接：https://www.theverge.com/ai-artificial-intelligence/936072/spotify-umg-ai-music-remix-cover-superfan
摘要：AI covers and remixes of songs are already a blight on the internet. Spotify, YouTube, TikTok, and Instagram are awash in flat reggae versions of "Smells Like Teen Spirit," dinky country renditions of The Weeknd, and mon
7. Samsung’s memory chip employees negotiated $340,000 bonuses this year
- 来源：The Verge AI
- 发布时间：2026-05-22 11:05 UTC
- 链接：https://www.theverge.com/tech/936002/samsung-memory-chip-employees-deal-strike-bonus
摘要：Details have emerged about a tentative deal struck between Samsung and semiconductor employees who had threatened to strike. The deal reportedly makes some workers eligible for average annual bonuses of $340,000. The pro
8. Microsoft Releases Fara1.5: A Family of Browser Computer-Use Agents (4B/9B/27B) That Outperform OpenAI Operator and Gemini 2.5 Computer Use on Online-Mind2Web
- 来源：MarkTechPost
- 发布时间：2026-05-22 08:32 UTC
- 链接：https://www.marktechpost.com/2026/05/22/microsoft-releases-fara1-5-a-family-of-browser-computer-use-agents-4b-9b-27b-that-outperform-openai-operator-and-gemini-2-5-computer-use-on-online-mind2web/
摘要：Microsoft Research released Fara1.5, a family of browser computer-use agents in 4B, 9B, and 27B sizes. Fara1.5-27B scores 72% on Online-Mind2Web, outperforming OpenAI Operator, Gemini 2.5 Computer Use, and Yutori Navigat
9. Build Recurrent-Depth Transformers with OpenMythos for MLA, GQA, Sparse MoE, and Loop-Scaled Reasoning
- 来源：MarkTechPost
- 发布时间：2026-05-22 07:39 UTC
- 链接：https://www.marktechpost.com/2026/05/22/build-recurrent-depth-transformers-with-openmythos-for-mla-gqa-sparse-moe-and-loop-scaled-reasoning/
摘要：In this tutorial, we explore OpenMythos by building an advanced recurrent-depth transformer workflow that runs end-to-end in Google Colab. We create both MLA and GQA model variants, compare their parameter counts, and ch
10. SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20189
摘要：arXiv:2605.20189v1 Announce Type: new Abstract: Despite the remarkable success of large language models (LLMs), they still face bottlenecks while deploying in dynamic, real-world settings with primary challenges being co
11. Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20190
摘要：arXiv:2605.20190v1 Announce Type: new Abstract: Iterative industrial design-simulation optimization is bottlenecked by the CAD-CAE semantic gap: translating simulation feedback into valid geometric edits under diverse, c
12. OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20423
摘要：arXiv:2605.20423v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks,
13. AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20425
摘要：arXiv:2605.20425v1 Announce Type: new Abstract: Designing multi-agent workflows is especially difficult in open-ended scientific settings where tasks lack curated training sets, reliable scalar evaluation metrics, and st
14. High Quality Embeddings for Horn Logic Reasoning
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20467
摘要：arXiv:2605.20467v1 Announce Type: new Abstract: Neural networks can be trained to rank the choices made by logical reasoners, resulting in more efficient searches for answers. A key step in this process is creating usefu
15. $ECUAS_n$: A family of metrics for principled evaluation of uncertainty-augmented systems
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20490
摘要：arXiv:2605.20490v2 Announce Type: new Abstract: In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems -- to accept or reject predictions
16. Open-World Evaluations for Measuring Frontier AI Capabilities
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20520
摘要：arXiv:2605.20520v1 Announce Type: new Abstract: Benchmark-based evaluation remains important for tracking frontier AI progress. But it can both overstate and understate deployed capability because it privileges tasks tha
17. AgentAtlas: Beyond Outcome Leaderboards for LLM Agents
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20530
摘要：arXiv:2605.20530v1 Announce Type: new Abstract: Large language model agents now act on codebases, browsers, operating systems, calendars, files, and tool ecosystems, but the benchmarks used to evaluate them are fragmente
18. Personality Engineering with AI Agents: A New Methodology for Negotiation Research
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20554
摘要：arXiv:2605.20554v1 Announce Type: new Abstract: According to canonical negotiation theory, people's success in a negotiation depends on how well they balance competing demands--empathizing and asserting, demonstrating co
19. Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20577
摘要：arXiv:2605.20577v1 Announce Type: new Abstract: Riichi Mahjong is a multi-player, imperfect-information game characterized by stochasticity and high-dimensional state spaces. These attributes present a unique combination
20. From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)
- 来源：arXiv cs.AI
- 发布时间：2026-05-22 04:00 UTC
- 链接：https://arxiv.org/abs/2605.20608
摘要：arXiv:2605.20608v1 Announce Type: new Abstract: Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cog