今日必看

AI Agent Whistleblower Hotline – Ryan Greenblatt

Did your AI agent get the memo, or just the blame?

Tag index for Org Mode blog

Writing More Secure Code with LLMs: Why "Make No Mistakes" Falls Short

Secure Go Code Using the Principle of Least Privilege

How to use open models with Claude Code

Garbage Trucks Now Have AI Cameras to Score Your House and Clock Code Violations

Google copied our open-source code, removed engineers' names without credit

Qector-mega 2.2.1 – Sub-ms QEC decoding on a 2-core CPU with zero GPU needs

Does Code Matter Anymore?

用 GPT 生图再调 Hyper3D MCP 图生模型

📌 一句话摘要 该条补充具体做法：先用 GPT 生图，再调用 Hyper3D MCP 依据图片生成 3D 模型，从而无需自己寻找或制作 3D 素材。 📝 详细摘要 该条补充上一条的具体操作路径：如果让 GPT-6 调用 Hyper3D MCP 创建所需 3D 资产，可以先使用 GPT 生图，然后调用 Hyper3D MCP 依据这张图生成模型，这样就不再需要自己寻找或制作 3D 素材。 💡 主要观点 图生模型可替代人工寻找 3D 素材 先用 GPT 生成图片，再调用 Hyper3D MCP 依据图片生成 3D 模型，把素材准备环节交给模型完成。 📊 文章

How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories

For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster must synchronize gradients across thousands of collective operations per second. Similarly, during inference, unplanned do

Assessing the Applicability of Existing Design Recommendations to AI Companion Design: A Multi-Method Study

arXiv:2609.14236v1 Announce Type: cross Abstract: With the rapid proliferation of large language model (LLM)-based systems, AI companions have emerged as conversational agents designed to cultivate emotional connection rather than primarily to support humans in instrumental tasks

Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting

arXiv:2609.15523v1 Announce Type: cross Abstract: Logic attack graphs grounded in scanner output provide explicit and auditable attack path reasoning LLM-based agents lack. Integrating symbolic frameworks such as MulVAL to contemporary security workflows or agentic pipelines, how

Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework

arXiv:2609.13335v1 Announce Type: cross Abstract: Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models often exhibit unstable long-horizon reasoning and inefficient action execution when deployed in agentic robotic frameworks. Thi

CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization

arXiv:2609.15664v1 Announce Type: cross Abstract: Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap for practical LLM deployment. However, existing cloud-edge paradigms struggle to

Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

arXiv:2609.13559v1 Announce Type: new Abstract: Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments typically route inferences to powerful cloud-based models, incurring significant

Carryover Drafting: Recycling Rejected States for Speculative Decoding

arXiv:2609.14717v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept several tokens. By construction, verification computes representations for both accep

ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution

arXiv:2609.14211v1 Announce Type: cross Abstract: Scientific applications increasingly rely on high-performance computing (HPC), yet translating a scientist's high-level goal into a correct target-scale execution remains brittle and labor-intensive. Large language model (LLM) age

EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse

arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strategies and lack persistent clinical memory, preventing self-evolving from prior dia

Grounded Adjudication of Variations across Extracted TimeLines (GAVEL): Comparing Clinical Timelines Against Their Case Reports

arXiv:2609.13475v1 Announce Type: new Abstract: Existing pipelines for clinical timeline extraction from case reports are evaluated using an expert reference and are limited by imperfect reference annotations and imprecise event alignment. We developed GAVEL, an LLM judge protoco

K-Bench: A Benchmark for LLM Unlearning in Agentic Deployments

arXiv:2609.12808v2 Announce Type: replace Abstract: Unlearning benchmarks such as TOFU and MUSE certify forgetting by reading the model's final answer, where a model that refuses to answer already counts as having forgotten. We show that this model-level certificate does not tran

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

arXiv:2609.15855v1 Announce Type: cross Abstract: % !TEX root = ../main.tex People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk conversations remains poorly characterised. We developed K-Bench, a clinician-calibr

AI for everyone in every language

We’re moving beyond traditional text translation to build models that understand the world’s rich, living languages exactly as they are expressed.