Pai 的 AI 雷达
今天这份简报把智能体开发的落地链路串起来：从多语言应用框架、韧性代理和电脑使用评测，到支撑高并发开发的 Git 基础设施，再延伸到堆叠式 PR、嵌入模型、企业知识、GPU 集群和一批可接入的工具与技能。阅读时可先用 featured 了解方向，再用 why_items 按“能接入什么、要验证什么、有哪些边界”筛选可执行动作。
arXiv:2609.33423v2 Announce Type: replace Abstract: Large Language Models (LLMs) can contribute useful engineering knowledge to robot design, but directly generated designs may rely on implicit assumptions and provide no guarantees of feasibility or optimality. These assumptions
arXiv:2610.06843v1 Announce Type: cross Abstract: LLM agents that orchestrate frozen vision-language-action (VLA) policies improve across episodes through text memory, which records what the agent did but not how the task is done. A demonstration video shows it, but fits poorly i
arXiv:2610.06207v1 Announce Type: new Abstract: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents are increasingly embedded in operational business processes. Yet Business Process Management (BPM) curricula and frameworks still largely treat artifi
arXiv:2610.04824v1 Announce Type: new Abstract: AI agents based on foundation models (FMs) have demonstrated strong capabilities to perform complex open-ended tasks. However, they face some common challenges in practice: (a) agent behavior can deviate drastically even for semanti
arXiv:2610.05586v1 Announce Type: cross Abstract: As Large Language Models (LLMs) gain tool use capabilities such as web search, they can retrieve and cross-reference public information, creating privacy risks beyond memorization. One manifestation is re-identification: linking a
arXiv:2610.06001v1 Announce Type: cross Abstract: AI agents built on large language models (LLMs) run shell commands, read and write files, and reach the network, typically with their user's privileges. However, what an agent does during an execution is difficult to understand: t
arXiv:2610.04168v1 Announce Type: new Abstract: Agentic large language model (LLM) systems are commonly implemented as an LLM in a loop with Planning, Memory, Tools, and Control Flow. This application-focused view connects agentic LLM research with deployable systems and leaves o
arXiv:2610.06522v1 Announce Type: cross Abstract: A model under pushback can correct itself, capitulate, or hold, and one flip rate counts a correction and a capitulation alike. Using SycoLens, a modular replay protocol, we test how user pressure and evaluation settings shape mea
arXiv:2610.04196v1 Announce Type: new Abstract: LLM-based long-horizon agentic post-training is often bottlenecked by rollout generation: trajectories span many interaction turns, completion times vary substantially, and synchronous update barriers leave faster workers waiting fo
arXiv:2608.18613v2 Announce Type: replace Abstract: Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at query time. The harness side of this shift has matured rapidly, but the corpus side has n
arXiv:2610.06597v1 Announce Type: new Abstract: LLM agents increasingly execute complex workflows involving multi-turn reasoning, tool use, and parallel agents. Efficient serving requires decisions that span two layers with complementary information: the agent harness understands
arXiv:2610.04921v1 Announce Type: cross Abstract: LLM-based agentic systems are emerging as a new software paradigm. Modern agents are typically composed of backbone LLMs and a surrounding harness that serves as the operational software infrastructure for agent execution. As agen
arXiv:2610.04542v1 Announce Type: new Abstract: Clinical LLMs must decide not only what diagnosis to produce, but also whether the available evidence is sufficient for autonomous decision making. Binary DECIDE/ABSTAIN formulations merge distinct non decision states and do not exp
arXiv:2610.06563v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly capable of acting in complex tool-use environments, yet they often fail to recognize when tasks are infeasible and no valid solution exists. Recent work has formalized this reliabil
arXiv:2610.05342v1 Announce Type: cross Abstract: Infrared small-target detection plays an important role in maritime monitoring and aerial surveillance. Although multimodal large language models (MLLMs) offer promising capabilities for visual understanding, existing MLLM-based a
arXiv:2610.04544v1 Announce Type: new Abstract: Many groups now delegate label ontology and metadata harmonization to agentic LLM pipelines. We built one and audited it. Our aggregate scores looked healthy, but the pipeline kept failing in ways they did not show, so we set out to
arXiv:2610.04169v1 Announce Type: cross Abstract: LLM fingerprinting via watermark distillation embeds a statistical watermark signal into model weights, enabling model owners to identify their models behind black-box APIs. Revisiting a recent protocol, we find that its utility e
arXiv:2610.05223v1 Announce Type: new Abstract: The architecture of modern LLMs consists of a profound cognitive polarization. LLMs possess implicit intuition encoded in their parameters, yet rely on a disconnected, explicit mechanism to access the outside world. Agentic framewor
arXiv:2610.03938v1 Announce Type: new Abstract: Agentic multimodal large language models (MLLMs) have recently pushed the frontier of visual reasoning by calling tools such as zooming and tagging. Despite the recent strong success of agentic MLLMs, this work uncovers a critical s
arXiv:2610.05124v1 Announce Type: new Abstract: Persistent memory for Large Language Models (LLMs) has matured rapidly: systems such as MemGPT/Letta, Mem0, and Zep now provide agents with tiered, temporally-aware, model-agnostic external storage, while the Model Context Protocol
arXiv:2607.12111v2 Announce Type: replace-cross Abstract: Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network enviro
arXiv:2610.03894v1 Announce Type: new Abstract: A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action has run. Frontier chat APIs hide the model's token probabilities; the agent's stated confidence barely
ChatGPT 发布 Meetings 插件，可替用户记会议笔记，并基于 ChatGPT 对用户和既往工作的了解，在 ChatGPT Space 保存个性化摘要和后续步骤。笔记可私密保存或与团队共享，还能让 ChatGPT 更新项目计划或起草跟进内容。目前以 beta 形式面向 Pro 和 Business 用户开放，可在 macOS 桌面应用的插件目录搜索 Meetings 使用，Enterprise 版即将推出。作者 Tibo 补充称，可放心畅谈并将积累的上下文载入 codex 来完成任务。 🔗 阅读原文 via AIHOT · https://ai
Organization and enterprise administrators can now see AI Scan for pull requests enablement status in the security overview coverage view. The code scanning summary shows enabled and not enabled repository counts, while repository rows show each repository’s effective AI Scan ena