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arXiv:2608.14577v1 Announce Type: new Abstract: Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly
arXiv:2608.14551v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty. We show that an auxiliary BERT+GCN classifier supplies a structured uncertainty signal that improve
arXiv:2608.14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists". We argue that this overlooks the social aspects of scientific teamwor
arXiv:2608.14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mother
arXiv:2608.14641v1 Announce Type: new Abstract: Agentic systems increasingly delegate model selection to a router, yet open-source routers are usually evaluated with different tasks, candidate pools, and execution protocols, limiting direct comparison. We present a common measurement protocol and h
arXiv:2608.14624v1 Announce Type: new Abstract: Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents. Across these workflows, every agent repeatedly executes a fixed context consisting o
arXiv:2608.14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and spatial geometry, here denoted as mechanical engineering awareness, has not been quantified systema
arXiv:2608.14613v1 Announce Type: new Abstract: Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool access and Google's Agent2Agent (A2A) protocol for agent delegation and negotiation. However, these protocols sp
arXiv:2608.14610v1 Announce Type: new Abstract: Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case -- a capability we term temporal applicable-law determination. However, whether large language models (LLMs) c
arXiv:2608.14590v1 Announce Type: new Abstract: LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use of tools. However, no existing system provides formally grounded, task-level safety guarantees for the plans th
arXiv:2608.14588v1 Announce Type: new Abstract: Sequential multi-agent LLM pipelines chain specialized agents without verification at handoffs, creating a structural flaw with measurable and severe consequences. We show that hallucinations injected at Stage 1 do not merely persist; they transform:
arXiv:2608.14587v1 Announce Type: new Abstract: Background: Recent advances in information retrieval (IR) leverage both dense and sparse representations, large language models (LLMs), and specialized retrieval models to improve ranking accuracy, relevance, and cross-lingual performance. Complementa
arXiv:2608.14585v1 Announce Type: new Abstract: Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and algebraic computation. Yet, existing approaches typically address only a subset of these abilities
arXiv:2608.14580v1 Announce Type: new Abstract: OGX (Open GenAI Stack) is an open-source AI application server and Python library that implements the APIs of major frontier labs (OpenAI, Anthropic, Google) with pluggable backend providers. Developers building agentic AI applications--such as retrie
arXiv:2608.14579v1 Announce Type: new Abstract: Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structures. Traditional expert-designed flows lack adaptability, while reinforcement learning (RL) methods o
arXiv:2608.14569v1 Announce Type: new Abstract: Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental limitation persistent constraint violations occur under distribution shifts even when the model reports high conf
arXiv:2608.14566v1 Announce Type: new Abstract: Recent work on evaluating the moral competence of large language models (LLMs) has focused primarily on what we call the moral value problem, i.e., whether model outputs align with human moral values. In contrast, the moral norm problem, i.e., whether
arXiv:2608.14559v1 Announce Type: new Abstract: Effective communication in multi-agent reinforcement learning requires agents to decide not only \textit{what} to communicate, but when? Existing approaches either communicate at every timestep or learn a binary gate through REINFORCE policy gradients
arXiv:2608.14558v1 Announce Type: new Abstract: Current multimodal models have demonstrated remarkable proficiency in recognizing static visual and auditory content. However, their capacity for abstract perceptual reasoning, inferring unseen information from dynamic, generative processes, remains a
arXiv:2608.14552v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty. We developed a controlled, psychophysics-inspired clinical
covonaut 是一个 Go 语言生产级 Agent 开发框架，基于 Go 1.25 构建，MIT 开源。框架覆盖 Agent 循环、工具注册与生命周期钩子、MCP 协议桥接、A2A 多智能体协作、图引擎（DAG 与 Pregel）、会话管理、工作流编排等功能，并自带一套完整的终端图形界面库。 v1.1.3 变更 本次发布聚焦终端界面交互。 输入刻度条（Input R...
就在这两天，联发科正式发布新一代旗舰移动平台天玑9600 Pro。作为天玑首款Pro旗舰，它率先采用台积电2nm制程，并以全新AI原生架构重构移动计算，带来双超大核CPU、重构双NPU、神经网络渲染与智能影像等一系列架构级升级，推动性能、能效、AI、游戏、影像以及通信显示体验全面跃升。过去几年，AI正从云端加速走向终端，手机也成为AI能力最重要的入口之一。随着生成式AI持续发展、消费者智能体进入新阶段，设备需要承载更强的理解与推理能力，也要更高效地调动内部多种算力资源，这让旗舰芯片的竞争开始从单一计算单元的性能提升，走向整颗SoC的系统级协同。正是在这种思路之下，天玑9600 Pro 首创AI
Meta 推出 WhatsApp Business Tools MCP 服务器，使 Claude、Cursor、Codex 和 ChatGPT 等 AI 编程 Agent 能直接连接 WhatsApp Business Platform。开发者可通过对话让 Agent 完成企业账户创建、电话号码验证、Cloud API 注册、消息模板编辑、测试及故障排查等工作。此举将 Meta 的 MCP 服务从广告管理和应用配置监控扩展至企业消息业务，可降低 WhatsApp Business 接入门槛，并提升开发与运营效率。
物理 AI 研究一直卡在一个尴尬的地方：软件栈开源得热火朝天，硬件却始终是实验室里那台没法复现的定制臂。OpenArm 想把这个口子撕开——一台 7 自由度的全开源人形臂，整套 bimanual 系统卖 6500 美元。 这个价格在同类里算激进。更激进的是它的定位：不是给你一台能拿东西的机械臂，而是给物理 AI 一个能全球复现实验条...
Elon Musk, the world’s richest man, says he’s living in an Airstream trailer to oversee xAI’s biggest expansion yet Fortune
近日，Apache DolphinScheduler 3.4.3 正式发布。本次版本围绕调度、权限、安全、性能和稳定性等核心能力持续优化，并针对任务实例与工作流实例查询、API 权限控制、敏感信息保护、任务失败恢复等多个生产环境中的实际场景进行了改进和修复。此外，预计将在 3.5.0 版本添加调度补火策略， 作为面向复杂数据工作流场景的开...
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9月15日，阶跃发布新一代语音大模型 StepAudio 3 系列，一次性推出 StepAudio 3 Realtime、StepAudio 3 ASR、StepAudio 3 TTS、StepAudio 3 Gen 和 StepAudio 3 Music 五款模型，覆盖实时语音交互、语音识别、真人级语音生成和音乐创作等场景。其中，多款模型在权威第三方 AI 模型评测机构 Artificial Analysis 榜单中位列全球第一。相比过去主要围绕语音识别或语音生成等单项能力展开竞争，StepAudio 3 将能力进一步延伸至语音理解与生成、实时推理、任务执行和音频创作的全栈能力，让 AI
用三套 checkpoint 完成多轮证明搜索，用过程透明平息学术怒火，用 1.5TB 显存门槛锁定算力霸权。名为代码平权，实则算力集权。 作者丨郑佳美 编辑丨岑 峰 9 月 9 日，NVIDIA 公布了 Nemotron 3 Ultra 在 2026 年 IMO 上使用的整套数学推理系统。这套系统最终拿到 30/42 分，超过当届 29 分的金牌线。整个比赛过程中，模型只用自
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