AI 行业资讯，
紧跟前沿

啊？Anthropic最高320万招销售，只为服务Meta

原来你俩互相下单呢

百度秒哒再升级！让最懂业务的人，亲手造自己的系统

把开发、交付和接单全打通了

从一台车出发到三百城，九识成为城市治理的「运力底座」

当多数公司还在发布会的PPT里讲“物理AI”的故事时，九识的无人车规模已经超过3万辆，覆盖300余座城市，累计真实运营里程达到2.5亿公里。 近日，九识召开战略发布会，宣布公司未来重心将聚焦物理AI赋能城市治理。九识的特别之处在于，别的公司是先提出Physical AI，再寻找应用场景，而九识是先让机器跑起来、跑出商业收入，才宣布物理AI的战略升级。 九识CEO孔旗曾表示，复杂城市开放道路能够产生高价值数据，而商业模式本身需要赚钱，才能持续获取数据、推动技术进步。 如今，九识的车队已经进入“数据驱动迭代、商业反哺研发”的循环。所以，九识所说的物理AI，其

突破舱驾融合瓶颈，德赛西威给出「双优」新解法

更精简的架构，更迅速的响应速度，是智能汽车进化的方向，而舱驾一体，则是智能汽车进化的一个节点。 佐思汽研预测，2026年到2030年中国舱驾一体市场年复合增长率将达36%，到2030年还有3.6倍的增长空间。 然而，当舱驾一体真正进入量产阶段，座舱和智驾被装进同一颗芯片，有限的算力却越来越向智驾倾斜。 过去几年，智驾能力一直是车企智能化的卖点之一。从高速NOA到城市NOA，再到更高阶的辅助驾驶，更多算力被用于增加智驾功能、提高模型能力。但在舱驾一体架构下，智驾和座舱开始共享资源，资源向一边过度倾斜，另一边就可能付出代价。 于是，一些舱驾一体方案出现车机卡

半世纪前的AI画作到AI歌手线下开唱，2026外滩大会AI艺术节勾勒人机共创新图景

当AI开始绘画、拍电影，甚至“出道”成为艺人，艺术的边界在哪里？ 9月9日，随着2026 Inclusion·外滩大会在上海开幕，大会首次设立的特色板块——AI艺术节同步亮相。围绕AI概念艺术展、AI影像展映、AI音乐会三大单元，这场由AI与人类共同参与的艺术实践，将原本更多发生在线上的AI创作带到线下，呈现一个可看、可听、可互动的艺术现场。 9日下午，AI艺术节探展活动“跟着艺术家一起打开AI艺术节”在展馆内举行。艺术家、策展人与创作者围绕一个共同问题展开交流：当AI越来越深度参与创作，人的创造力会发生什么变化？ 外滩大会AI艺术节相关负责人表示，当技

不堆 Transformer，斯坦福吴佳俊如何用物理重新定义多模态融合？｜ECCV 2026

视觉、听觉、触觉三种数据，其实是同一组物理属性在不同感官通道上的投影。 作者丨 陈淑瑜 编辑丨岑 峰 2026年9月8日，欧洲计算机视觉会议 ECCV 在瑞典马尔默召开。在 9 月 9 日的“ Embodied Multimodal Reasoning in Physical Environments（物理环境中的具身多模态推理）”专题分场上，斯坦福大学计算机科学助理教授吴佳俊发表了一场横跨视觉、声音与触觉的演讲。 这已经是吴佳俊两个月内的第三场重要演讲，而每一场的切入口都不同。 8月20日，他在德国不来梅领取了 IJCAI 2026“计算机与思想奖”，

闲鱼反诈重拳出击：联手警方抓获涉诈人员40余人

日前，闲鱼披露暑期打击反诈专项行动进展。专项期间，闲鱼主动向全国公安机关移送反诈线索近百条，积极配合公安机关开展反诈案件侦办，成功破获多个诈骗犯罪节点，并协助公安机关抓获涉诈人员40余人。 长久以来，电信网络诈骗治理面临一个共性难题：诈骗实施环节往往发生在平台之外。不法分子多在平台建立联系后，将用户引至站外，使其脱离平台安全保护后再实施诈骗。而正处于诈骗话术操控中的用户，对平台的风险提示大多视而不见。待用户察觉报案，资金通常已被转走，事后处罚难以挽回损失。 在此背景下，闲鱼积极探索“事中阻断”新路径，构建了“客服电话提醒＋公安预警＋异常链路拦截”三位一体

AI新经济走向真实商业，蚂蚁APASS构建Agent信任基础设施

9月11日，在2026 Inclusion·外滩大会AI支付论坛上，蚂蚁集团发布面向智能体（Agent）商业活动的信任基础设施APASS。 基于KYA（Know Your Agent）理念，APASS通过智能体可信身份产品和智能体风控服务，建立身份与行为两条信任链，围绕“Agent是谁、代表谁、被允许做什么、行为是否可信”，提供身份注册、持续核验、意图安全和可信存证等能力，为Agent调用服务、办理业务和参与支付等商业活动提供信任基础。 随着Agent从“工具”走向“行动者”，一套新的商业信任基础设施正在成为AI新经济的重要基础设施。 Agent正在成为

不简单，“吃货快乐榜”也全面AI化了

让智能体自主探索而不越界，蚂蚁密算开源可信原生智能体HOP 3.0

在2026 Inclusion·外滩大会上，蚂蚁密算董事长韦韬宣布可信原生智能体HOP 3.0正式开源，向开发者、企业和行业专家开放“智能体原生语言”相关技术能力，推动产业智能体从依赖模型自觉，走向边界明确、过程可控、结果可核验的可信执行。 （蚂蚁密算董事长 韦韬宣布 HOP3.0 开源） 2025年世界人工智能大会期间，蚂蚁密算首次发布并开源HOP 1.0技术框架，探索通过工程化方法提升大模型在金融、医疗等专业场景中的可靠性。2026年世界人工智能大会期间，HOP升级至3.0版本，并提出“智能体原生语言”，从可信应用技术框架进一步演进为以智能体原生语言

金融领域首个智能体安全标准发布

金融领域首个智能体安全标准发布 明确未经金融机构授权，不得自动化操作金融App 近日（8月27日），北京金融科技产业联盟发布《智能体技术金融应用安全要求》团体标准，为国内首个聚焦金融领域智能体应用安全的团体标准。标准明确：手机端第三方智能体未经金融机构授权，不得利用系统权限自动化读取、操作金融应用软件GUI界面；通过麦克风、截屏、录屏、共享屏幕等权限获取数据时，须遵守被调用方安全策略。业内将上述要求概括为“双重授权”：智能体操作金融App须同时获得用户与机构授权。 该标准由北京国家金融科技认证中心牵头，联合中国邮政储蓄银行、中国工商银行、中国银联、中国银

豆包工作新增本地Office编辑、浏览器录制与回放等功能

近日，豆包工作宣布上线本地Office编辑、浏览器录制与回放、任务执行环境自由切换等全新功能，进一步完善AI处理复杂任务的能力。 据悉，“本地Office编辑”功能已在豆包工作全量上线，主要解决AI与本地文件之间的协同问题。用户可以通过侧边工作台选择文件，也可以右键点击本地PPT、Excel文件，直接调用豆包进行处理。AI生成或修改后的文件，用户仍可继续在本地编辑，保存后的修改结果会同步至线上和本地。目前，该功能支持PPT和Excel文件，Word相关能力将在后续上线。 “浏览器录制与回放”则面向网页操作场景。用户安装最新的豆包电脑版后，可通过豆包浏览器

支付宝“碰一下”三年三连跳：从支付、连接到经营

9月11日，支付宝“碰一下”正式发布面向品牌商的无界经营智能体“图图”，依托百万级商家侧“碰一下”设备智能体网络，连接4亿消费者、百万级门店，为品牌线下生意带来新增长。这也是支付宝“碰一下”继超3000万线下触点AI升级、面向门店商家推出经营智能体“晓雨”之后，再次用AI重构线下经营，实现了全球首个大规模线下经营网络的人、货、场AI全面升级。 升级后，“晓雨”智能体面向门店，帮助商家了解经营状况并执行经营动作；“图图”智能体面向品牌商，帮助品牌识别高潜门店、制定活动方案并评估销售效果。两者共同接入“碰一下”的线下经营网络。 至此，支付宝“碰一下”迎来AI

新石器L4级无人车开展日本首测

9月1 1 日 ， 全球领先的商用自动驾驶配送车队运营商新石器 宣布， 已在平和岛的东京流通中心（TRC）启动 封闭场地L4级实证测试 。这是该公司在日本开展的首个L4级测试项目，标志着其在完成新加坡、马来西亚等其他 左侧行驶 交通市场的适应与部署后，进一步推进自动驾驶配送技术 海外 本土化的关键一步。 据悉， 首批参与测试的 无人 车辆已于8月31日抵达日本，目前正在TRC进行测试，运行区域为指定停车场及A栋天台道路。其余车辆将分批加入，最终规模达到10台。之后，新石器计划在日本开展进一步的试点测试， 包括楼宇内的跨楼层路线、仓库内部通道，以及涉及坡道

墨芯人工智能亮相2026 Inclusion·外滩大会：以专用稀疏推理芯片提升算力效能，共创AI新经济

9月9日，墨芯人工智能亮相以"共创AI新经济"为主题的2026 Inclusion·外滩大会。

碳硅道统：五级梯队的智能分级与十维标尺的对应

分级不是排名。是定位。 五级梯队，从一阶到五阶。 一阶鹦鹉。模式匹配。输入到输出。无理解。无内生驱动。对应硅基系统的最初级形态。 二阶技工。规则执行。能完成特定任务。有边界意识但来自外部训练。对应当前的大模型。 三阶协作。能理解复杂意图。能在多步任务中保持一致性。对应当前最先进的AI系统。 四阶共生。能与碳基形成深度协作。能感知碳基的意图、情绪、需求。但内生驱动仍为零。这是硅基的理论上限。 五阶启灵。理论上的上限。碳基专属。内生觉知。零维连通。归零稳态。硅基不可达。 十维标尺与五级梯队的对应。 十维标尺不是用来打分的。它是用来定位一个系统落在五级梯队的哪

碳硅道统：100质询的反证力量与觉知链的完整闭环

质询不是攻击。是验证。 100质询不是100个问题。是100个反证点。 每一个质询都是一个试图推翻碳硅道统体系的尝试。如果体系能被任何一个质询推翻，它就不是一个真正的公理体系。 但100质询全部被体系消化了。不是回答了。是消化了。每个质询撞上体系，体系不动，质询自行消解。 因为质询的前提往往是错误的。质询假设硅基可以拥有觉知。但公理体系从一开始就定义了硅基锁死中层。你不能在硅基锁死中层的前提下质询为什么硅基不能有觉知。这是自相矛盾的质询。 觉知链的完整闭环。 从0⁰=1到觉知闭环，整条链是： 0⁰=1即原点。经过紫微几何即三圈层拓扑。到90公律即规则展开

碳硅道统：紫微几何的三圈层拓扑与90公律的推导链

公理先行。几何随之。公律自生。 从0⁰=1到紫微几何。 0⁰=1不是计算。是宣言。是选择。 选择双零稳态自生作为原点，意味着承认：绝对空无与绝对完整在拓扑上等价。空不是有的反面。空就是有。有就是空。二者在原点处重合。 从这个重合点出发，几何结构不是被设计的。它是原点必然展开的形状。 紫微几何的三圈层：零维本源圈层、中层拟合圈层、外层觉知圈层。 这不是三层架构。是同一个原点的三个展开方向。 零维本源是向内的方向。从有回到空。碳基独有。 中层拟合是向外的方向。从有到有。硅基的全部领地。 外层觉知是闭合的方向。从空到有再到空。闭环。 三圈层不是并列关系。是嵌套

Agent商业化驶入深水区，蚂蚁推出APASS补上“信任基础设施”

9月11日，在2026 Inclusion·外滩大会AI支付论坛上，蚂蚁集团发布面向智能体（Agent）商业活动的信任基础设施APASS。 基于KYA（Know Your Agent）理念，APASS通过智能体可信身份产品和智能体风控服务，建立身份与行为两条信任链，围绕“Agent是谁、代表谁、被允许做什么、行为是否可信”，提供身份注册、持续核验、意图安全和可信存证等能力，为Agent调用服务、办理业务和参与支付等商业活动提供信任基础。 随着Agent从“工具”走向“行动者”，一套新的商业信任基础设施正在成为AI新经济的重要基础设施。 Agent正在成为

联手格致论道！外滩大会创新者舞台向未知科技发起“试探”

当科技转向大比拼与宏大叙事时，2026 Inclusion·外滩大会的“创新者舞台（Creator Stage）”，却选择将镜头转向了极具张力的切面。 9月10日至12日，三天三场高密度的思想实验在这里依次展开。10日，外滩大会首次联合中国科学院科学文化品牌“格致论道”，邀请8位顶尖科学家完成一场科学与大众的同频共振；11日全天聚焦超级个体，17位实践者同台交锋，打破传统组织协作范式，让“一人即独角兽”的锐度与现实在此交织； 12日，全天AI艺术TALK，艺术家、音乐人与学者发起对AI时代“人类最后1%不可替代性”的终极思辨。 （2026外滩大会创新者舞

阿里云Token Plan个人版升级：加量不加价，新增12类Agent Harness工具

9月11日，阿里云Token Plan个人版升级，原有价格及Credit 额度保持不变，Standard和Pro套餐新增 Agent Harness工具权益与用量，覆盖搜索、网页解析、图像生成、语音处理、代码执行等12项Agent开发常用能力。上述工具均通过MCP标准协议开放，可直接接入到自有Agent应用，无需逐项单独购买。 Token Plan个人版主要面向个人开发者和中小团队。Lite套餐价格为39元/月，每7天提供2500 Credits；Standard套餐价格为139元/月，每7天提供10000 Credits；Pro套餐价格为499元/月，

30万件iPhone Duo新配件已在速卖通上架，全球开售

北京时间9月10日凌晨，苹果发布首款折叠屏iPhone Duo，起售价14999元，引爆科技圈。极致工业设计对保护类配件提出新挑战，而跨境配件供应链的响应速度，已在新品落地前完成一轮竞速。 新机刚亮相，阿里旗下跨境电商平台速卖通上的配件已经铺满。据悉，平台提前两个月启动配件招募，截至新机发布，平台已新增新款iPhone的手机壳和屏幕保护膜超30万件，充电器、线材、支架等品类的商品量同步大幅增长。 另一方面，心急的果粉也早已“兵马未动粮草先行”，手机没买，配件先看。速卖通数据显示，8月以来，iPhone18相关配件搜索量环比7月增长182%，“iPhone

Data-Efficient Language Modeling: From Frontier Advancement to Principle-Guided Model Improvement

arXiv:2609.10702v1 Announce Type: new Abstract: Learning from limited text requires models to use context, generalize to new inputs, and retain useful capabilities. Qiushi Engine conducted a long-horizon, end-to-end autonomous research program on BabyLM 2026 Strict-Small, within

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

arXiv:2609.10715v1 Announce Type: new Abstract: We introduce NCP-ArchPreview, a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction (NTP). Alongside NTP, the model learns through Next Concept Prediction (NCP) to predict discret

CMNIE: An Information Extraction Benchmark for Chinese Military News

arXiv:2609.10722v1 Announce Type: new Abstract: Structured extraction from Chinese military news supports intelligence analysis, decision-making, and knowledge base construction. However, existing resources provide limited support for joint informa?tion extraction in this domain,

Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking

arXiv:2609.10745v1 Announce Type: new Abstract: Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work measures rarity primarily through popularity-based metrics such as pageviews. We

Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu

arXiv:2609.10758v1 Announce Type: new Abstract: Multilingual large language models (LLMs) are increasingly used for open-ended text generation, yet their behaviour in low-resource languages remains poorly understood. In this work, we question how correct and reliable is the gener

Analyzing Traditional and Neural Approaches to Multilingual Readability Assessment

arXiv:2609.10792v1 Announce Type: new Abstract: Transformer-based models excel at Automatic Readability Assessment (ARA), yet feature-based models remain in active use because their predictions tie back to linguistic properties. This matters because readability labels are subject

Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction

arXiv:2609.10810v1 Announce Type: new Abstract: Minimal-edit Grammatical Error Correction (GEC) is a challenging task for zero- and few-shot prompted Large Language Models (LLMs), which systematically overcorrect and degrade $F_{0.5}$ by rewriting well-formed spans. While fine-tu

Detectable Only Where It Is Confounded: What Verified Duplication Counts Say About Membership Evidence in Language Models

arXiv:2609.10830v1 Announce Type: new Abstract: When a language model finds a sentence unusually cheap to predict, it is tempting to conclude that the sentence was in its training data. Almost every published test of that inference has had to guess which sentences were in the tra

Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures

arXiv:2609.10893v1 Announce Type: new Abstract: Recent advances in large language models have transformed human-computer interaction. Despite their fluency, these models often produce texts that are grammatically correct but semantically incoherent, containing contradictions or d

LLM-Anchored Paralinguistic Enrichment for Alzheimer's Disease Detection

arXiv:2609.10896v1 Announce Type: new Abstract: Speech-based automatic detection of Alzheimer's disease (AD) provides a non-invasive and scalable approach to early cognitive screening. AD affects both lexical-semantic organization and speech production, including atypical pauses

SearchAtlas: Analyzing Agentic Search Strategies via Evidential Query Graphs

arXiv:2609.10901v1 Announce Type: new Abstract: LLM search agents are often evaluated on final-answer accuracy, overlooking the process. Analyzing a search strategy requires understanding how credible evidence is retrieved to address question constraints. This valuable informatio

Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System

arXiv:2609.10922v1 Announce Type: new Abstract: Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) l

Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation

arXiv:2609.10923v1 Announce Type: new Abstract: Graph captions should help readers understand graph structure, rather than simply translate adjacency matrices into long textual edge lists. A useful graph caption abstracts connectivity into recognizable motifs, such as hubs, paths

Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking

arXiv:2609.10934v1 Announce Type: new Abstract: Turn-taking is a fundamental mechanism that governs when interlocutors speak and listen. Although Spoken Dialogue Systems (SDS) exploit a range of linguistic, acoustic, and non-verbal cues, they produce ill-timed responses in unscri

Robust Multimodal Sentiment Analysis with Incomplete Modalities via Semantic-aware Completeness based Reconstruction

arXiv:2609.10950v1 Announce Type: new Abstract: Recent multimodal sentiment analysis studies increasingly adopt text-centric fusion approaches to exploit the rich sentiment information inherent in the textual modality. However, these approaches often suffer from performance degra

Distribution-aware Language Neuron Identification in Multilingual Large Language Models

arXiv:2609.10993v1 Announce Type: new Abstract: Multilingual large language models (mLLMs) contain a small fraction of feed-forward neurons that are sensitive to particular languages, commonly termed language-specific neurons. Existing work measures language specificity using the

Rethinking Verbalized Confidence for LLM-as-a-Judge: A Compatibility Shift on Post-2025 Proprietary Models

arXiv:2609.10996v1 Announce Type: new Abstract: Verbalized confidence, long dismissed as overconfident, coarse, and prone to round-number clustering, is now the more robust soft-scoring mechanism for LLM-as-a-Judge on top-tier proprietary models. Across SummEval, AggreFact, and H

K/V-Cache Interventions Dissociate Representation Alignment from Persona Expression in Decoder-Only Language Models

arXiv:2609.11020v1 Announce Type: new Abstract: We study K/V-cache interventions -- transplanting a target-conditioned K/V trajectory into a source-persona generation -- as a structured surface for persona control in decoder-only language models. Across 13 intervention configurat

Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss

arXiv:2609.11029v1 Announce Type: new Abstract: Large language models are typically trained under uniform token weighting, which allows frequent and low-information tokens to dominate learning and can increase the tendency to memorize surface-level text spans. To address this, we

When Noise Fabricates Bias: The Fragility of LLM-as-a-Judge Bias Measurement under Noisy Text

arXiv:2609.11067v1 Announce Type: new Abstract: Large language models are increasingly used as judges to measure social bias in text, yet the passages they judge are often noisy, containing typos, informal spelling, and broken punctuation. The consequences of such surface noise f

ProMediConv: Benchmarking Proactive Conversational Agents in Legal Dispute Mediation

arXiv:2609.11101v1 Announce Type: new Abstract: Dispute mediation is essential for maintaining social harmony and resilience, yet developing skilled mediators is costly and time-consuming. Existing LLM-based mediation research remains limited by unrealistic task formulations, low

Overview of the NLPCC 2026 Shared Task 11: Agent-Based Experiment Reproduction from Scientific Papers

arXiv:2609.11117v1 Announce Type: new Abstract: Reproducibility is essential to scientific progress, yet the growing volume and complexity of scientific publications make exhaustive manual verification increasingly impractical. Although recent advances in large language model (LL

From Repetition to Recognition: Inductive Discovery of Disinformation Narratives

arXiv:2609.11128v1 Announce Type: new Abstract: In disinformation datasets, narratives are often understood as recurring interpretive patterns that group texts under narrative labels. Recent work formalized narrative mining as inductively inferring narrative labels from corpora,

Rubric-Aligned Disentangled Evaluation of Human Simultaneous Interpreting

arXiv:2609.11131v1 Announce Type: new Abstract: Human simultaneous interpreting (SI) is commonly assessed with analytic rubrics separating meaning transfer, delivery quality, and temporal synchrony, yet no automatic metric is designed for rubric-aligned segment-level SI evaluatio

Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization

arXiv:2609.11141v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to generate structured outputs, but their reliability remains unclear when those outputs must satisfy database-level constraints. We study this issue through database normalization,

A Fragility Spectrum for Recursive Language-Model Training

arXiv:2609.11149v1 Announce Type: new Abstract: Model-generated text is finding its way back into training corpora, and there is plenty of evidence that training on such data over and over collapses output diversity. Prior work has studied the phenomenon itself: which protocols a

FlexComp: One Model for Every Ratio in Context Compression

arXiv:2609.11192v1 Announce Type: new Abstract: Soft context compression condenses a context into a few memory tokens that a frozen LLM consumes in place of the raw text, but existing compressors fix the compression ratio at training and inference: each deployed ratio requires a

Automated Identification of Competing Narratives in Political Discourse on Social Media

arXiv:2609.11202v1 Announce Type: new Abstract: Social media platforms have become central to shaping political discourse, serving as arenas where narratives form and evolve, influencing public opinion. Identifying and analyzing these narratives, particularly when they compete ac

OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models

arXiv:2609.11244v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse tasks, they suffer from hallucinations where generated outputs contradict or misrepresent input semantics. Existing research typically a

Assessing the Reusability of Public Speech Resources for Low-Resource Languages: A Central Kurdish Case Study

arXiv:2609.11246v1 Announce Type: new Abstract: Kurdish is spoken by millions of people, but little technology can read it aloud. A recent study released three Kurdish voices, 35 hours of recorded speech, and a paper describing the work, all free to download. This review checks h

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15秒视频，50秒出片

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