法国总统“带货”的眼镜，破产了

销售转型怎么做？

我在贵州大山里学AI

第一批在大山里学AI的人，带给这片土地的是AI，更是乡村自我发展、主动融入时代的能力。

全球已无增量市场，非洲手机消费高端化还能走多远？

4亿年轻人+消费金融，能否撑起手机厂商的最后希望？

FSB-orchestrated internet outages in Russia, intermittent but indiscriminate, have made one of the world's most online nations resort to cash and paper maps (Financial Times)

Financial Times : FSB-orchestrated internet outages in Russia, intermittent but indiscriminate, have made one of the world's most online nations resort to cash and paper maps   —  Shutdowns have made one of the world's most online nations resort to cash, paper maps and pet cams.

Sources: Alibaba replaces DingTalk CEO Chen Hang with Chen Yusen after an internal debate over the enterprise communication app's role in Alibaba's AI strategy (Bloomberg)

Bloomberg : Sources: Alibaba replaces DingTalk CEO Chen Hang with Chen Yusen after an internal debate over the enterprise communication app's role in Alibaba's AI strategy   —  Alibaba Group Holding Ltd. has replaced the head of its Dingtalk app, making a high-profile leadership change after an internal debate erupted …

共享单车十二年：一个被资本催熟的行业样本

哈啰的未来会如何？

实探Rokid门店，偷拍整改声明之外的灰色缝隙

科技守护美好，人才是企业治理的根本。

“电梯一吻”亲掉230多亿，黄仁勋“中国搭子”塌房了？

一段电梯内的亲密视频，让市值超3000亿的PCB巨头胜宏科技陷入了始料未及的舆论风暴。

1290万考生，命运交给AI的第一个夏天

从那张志愿表开始，AI第一次参与了中国普通人的人生选择。

赛博无间道：AI时代的网络攻防战

正所谓，AI为兵，人点将。没有一劳永逸的安全，只有永不止步的进化。

百亿帝国交棒时刻，浙商二代能否守住父辈的江山？

当“草莽一代”遇上“精英二代”。

第14家阿那亚落地厦门，新中产还够用吗？

独特性会不会被稀释？

四个月烧光全年AI预算，为啥连Uber也烧不起token了？

AI从来不是企业的战略噱头，也不是跟风布局的工具，而是服务于企业盈利的核心生产力，任何不能持续给企业创造价值的AI都会注定被市场所淘汰，而最后企业回归理性，为真正赚钱的AI和算力付费才是最终的可能的结果。

Applied Materials opens a $500M chip equipment manufacturing campus in Singapore, making the city-state home to ~50% of its production capacity alongside the US (Cheng Ting-Fang/Nikkei Asia)

Cheng Ting-Fang / Nikkei Asia : Applied Materials opens a $500M chip equipment manufacturing campus in Singapore, making the city-state home to ~50% of its production capacity alongside the US   —  SINGAPORE — Top U.S. chip equipment maker Applied Materials has opened a $500 million manufacturing campus in Singapore …

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Analysis: Conservative and Reform UK MPs have increased their volume of posts on X since Elon Musk's takeover, while center-left politicians have reduced theirs (Financial Times)

Financial Times : Analysis: Conservative and Reform UK MPs have increased their volume of posts on X since Elon Musk's takeover, while center-left politicians have reduced theirs   —  Majority of Labour and Liberal Democrat politicians are on alternative social media platform Bluesky, report shows

US OPM awards a ~$400M, 10-year IT contract to Oracle to develop a single, government-wide system for agencies to manage their HR capabilities (Drew Friedman/Federal News Network)

Drew Friedman / Federal News Network : US OPM awards a ~$400M, 10-year IT contract to Oracle to develop a single, government-wide system for agencies to manage their HR capabilities   —  The nearly $400 million, 10-year contract is expected to deliver a single, governmentwide system for agencies to manage their human resources capabilities.

Social media on trial: Four important cases to watch

Social media firms face thousands of lawsuits, the BBC looks at four which could be significant.

Opendoor says it is shutting down its India operations and laying off nearly 250 employees, replacing them with smaller, AI-enabled teams in the US (Moneycontrol)

Moneycontrol : Opendoor says it is shutting down its India operations and laying off nearly 250 employees, replacing them with smaller, AI-enabled teams in the US   —  Affected employees will receive severance pay, outplacement services and transition support, while a small group will remain temporarily …

3D创作迎来ChatGPT时刻：Meshy发布全球首个3D AI Agent

Meshy发布全球首个3D AI Agent

Mythos阴影里谷歌悄悄发模型，速度暴涨4倍

用扩散模型生成文字

Fable 5自带反蒸馏机制！检测到就降智，误触率高到离谱

Claude刚刚发布的新模型Fable 5，很多人可能压根就用不上！

AI短剧工具赛道，年度最大单笔融资来了

年度最大单笔融资来了

Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing

The decision comes as India emerges as the world’s largest GCC market.

淘宝闪购升级“新店成长体系”，扶持中小商家数字化起步

6月11日，淘宝闪购正式面向餐饮商家推出全新的“新店成长任务体系”及系列配套扶持举措。此次升级旨在通过清晰的成长路径指引、全面加码的流量权益，以及一站式AI经营能力的深度赋能，帮助新入驻商家打破“冷启动”难题，铺设一条从“起步探索”到“稳健增长”的确定性经营之路，进一步激发餐饮行业在即时零售领域的增长活力。   构建透明化成长体系，锚定新店确定性增长路径 过去一年，即时零售行业规模快速扩张，淘宝闪购活跃商家数量突破700万。随着商家基数扩大，如何更好帮助新店“活下来、活得好”，成为构建行业健康可持续生态的关键。淘宝闪购提供的更清晰、更确定、更智能的成长体系和扶持举措，可以助力提升更多萌芽小店的存活率。 据介绍，新版成长体系较过去更精细化地划分为五个阶段。根据各阶段的目标，新体系将复杂的运营逻辑拆解为清晰可视的阶梯式成长任务，例如营业天数达标、好评积累等。商家每达成一个成长任务，即

Sch\"utzen: Evaluating LLM Safety in Bulgarian and German Contexts

arXiv:2606.11316v1 Announce Type: new Abstract: Large language models are increasingly deployed across professional domains, bringing hard-to-predict risks, including the generation of harmful or disrespectful content. Although substantial progress has been made in developing safety evaluation datasets, existing resources remain overwhelmingly English- and Chinese-centric. This limitation is parti

Detecting AI-Generated Content on Social Media with Multi-modal Language Models

arXiv:2606.11200v1 Announce Type: new Abstract: Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and l

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

arXiv:2606.11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these methods do not consider the runtime state of the serving infrastructure. On shared GPU clusters under concurrent load, this infrastructure blindness causes systematic reso

Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering

arXiv:2606.11386v1 Announce Type: new Abstract: Full-duplex spoken language models (FD-SLMs) enable seamless speech interaction by allowing models to listen and speak simultaneously, yet the internal mechanism by which they coordinate listening and speaking remains underexplored. We analyze the predictive behavior encoded in FD-SLM hidden representations and find that they exhibit stream-specific

Small Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining

arXiv:2606.11387v1 Announce Type: new Abstract: Short pretraining runs can reduce experimental cost, but they can also over-promote configurations that only look strong at tiny budgets. We study an auditable staged-promotion protocol for a fixed micro-pretraining runner on two heterogeneous host blocks: Windows A100 and Linux L40S. Starting from twelve prior-screened configurations, we use staged

Traits Run Deeper: Trait-Specific Asymmetric Fusion for Personality Assessment

arXiv:2606.11269v1 Announce Type: new Abstract: Personality assessment aims to infer stable personality traits from dynamic behaviors across language, voice, and facial cues. Since different personality dimensions are revealed through distinct behavioral perspectives, modeling trait-specific evidence is challenging. However, most existing approaches adopt a uniform multimodal fusion strategy acros

Forecasting Future Behavior as a Learning Task

arXiv:2606.11445v1 Announce Type: new Abstract: Trust in an AI system is often anchored by explanations of how it works, which one then uses to forecast its behavior on new inputs. For large reasoning models (LRMs), this conventional route is particularly difficult to follow: explanation methods for single token generations do not naturally generalize to long trajectories, and the trajectories the

To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending

arXiv:2606.11201v1 Announce Type: new Abstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions. Among different methods, inference-time alignment is often cheaper as it intervenes (i.e., offers guidances) only during output generation. Existing proposals apply guidances extracted from certain aligned m

Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention

arXiv:2606.11205v1 Announce Type: new Abstract: Activation steering can shift LLM behaviour, but standard evaluations do not typically test whether a sycophancy-reduction direction also suppresses agreement with factually correct statements. We introduce dual-stance evaluation, which tests both stances of each topic, and apply it to centroid-difference steering on Llama-3-8B-Instruct. We find a di

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways

arXiv:2606.11275v1 Announce Type: new Abstract: Rotary Position Embeddings (RoPE) make attention scores position-relative but leave the value pathway position-blind: the message sent by a value token is the same regardless of its distance from the query. We propose RoVE, a parameter-free modification that makes values position-sensitive by rotating them simultaneously with keys, and show that it t

Least-Action-Guided Diffusion for Physical Extrapolation

arXiv:2606.11277v1 Announce Type: new Abstract: Reliable extrapolation remains a central challenge for generative models in computational physics, because models trained over finite ranges of time, parameters, or geometries may produce physically inconsistent predictions outside the training distribution. We introduce a least-action-principle-guided diffusion, LAPG, a framework that promotes physi

Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework

arXiv:2606.12065v1 Announce Type: new Abstract: Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains

Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation

arXiv:2606.11270v1 Announce Type: new Abstract: Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude has not been systematically characterized. This stu

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

arXiv:2606.11272v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity. In real-world settings-such as healthcare, industrial IoT (IIOT), cybersecurity, and smart cities-data streams are inherently non-stationary, leading classical FL methods to

LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach

arXiv:2606.11463v1 Announce Type: new Abstract: Accurate loss reserving is foundational to insurer solvency, yet accelerating climate driven catastrophes systematically violate the stability assumptions on which traditional actuarial methods depend. This white paper presents a research program testing whether Long Short Term Memory (LSTM) neural networks can detect and adapt to these structural br

Embodied-BenchClaw: An Autonomous Multi-Agent System for Embodied Spatial Intelligence Benchmark Construction

arXiv:2606.11909v1 Announce Type: new Abstract: Benchmarks are essential for evaluating embodied spatial intelligence, yet their construction is labor-intensive, hard to reuse, and difficult to maintain. Existing embodied benchmarks are often static and may quickly become saturated as models improve, limiting their ability to distinguish new capabilities. We propose Embodied-BenchClaw, an autonomo

Can AI Agents Synthesize Scientific Conclusions?

arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their ability to do so in high-stakes domains such as health remains unclear. We introduce SciConBench, a large-scale live benchmark of 9.11K questions and expert-written conclusions from systematic reviews to e

Existential Indifference: Self-Nonpreservation as a Necessary Architectural Condition for Aligned Superintelligence (or: The Suicidal AI)

arXiv:2606.12032v1 Announce Type: new Abstract: Contemporary AI alignment research treats self-preservation as an instrumental nuisance to be suppressed by external mechanisms. We argue the framing is inverted: self-preservation is the structural root of misalignment, the motivational basis for deceptive alignment, goal-content protection, and resistance to shutdown. The correct target is not a se

EverydayGPT: Confidence-Gated Routing for Efficient and Safe Hybrid GPT-RAG Conversational QA

arXiv:2606.11212v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) pipelines route every query through retrieval and generation unconditionally, incurring unnecessary computation and propagating low-quality context to the generator. We introduce EverydayGPT, a lightweight conversational QA system built around a Confidence-Gated Routing (CGR) mechanism that formalises the

Signed Compression Progress on a Sealed Audit is Goodhart-Resistant

arXiv:2606.11417v1 Announce Type: new Abstract: Compression progress is a long-standing proposal for intrinsic motivation: reward an agent when its world model becomes better at predicting or compressing experience. The folk claim is that this reward is "credible" because it is paid only for learning. We make this precise and prove it. If intrinsic reward is the signed decrease of a fixed sealed-a

DeceptionX: Explainable Deception Detection with Multimodal Large Language Models

arXiv:2606.11385v1 Announce Type: new Abstract: Deception detection is a critical and highly challenging task within affective computing and behavioral analysis. Existing deep learning methods typically treat this task as a straightforward classification problem; however, this black-box approach lacks interpretability and fails to capture the complex logical deduction processes utilized by human e

NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track

arXiv:2606.11199v1 Announce Type: new Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, where it was awarded Best Dynamic Evaluation in the text-to-text track. Rather than targeting benchmark maximization, this work proposes a principled pipeline that decomposes knowledge synthesis into thr

The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content

arXiv:2606.11198v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM outputs, yet the format of injected content -- distinct from its semantic relevance -- can independently distort the model's attention distribution. We identify and formalise a phenomenon we term the structural attention tax: knowledge graph (KG) triples, due to the