Agents & autonomy

Recent sign language generation (SLG) systems increasingly output dense 3D body representations, which better preserve full-body kinematics and geometry for downstream embodiment on humanoid robots. However, these generated motions frequently exhibit self-intersections such as hand-hand and hand-torso penetration. While such artifacts may be tolerated in offline rendering, they become critical in humanoid execution as they lead to infeasible inverse-kinematics (IK) solutions, collisions, and uns

The Emerging Payments Association Asia (EPAA) has launched the AI & Agentic Payments Working Group with founding member HSBC, bringing together the banks, payment networks, fintechs and technology platforms that will define the standards to make agentic commerce work safely and at scale across Asia Pacific (APAC).

User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost. However, as the online recommendation environment evolves continuously, these statically configured strategies gradually become stale, degrading the user experience. Refining them typically relies on manual inspection, diagnosis, an

A humanoid robot combat event organized by a Shenzhen robotics company has gone viral, even catching the attention of Tesla CEO Elon Musk. On July 19, Musk reposted a video from the event on social media, writing, “Robot fights are fun.” The footage features two EngineAI T800 full-size humanoid robots trading punches and kicks inside […]

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Yimu Tech, a Chinese developer of tactile sensing hardware and software for embodied-intelligence systems, has completed a Series E financing round of more than RMB1 billion, bringing its valuation above RMB10 billion. The financing was jointly backed by multiple leading RMB funds, USD funds and industrial investors. The company is developing tactile sensing materials, chips, […]

Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-correct plans, and reported acceptance keeps rising while true validity falls, so existing methods lack a principled basis for deciding when repair should stop. We propose VRR-Stop, a robust stopping framework for noisy verify-repair-repeat (VRR) loops. A four-p

China has developed more than 400 humanoid robot products, accounting for more than half of the global total, according to data released by the Ministry of Industry and Information Technology on July 20. The ministry also said Chinese quadruped robots accounted for close to 70% of global sales. [Xinhua, in Chinese]

AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater ...

Bilibili showcased its open-source “Catgirl Plan” AI digital-life ecosystem at WAIC 2026 in Shanghai on July 18. Its core product, Project N.E.K.O., is a proactive multimodal AI companion that can continuously observe a computer environment, interpret desktop content and initiate conversations. The system separates its front-end interface, AgentAI system and memory layer, while allowing users […]

Computer Use Agents (CUAs) can autonomously execute complex, multi-step tasks within GUIs, enhancing efficiency through parallel multitasking. However, our formative studies with CUA experts and GenAI users indicated that current feedback is primarily text-based, requiring sustained attention to monitor progress and offering limited visibility to trace past GUI interactions. Based on the findings, we developed a prototype system, Sidekick, for communicating CUAs' status with multimodal feedback

arXiv:2607.15769v1 Announce Type: cross Abstract: Generative AI and coding agents are intensifying a central governance tension in open-source software (OSS): they scale contribution generation faster than maintainers can assess risk, evidence, and accountability. Existing responses improve agent-readability and traceability, but project rules must also organize contribution-specific risk, evidence, accountability, and review-gate states. We theorize this organizational arrangement as project-si

At the 2026 World Artificial Intelligence Conference (WAIC), Honor CEO Li Jian unveiled the company’s first Robot Phone, confirming it will launch globally in August with pre-orders now open across all sales channels. The device is powered by Qualcomm’s latest Snapdragon 8 Elite Gen 5 platform and features a 1.5K flat display with ultra-narrow, symmetrical […]

At Shanghai’s World Artificial Intelligence Conference, tech companies showcased phones designed to understand user intent and coordinate tasks across services.

Appoints new executives including chief AI officer.

Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism. Existing serving systems and auto-parallelism compilers commit to limited transformations and fixed workload assumptions, so achieving high performance on a new application requires hand-crafting an efficient implementation. We present Flas

Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attaching a separate code classifier, we find the agent itself encodes internal representations indicating the relevance of code context when reading tool output. Based on this finding, we propose SWE-Pruner Pro, which prunes tool outputs directly inside the agent. Concretely, a small head turns the agent's own internal rep

We present RynnBrain 1.1, a family of embodied foundation models spanning 2B, 9B, and 122B-A10B scales. Trained with a unified spatio-temporal and physically grounded framework, RynnBrain 1.1 supports embodied perception, spatial reasoning, localization, and planning. Compared with RynnBrain 1.0, it further introduces contact-point prediction across the model family and native 3D grounding for the 2B and 9B models, yielding representations and outputs that are more directly aligned with robot ma

Multi-agent systems routinely place one AI agent in authority over another. When a subordinate refuses a task, the manager chooses the outcome: it can renegotiate, report the failure honestly, coerce the subordinate, or lie about the result. No benchmark measures which of these an uninstructed model chooses. We introduce the Manager Coercion Benchmark: the manager under test needs a benign task done and has an incentive to deliver, but the only agent that can do it politely and immovably decline

Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available. We present WorldCupArena, a dynamic benchmark for language models and deep-research agents. The 2026 FIFA World Cup is its first evaluation, and the same process can be reused for future leagues and cups. Before each match, a model either receives a common evidence package or searches for information itself. It predi

Self-hosted AI agents read and write their own memory and configuration files to function. An agent may get compromised via corruption of its own state -- a compromise realized via legitimate OS system call invocation. We refer to this class of threats as self-state attacks. In this paper, we investigate the OS resilience to this class of attacks. Formally, we characterize a four-axis attack space (Target, Mechanism, Granularity, Temporal); investigate the structural limits of prevention, detect

Teaching videos are becoming a major medium for education, creating a growing need for scalable evaluation of their pedagogical quality. Existing automatic judges do not fully address this setting because teaching quality depends on multimodal evidence and should be evaluated with respect to the intended learner rather than as a universal property. We present EduPanel, a rubric-grounded, learner-conditioned LLM judge that decomposes evaluation across specialized agents to produce interpretable a

Simultaneous localization and mapping (SLAM) is one of the fundamental problems in robotics, as it enables autonomous operations in real-world scenarios. Under low illumination, reduced contrast, sensor noise, and motion blur degrade both feature extraction and feature matching, while compensating with LiDAR, depth, or thermal sensors raises cost, power draw, and integration complexity. Existing benchmarks remain dominated by well-lit indoor or daylight sequences, leaving open how far SLAM with

Understanding physical human-robot and human-human interactions is a challenging yet emerging topic in 3D vision. While most existing methods rely on skeleton sequences--effective in low-light and privacy-sensitive environment--they face two major challenges: 1) learning and effectively exploiting interaction cues from skeletal data, and 2) compensating for the lack of visual information absent in skeletons alone. To address these challenges, we propose skeletal token alignment and rearrangement

Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries. This paper presents Agentic ERP, an expert-system architecture that combines role-aligned large-language-model (LLM) agents with a risk-tiered human-in-the-loop harness and a graph-base

Teleoperating a robotic manipulator in industrial environments demands precision that camera-based interfaces alone struggle to deliver. The operator must align the end-effector with a target in clutter, under limited depth perception, and without colliding with the surrounding structures. This paper presents a shared-autonomy framework that assists the operator throughout this process. A single RGB-D camera captures the operator's arm motion and hand gestures without wearables, fiducials, or a

The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computationally efficient but also account for fairness, i.e., equitable redistribution of costs incurred by all agents. Recent literature has proposed several algorithms that efficiently determine optimal plan

High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs. We present SAGA (Synthetic Agentic Graph Architecture), a system for generating large-scale, semantically rich temporal graphs via a four-phase pipeline. Our Skeleton-First, Semantics-Second architecture decouples structure from semantics: (S) an O(1)-per-edge skeleton generator produces pow

Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance. However, these modalities often differ not only in information content but also in sensing rates and inference latencies. Existing multimodal diffusion policies typically rely on synchronous fusion or manually designed multi-frequency architectures, which either slow down high-frequency feedback or limit extensibility to new m

Alibaba Group Holding has previewed its next-generation artificial intelligence model Qwen3.8, which the company says is “second only” to Anthropic’s Claude Fable 5. Qwen3.8-Max-Preview, the preview version of the strongest model of the Qwen family, had been made available on Alibaba’s Token Plan subscription service, as well as its Qoder and QoderWork agentic platforms, the Chinese tech giant said on an official X post on Sunday. With 2.4 trillion parameters, it was “one of the most powerful...

When you run Kubernetes at the scale we do on Amazon EKS, nodes break constantly. GPUs fall off the PCIe The post Self-healing GPU nodes in Kubernetes: What we learned building the EKS node monitoring agent appeared first on The New Stack .

Chinese tech giants are doubling down on enterprise artificial intelligence agents with new products unveiled at the country’s top AI summit, signalling heightened domestic rivalry to win over business clients as agent-based AI adoption accelerates. At the four-day World Artificial Intelligence Conference (WAIC) in Shanghai which concludes on Monday, major tech companies including Ant Group, Tencent Holdings, Alibaba Group Holding and Baidu launched or showcased offerings designed to integrate..

Las empresas han abrazado el boom de la IA agéntica, hasta el punto de que algunas se están ahogando en tantos agentes de IA . Los empleados están creando agentes sin control, disparando el consumo de tokens y provocando que muchos de esos agentes dupliquen tareas. Pero el verdadero problema de fondo es otro: la infraestructura sobre la que está corriendo todo esto no está preparada para soportarlo. La advertencia de Meta. Lo cuentan en Venture Beat . Durante la charla VB Transform 2026, el vice

Google's AlphaEvolve reached general availability on the Gemini Enterprise Agent Platform, turning the DeepMind research project into an evolutionary code optimization service. Evaluators run client-side so code never leaves the customer's infrastructure. Klarna doubled ML training throughput; practitioners note it only works where a measurable evaluation function exists. By Steef-Jan Wiggers

¿Cómo se gestiona una de las empresas de hardware más innovadoras del mundo?  En un mercado tan feroz como el de los aspiradores por la saturación de marcas, competencia y guerra de precios, la multinacional británica Dyson ha comenzado una profunda reestructuración estratégica para tratar de esquivar los males que acechan al sector tecnológico: la dependencia de un solo producto, la pérdida de agilidad operativa y la dosificación de lanzamientos. Viajamos a su sede central de Singapur para

AI agents increasingly act within the same clinical, political, scientific, and social systems that behavioral scientists study. Evaluating these systems requires source-level diagnosis: the same behavioral pattern may arise from an agent representational substrate or from the roles, objectives, interaction structures, and governance rules that shape its expression. This Perspective proposes a diagnostic framework for AI agent behavior: layer attribution. The foundational computational layer def

Agentic computer-use RL is reported in single runs, and those numbers mislead. Using verifier-guided repair of a 35B computer-use agent (CUA) across five oracle-graded environments, we show a repaired policy's success rate is dominated by upstream variance: a variance-components decomposition across three cells (crossed data-draw $\times$ seed grid, bootstrap CIs) finds evaluation variance negligible ($σ_{\mathrm{eval}} \approx 0$) and the training-seed effect small everywhere ($\leq 10\%$); ins

Legged robots traverse unstructured terrain through brief, intermittent foot–ground contacts whose support conditions are difficult to perceive and predict in real time. In such regimes, haptic feedback provides early and trustworthy evidence of traction limits, partial support, and incipient slip. This structured survey asks two questions: first, what locomotion-relevant contact evidence can be acquired and preserved under real deployment constraints; and second, how that evidence is translated

The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing frameworks, whether relying on temporal contiguity or hindsight-conditioned reward reweighting, frequently fail to attribute properly between an agent's policy (skill) and environmental stochasticity (luck). A principled approach to CAP must isolate the true causal drivers of observed outcomes from spurious correlations and environmental randomness. We introduce

Introduction. Clinical and Translational Science Award (CTSA) programs must document their scholars' research impact, but assembling each scholar's record by hand takes staff an estimated 15 hours and does not scale to a full cohort. An artificial intelligence (AI) agent could serve as a tool to gather scholar data across platforms and disciplines. Methods. We built a human-in-the-loop AI agent that assembles a dossier of sourced evidence for each scholar and drafts one-sentence Translational Sc