TC260 Secretariat released the Cybersecurity Standards Practice Guide—Security Guidance for the Deployment and Use of AI Agents, setting lifecycle security requirements (pre-deployment assessments, hardening, least-privilege, audit logs, and secure decommissioning).
The state government plans to unveil its draft “AI Policy 2026” at a national stakeholders’ consultation in Delhi on July 8–9. The article describes a proposed five-year framework for responsible AI integration across public services.
Seattle City Council passed a unanimous year-long moratorium on construction of new datacenters, framed as targeting electricity-intensive AI infrastructure. The pause is intended to allow drafting of datacenter regulations and protect residents from environmental and electricity-bill impacts.
IT Secretary S Krishnan said India is moving toward a dedicated regulatory framework for AI and that MeitY will prepare draft AI legislation when the time is right. The statement indicates intent to develop a separate AI law beyond existing IT rules.
China’s “Interim Measures for the Administration of AI Personified Interaction Services” take effect July 15, regulating AI personified interaction/agent services. The article links the effective date to ByteDance and Alibaba shutting down agent features on July 15.
Motion planning algorithms should be evaluated in human-in-the-loop environments to ensure they produce safe and efficient behaviors during interactions. However, existing simulation platforms often rely on recorded datasets, lack dedicated interfaces for real-time human interaction, or remain weakly integrated with an autonomous driving ecosystem. Moreover, many human-in-the-loop simulators are c
Offline reinforcement learning (RL) holds significant potential for crowd robot navigation in human-robot coexistence applications. However, the inherent complexity of pedestrian motion renders the design of effective reward functions for promoting socially compliant robot behaviors a persistent challenge. This paper proposes a Social Preference Learning for Crowd Robot Navigation (SPLC) algorithm
Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance. We present MMBench-Live, a continuously evolving multimodal benchmark built by a multi-agent-driven automated pipeline. Our framework treats benchmark evolution as task-guided dataset co
Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices. Despite decades of research, OL lacks a shared infrastructure for systematic evaluation and ontology access. This absence has hindered progress and fragmented research, leaving the central challenges of OL largely unaddress
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal understanding, yet their enormous parameter scale and cross-modal computation incur substantial memory and latency overhead, severely limiting real-world deployment on resource-constrained devices. Binarization offers an attractive solution by drastically reducing storage and computational costs. However, existing
Capital remains concentrated in compute and adjacent infrastructure, with large late-stage checks and public-market exits still setting the tone. The mix signals continued investor preference for scale, hardware leverage, and platform control.
Capital remains concentrated in compute and adjacent infrastructure, with large late-stage checks and public-market exits still setting the tone. The mix signals continued investor preference for scale, hardware leverage, and platform control.
Capital remains concentrated in compute and adjacent infrastructure, with large late-stage checks and public-market exits still setting the tone. The mix signals continued investor preference for scale, hardware leverage, and platform control.
Named moves continue to matter most when they touch core AI leadership or field operations. Departures and hires at major institutions can reshape how data, product, and go-to-market teams are organized.
Named moves continue to matter most when they touch core AI leadership or field operations. Departures and hires at major institutions can reshape how data, product, and go-to-market teams are organized.
Named moves continue to matter most when they touch core AI leadership or field operations. Departures and hires at major institutions can reshape how data, product, and go-to-market teams are organized.
Benchmark activity is centered on a single model family across multiple tests, which matters because clustered scores reveal where capability is being tracked most closely. Even without a leaderboard shake-up, the evaluation surface is still being refreshed.
Benchmark activity is centered on a single model family across multiple tests, which matters because clustered scores reveal where capability is being tracked most closely. Even without a leaderboard shake-up, the evaluation surface is still being refreshed.
Benchmark activity is centered on a single model family across multiple tests, which matters because clustered scores reveal where capability is being tracked most closely. Even without a leaderboard shake-up, the evaluation surface is still being refreshed.