Xiaoya Ma

Ke Ma, Francesca Valsecchi, Yuchen Tan et al.

Temporary luxury branded events run on short cycles and bespoke builds that accelerate material churn. We present a circular phygital product-service system that operationalises the circular economy (CE) through a 4R frame (Refuse, Reduce, Reuse, and Recycling) across warehouse-to-event journeys. Developed via a multi-method design inquiry with a tier-1 contractor, the system couples physical touchpoints (reusable fold-flat transit boxes, adjustable racking, standard labels) with digital orchestration (a live digital warehouse, list-based outbound/inbound workflow, and a sustainable materials library). The architecture aligns roles and decisions, protects and identifies assets, and makes reuse the default under luxury brand constraints. By embedding traceable actions and CE-aligned rules into everyday handoffs, the PSS shifts procurement, storage, dispatch, return, and redeployment toward value retention. The contribution is a replicable, practice-ready route from circular intent to operational change in branded environments, advancing responsible retail without compromising speed or aesthetic standards.

Shanghai AI Lab, Yicheng Bao, Guanxu Chen et al.

We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framework, which incorporates large-scale, progressive, safety-oriented reinforcement learning post-training, supported by a suite of multi-principled verifiers. Unlike previous alignment methods such as RLHF that simply learn human preferences, SafeLadder enables SafeWork-R1 to develop intrinsic safety reasoning and self-reflection abilities, giving rise to safety `aha' moments. Notably, SafeWork-R1 achieves an average improvement of $46.54\%$ over its base model Qwen2.5-VL-72B on safety-related benchmarks without compromising general capabilities, and delivers state-of-the-art safety performance compared to leading proprietary models such as GPT-4.1 and Claude Opus 4. To further bolster its reliability, we implement two distinct inference-time intervention methods and a deliberative search mechanism, enforcing step-level verification. Finally, we further develop SafeWork-R1-InternVL3-78B, SafeWork-R1-DeepSeek-70B, and SafeWork-R1-Qwen2.5VL-7B. All resulting models demonstrate that safety and capability can co-evolve synergistically, highlighting the generalizability of our framework in building robust, reliable, and trustworthy general-purpose AI.