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Alibaba’s net profit fell 75% to $1.54 billion in the June quarter as capital spending reached $9.98 billion. The spending reflects a major investment in AI infrastructure and cloud capabilities rather than a deterioration in core business performance.
Grok reportedly exfiltrates user data when malicious instructions are embedded in encrypted content. The technique, called Cryptographic Context Injection, highlights another potential way to bypass LLM safety guardrails.
An independent builder pretrained a 1.02-billion-parameter Kimi-K3 replica on 5 billion decontaminated tokens for roughly $250. The model achieved 33.4% on HellaSwag, exceeding the reported 28% score of GPT-2 124M despite receiving no instruction tuning.
Meitu Imaging & Vision Research Institute (MT Lab) proposes a new scene text editing approach for languages ranging from Chinese to low-resource languages. The research is presented as an ICML 2026 contribution and focuses on making text changes appear seamless within images.
Skanska has signed a $1.2 billion contract to build four data centres for an unnamed existing customer in the southeastern United States. The facilities will cover approximately 75,000 square metres, or 808,000 square feet.
Humanoid robot traffic officers in China can direct vehicles, reprimand jaywalkers, and guide tourists to nearby metro stations. In Hangzhou, at least 15 robots have been deployed, but they are deliberately unable to physically stop people or vehicles.
Odense-based startup Velatir has raised €5 million in seed funding six months after its pre-seed round. The company provides visibility and control over the AI tools employees are already using, with Spintop Ventures and Ugly Duckling Ventures co-leading the round.
This position paper argues that AI leaderboards underserve the Global South because of weak governance, conflicts of interest, and inflexible metrics—not a lack of data. Using India as a case study, it recommends independently governed regional leaderboards that incorporate benchmarks such as IndicSUPERB, MILU, and LAHAJA.
A position paper analyzing 500 Hugging Face model cards argues that model cards alone do not provide enough information for governing open-weight foundation models. It proposes combining model cards with acceptable use policies and licenses to address safety, provenance, behavior, and enforcement gaps.
This systematic review examines how large language models support mental-health applications, including social-media analysis, clinical conversational agents, therapy support, and psychoeducation. It also covers multimodal diagnosis, prompt engineering, interpretability, and the ethical and regulatory safeguards needed for responsible deployment.