The European Commission published a voluntary Code of Practice on marking and labelling AI-generated content to help providers and deployers meet AI Act transparency obligations starting 2 August 2026.
Canada has introduced proposed legislation to ban social media for children under 16 and to regulate AI chatbots. The Reuters item indicates a legislative introduction rather than enacted law.
A letter from the DSIT Secretary of State to Ofcom sets out growth goals for Ofcom. This is an official government communication that may influence Ofcom’s regulatory priorities.
U.S. Senator Ted Budd and bipartisan senators introduced the FARM AI Act to expand access to technology for American farmers. This is a proposed legislative action, not enacted law.
The Supreme Court of India has released draft AI rules for courts, requiring lawyers to disclose the use of AI in pleadings. This appears to be a draft instrument subject to further process.
Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data. Such mismatches are especially problematic in domain adaptation tasks, where only a few reference examples are available and retraining the diffusion model is infeasible. Existing inf
Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determining the optimal split point, meaning the layer where the model is divided still remains a critical challenge, especially when clients have heterogeneous hardware capabiliti
With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness. To handle the inherently personal and context-dependent nature of real-world requests, such agents must ground their generation in device-resident personal context. However, under tight memory budgets, the core bottleneck is what to
Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)- based forecasters have made promising advancements. Despite their effectiveness, existing methods often lack explicit experience accumulation and continual evolution. In this work, we propose MemCast, a learning-to-memory framework that reformulates TSF as
When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck. Existing contrastive decoding methods follow a \emph{context-aware} paradigm that unilaterally amplifies context over parametric priors, overwriting correct priors when the context is erroneous. We generalize this to the \textbf
Capital remains concentrated in infrastructure and robotics, with public-market exits at the top and a large strategic round in embodied AI below. The mix shows money still favoring compute, hardware, and systems that can absorb industrial partners.
Capital remains concentrated in infrastructure and robotics, with public-market exits at the top and a large strategic round in embodied AI below. The mix shows money still favoring compute, hardware, and systems that can absorb industrial partners.
Capital remains concentrated in infrastructure and robotics, with public-market exits at the top and a large strategic round in embodied AI below. The mix shows money still favoring compute, hardware, and systems that can absorb industrial partners.
The benchmark feed is thin but still useful: it captures a live snapshot of agent performance on a general reasoning suite. The spread between entries underscores how uneven current systems remain on the same evaluation surface.
The benchmark feed is thin but still useful: it captures a live snapshot of agent performance on a general reasoning suite. The spread between entries underscores how uneven current systems remain on the same evaluation surface.
The benchmark feed is thin but still useful: it captures a live snapshot of agent performance on a general reasoning suite. The spread between entries underscores how uneven current systems remain on the same evaluation surface.