A conceptual study proposes a four-layer architecture combining provenance, disclosure, privacy and safety, and accountability to govern AI-generated content across institutional workflows.
Unlike traditional SaaS, AI agents deliver uncertain value at a cost that keeps shifting, and that combination breaks the pricing logic SaaS was built on.
How AI agents are changing enterprise operations and why leaders must rethink business strategy, productivity, human oversight, governance, and accountability.
DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete…
Omni reference-to-video (R2V) generation is emerging as a general paradigm that enables flexible video customization from diverse and compositional visual references. However, existing benchmarks fall short of these emerging capabilities: their test cases cover only a limited…
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range…
Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. Using Value Sensitive Design, we analyzed, with LLM assistance, 73,093 first-person Reddit posts about using OpenClaw, each for…
Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising the question of whether this scale can be exploited to learn general-purpose neural representations transferable across diverse downstream…