The Council of the EU gave final approval to a regulation streamlining AI Act implementation, materially shifting the near-term European governance baseline: stand-alone high-risk AI system obligations move to December 2, 2027, embedded high-risk systems to August 2, 2028, while targeted bans on AI systems generating non-consensual sexual/intimate content or AI-generated CSAM, including nude-image or clothes-removal systems, are set for December 2026. It also delays national AI sandboxes to August 2, 2027, shortens the deadline for synthetic-content transparency solutions to December 2, 2026, and clarifies AI Office supervision of certain GPAI-based systems.
The Council of the EU gave final approval to a regulation streamlining AI Act implementation, materially shifting the near-term European governance baseline: stand-alone high-risk AI system obligations move to December 2, 2027, embedded high-risk systems to August 2, 2028, while targeted bans on AI systems generating non-consensual sexual/intimate content or AI-generated CSAM, including nude-image or clothes-removal systems, are set for December 2026.
The broader significance is that the EU is simultaneously delaying broad high-risk compliance while accelerating specific harm controls.
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Consilium
The European Commission has issued operational guidance for Article 50 of the AI Act before its transparency obligations begin applying on August 2, 2026. Providers must disclose when people are interacting with systems such as chatbots, agents, or avatars and make generative outputs detectable through machine-readable marking; deployers must disclose emotion-recognition or biometric-categorization uses and clearly label deepfakes and certain AI-generated public-interest text when it lacks human review or editorial control.
The NUS/Harbin-led team identifies a domain-specific form of generative-AI hallucination: molecular candidates can receive strong predicted binding scores while violating basic chemistry or producing physically impossible atomic arrangements. Its DrugRPG framework incorporates chemical-foundation-model priors and differentiable physical constraints during molecule generation, reducing severe steric clashes by 65.4% relative to the reported state-of-the-art baseline and increasing by 28.6% the share of generated candidates meeting combined potency, stability, and synthetic-feasibility criteria.
The authors warn that AI systems can amplify stale annotations, incorrect database relationships, inconsistent standards, and weak provenance when they continuously harvest scientific repositories that were designed as comparatively static resources. They extend the FAIR principles with COPE—Comparable, Organized, Predictive, and Engaged—calling for iterative updates, version tracking, uncertainty estimates, machine-actionable standards, and community validation whenever AI-supported analyses generate new knowledge.