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Tag: #chemistry-ai4 results

MMORF: Multi-Agent Retrosynthesis Framework

MMORF: Multi-Agent Retrosynthesis Framework

MMORF is a modular multi-agent framework for multi-objective retrosynthesis planning in chemistry, balancing quality, safety, and cost. It enables building systems like MASIL, which excels in soft constraints, and RFAS, achieving 48.6% success on hard constraints, outperforming baselines on a 218-task benchmark. Code and data are open-sourced.

Latent Flows Model Reaction Trajectories

Latent Flows Model Reaction Trajectories

LatentRxnFlow predicts reactions as continuous latent trajectories via Conditional Flow Matching from reactant-product pairs. Offers SOTA USPTO accuracy with trajectory diagnostics and uncertainty estimation. Enables error mitigation and reliable predictions.

ArXiv AIResearchFeb 12#research#latentrxnflow#v1
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Cybersecurity Must Protect Physical Reality

As industrial systems, infrastructure, robots, and AI agents gain the ability to change physical conditions, cybersecurity must protect actions and outcomes—not only data and access. The article argues that future defenses need to evaluate whether an authorized action is appropriate for the current environment, state, and safety boundaries.