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Abstract future prediction in latent space significantly boosts LLMs' chemical reasoning and molecular outcome prediction capabilities.
Chemists can now precisely steer retrosynthesis to favor specific precursor sets, moving beyond simple reaction recovery to nuanced chemical design.
A single generative flow model can dynamically transform molecular graphs, handling variable size and connectivity changes for retrosynthesis without explicit edit ordering.
Visual representations of graphs can transform how models reason and learn, bridging a critical gap between symbolic and visual understanding in AI.
Repurposing speculative decoding backbones for output-length prediction slashes short-request latency by nearly 35%.
Treating a frozen large language model as the decision-maker in molecular design dramatically enhances candidate selection and optimization efficiency.
Turns out, MLLMs struggle with manufacturing tasks not because they can't "see," but because they lack the domain-specific knowledge to understand what they're looking at.
