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Reasoning segmentation models stumble by cramming semantic interpretation and spatial localization into a single prompt token; decoupling "what" from "where" hits state-of-the-art grounding while fine-tuning less than 0.4% of the base MLLM.
G2D boosts zero-shot image classification accuracy by up to 27.42 percentage points by effectively combining generative verification with discriminative retrieval.
Achieving coupled-cluster accuracy for molecular properties with the computational cost of a single DFT calculation could revolutionize molecular modeling in chemistry.
CodeSpec transforms feature development by ensuring that LLM-based code agents produce reliable and verifiable functional chains, achieving up to 70.7% pass rates on complex benchmarks.
