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Large Language Models (LLMs) have shown impressive performance on a wide range of generative tasks. Yet their probabilistic nature makes them, in isolation, fundamentally unsuited for industrial product configuration, where outputs must be syntactically valid, semantically consistent with a knowledge base of hundreds o...
LLMZero, an agentic system that optimizes training trajectories via tree search by diagnosing pathologies at each checkpoint and proposing coordinated multi-parameter transitions, discovers strategies that improve over the base model and over grid search and over grid search, consistently outperforming random search an...
SD-MAR (Synthetic Data for Multi-image Analytical Reasoning) is introduced, a framework for training and evaluating VLMs on multi-image analytical reasoning that constructs paired visual scenarios through controlled perturbations and generates reasoning tasks spanning semantic change attribution and quantitative compar...
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