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AI agent harnesses can be optimized for performance-cost trade-offs without ground truth, yielding multiple deployment options.
LLM pruning can be task-aware: DIPrune's dual importance scoring jointly optimizes static feature saliency and dynamic semantic evolution, outperforming prior methods on LLaVA and Qwen-VL.