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MatcherCompass is presented, a deployment-aware benchmark for choosing local feature matchers in field robotics and provides measured evidence for choosing matching pipelines that fit a robot's sensing conditions and computing hardware.
DPO-tuned LLMs systematically achieve less than a third of requested emotional intensity because preference optimization cannot reward behavioral extremes that base models never sample.
Vetoing unstable tokens can boost dMLLM reasoning accuracy by up to 9% without any extra training costs.