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Retaining over 64% of in-context learning gains without further environment interactions could revolutionize how agents learn from their experiences.
Unearthing two distinct planning competencies in LLMs reveals that scaling up models enhances operational reasoning but leaves structural enumeration largely unchanged.
Despite advances in vision-language models, reasoning across sparse, multi-view observations remains surprisingly unsolved, with current models barely outperforming random guessing on a new benchmark.
Open-source VQ-VA models just got a massive boost: a new dataset and benchmark close the gap with proprietary systems on visual question-visual answering.
