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Bridging the gap between inference and adaptation in VLMs could lead to significant performance boosts by ensuring robust pseudo-labels that accurately reflect sample-level relationships.
Long-horizon LLM agents can achieve 96.9% task success by learning to adapt their external execution support through trainable harness policies.
The study reveals that maintaining answer accuracy in large models can come at the cost of losing critical reasoning support, highlighting a significant "answer-evidence gap" in KV cache compression.
SafeImpute not only delivers accurate clinical data imputations but also ensures that only reliable results are released, controlling for unacceptable errors.
