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Short-context models can achieve superior reasoning performance by leveraging long-context teacher models through innovative token alignment and training strategies.
The traditional linear correlation between language model perplexity and ASR word error rate breaks down in modern end-to-end systems, revealing the critical role of internal language modeling.
CHAUN achieves a remarkable 25.6% improvement in QINI scores, redefining the benchmarks for uplift modeling in the presence of unobserved confounding.
Action-conditioned world model embeddings can revolutionize failure detection in long-horizon robotic tasks, achieving reliable monitoring without dense annotations.
