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Selecting relevant evidence spans for test-time training can boost long-context LLM accuracy by up to 15%.
Pruning redundant reasoning in teacher traces can boost recommendation model performance while streamlining output length.
Achieving near-autoregressive accuracy while boosting decoding speed by over 2.4 times could redefine efficiency benchmarks in generative reasoning tasks.
