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An uncertainty-guided progressive contraction framework concentrates reasoning supervision and optimization on valuable trajectories to reduce training costs while maintaining reasoning performance.
Source: arXiv
https://arxiv.org/abs/2610.12933
#MachineLearning
Emotion classifiers are routinely applied beyond their training data on the assumption that same-named labels mean the same thing across resources. On identical inputs, one fear classifier flags 71% as fearful and another 8%.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.12841
#MachineLearning
KV-Repurpose is introduced for evaluating non-prefix KV-cache reuse in large language model serving. It achieves a 63.9% token-level cache hit rate under a Zipfian RAG-style workload, compared to 3.3% for prefix caching.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.13060
#MachineLearning
A new benchmark introduces 5,525 document-grounded question-answer pairs over 415 open-source geopolitical and strategic intelligence reports to evaluate retrieval and generation systems.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.12617
#MachineLearning
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