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Block Verification-aware loss (BV loss), a training objective designed to maximize the expected acceptance length of a drafted sequence, outperforms tokenwise acceptance objectives such as TV loss and LK loss, and its gains extend to token verification and greedy decoding.
Undesired information such as harmful content and private data propagates through Multilingual Large Language Models (LLMs) via direct training and indirect cross-linguistic spread. Multilingual Machine Unlearning (MMU) aims to remove such information, yet its evaluation remains underexplored, leaving unclear whether u...
FaVOR (Factor Validation through Observable Reasoning), an agentic framework that restructures factor mining around hypothesis-level evidence rather than return outcomes, shows that hypothesis-grounded factor discovery produces signals that are interpretable by construction, regime-robust, and economically faithful.
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