Unverified50% confidenceFactExact time
In TAPES's discrete latent variable model, given the value of the latent variable, the prediction targets are conditionally independent; after marginalizing out the latent variable, a conditional co-occurrence structure emerges among the targets.
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11/4/2026
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UnverifiedTAPES pretrains with a joint likelihood parameterized by shared discrete latent variables to explicitly model the conditional co-occurrence relationships among prediction targets.79% similarUnverifiedTraditional per-target prediction losses implicitly assume that prediction targets are mutually independent and do not explicitly model the conditional co-occurrence relationships among targets.79% similarUnverifiedNon-autoregressive generative models assume that positions or levels are conditionally independent to some degree, so that multiple outputs can be produced at once or hierarchically in parallel, but they often lose fluency because dependencies between tokens are ignored.74% similarUnverifiedThe target score identity expresses the score function as a self-consistent relation between the current model prediction and the gradient of the unnormalized density, making it possible to construct a training objective without external training data.71% similarUnverified模型参数需要同时服务于记住具体事实和泛化推理规则两个相互竞争的目标，会争夺参数空间70% similar
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