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Understanding a dynamical world calls for more than a latent state that summarizes its observations: the state should also be organized into the factors that stay shared across related observations and the factors that vary between them. For example, a robot pushing a cube to a goal should take the same action when the...
A unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning within a single framework is proposed and a novel explanation framework is introduced that learns a parametric transformation network to construct explanation-embedded instances.
TALRanker is a novel framework that formalizes pointwise relevance scoring as an agentic Markov decision process that achieves state-of-the-art performance across standard and reasoning-intensive retrieval benchmarks, matching throughput with pointwise rerankers while outperforming parameter-heavy reasoning models.
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