Markov Models
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
Neural Information Processing Systems
In this section, we provide brief background on information theory and details on our notation.
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Measuring Goal-Directedness
Neural Information Processing Systems
In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],
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Forward-BackwardLatentStateInferencefor HiddenContinuous-Timesemi-MarkovChains
Neural Information Processing Systems
Thepresented equations can be efficiently solved using well-known numerical methods.
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