Unifilarisation of stochastic Mealy machines is an instance of coalgebraic determinisation over monads with support structure, producing causal stochastic behaviours rather than Moore-style output distributions.
World Discovery Models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on the new information---humans can discover their world. The outstanding ability of the human mind for discovery has led to many breakthroughs in science, art and technology. Here we investigate the possibility of building an agent capable of discovering its world using the modern AI technology. In particular we introduce NDIGO, Neural Differential Information Gain Optimisation, a self-supervised discovery model that aims at seeking new information to construct a global view of its world from partial and noisy observations. Our experiments on some controlled 2-D navigation tasks show that NDIGO outperforms state-of-the-art information-seeking methods in terms of the quality of the learned representation. The improvement in performance is particularly significant in the presence of white or structured noise where other information-seeking methods follow the noise instead of discovering their world.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ICL-derived intrinsic rewards are biased in general MDPs but asymptotically match true learning progress in non-temporal settings, with supporting experiments.
citing papers explorer
-
Can In-Context Learning Support Intrinsic Curiosity?
ICL-derived intrinsic rewards are biased in general MDPs but asymptotically match true learning progress in non-temporal settings, with supporting experiments.