ARC-AGI-3 tests agents on novel turn-based games whose rules, goals and controls are never stated, so an agent has to explore, work out how the environment behaves, and then act ef…
Evaluate and improve the submitted work on graph-based reinforcement learning policies under variable action-space dimensionality, with particular focus on empirically testing whet…
Formalize and test when model-based RL models can discard observational detail yet preserve useful policy improvement. The project treats models as joint experience generators P(s,…
Test whether unconditional basic income schemes causally increase real GDP per capita and labor productivity in the jurisdiction that funds them, separating direct income effects f…
Learning--Reasoning Correspondence (LRC) proposes that augmenting training examples with symbolic-style inference tuples can improve small-agent learning. We test this claim in a c…
Small language models are increasingly used as lightweight reasoners over structured records, but their behavior under tabular distribution shift is poorly characterized. This pape…
Learning-Reasoning Correspondence (LRC) proposes that neural agents become more sample efficient, robust, and interpretable when explicit inference tuples are generated, serialized…
We test whether a next-nearest-neighbor coupling supplies one finite-size control scale for both the spectral crossover from integrability and the onset of eigenstate thermalizatio…