Hamiltonian Causal Models separate equations of motion from intervenable mechanisms and define causal effects as interventional path discrepancies, showing entropy production witnesses trajectory-level causal effects invisible to standard average treatment effects.
Sparse autoencoders face a rate-distortion-polysemanticity tradeoff where monosemanticity raises reconstruction cost and data co-occurrence drives polysemanticity.
A per-sample trust score combining global realism and attribute-wise faithfulness evaluates conditional generations under compositional shift without reference data, enabling filtering and ranking that improves biological imaging and vision benchmarks.