arXiv:2605.09870v1 Announce Type: cross
Abstract: We propose SVAR-FM (Structural VAR with Flow Matching), a framework for time series causal discovery that treats a physics-based simulator as a mechanical realization of Pearl’s do operator. Clamping a…
arXiv:2605.07600v1 Announce Type: cross
Abstract: Recent methods for improving LLM mathematical reasoning, whether through MCTS-based test-time search or causal graph-guided knowledge injection, cannot identify which concepts causally contribute to a …
arXiv:2605.07467v1 Announce Type: cross
Abstract: Existing interventional causal discovery methods — IGSP, DCDI, ENCO — assume causal sufficiency (no latent confounders) and rely on virtual interventions in synthetic simulators. In AI-for-Science se…
arXiv:2605.07485v1 Announce Type: cross
Abstract: Extrapolation to out-of-distribution conditions is a fundamental challenge for physics-constrained deep generative models. Existing methods apply physical constraints as a single static regularization …