TL;DR
The results indicate that statistical plausibility and structural admissibility are distinct reliability properties and that symbolic constraint handling becomes more valuable as graph-structural complexity increases.
This work proposes Graph Diffusion Counterfactual Explanation via Inversion (GDCE-I), a discrete denoising diffusion model with a novel discrete inversion scheme that enables distribution-aware edits leveraging the whole domain edit space and qualitatively shows that GDCE-I attains interpretable in-distribution solutio...
To address the systemic flaw of current large-scale language models exhibiting logical deduction failures when faced with out-of-distribution test data and synthetic counterfactual interference, this paper proposes a PertAdapt causal adaptive framework integrating directed acyclic graph constraints. By constructing qua...
This work introduces a Graph Neural Network (GNN)-based difficulty estimator that jointly captures topological and semantic aspects of violations, enabling the system to distinguish between cases suitable for automatic repair and those requiring human input.
We ask whether physically meaningful correspondences discovered inside a trained scientific model remain meaningful outside the conditions under which they were discovered. We introduce GIIT (Graph-based Inspection and Intervention Tool), which represents governing physics as a computational physics dependency graph, m...
FlowNeg is introduced, a context-conditioned hierarchical generative flow network that amortizes reward-proportional sampling without normalizing a composite reward over the entity set: given a positive triple and corruption side, it selects a type, then an entity.
Diffusion language model (dLLM) compression faces a known challenge because calibration is typically performed on clean, fully visible activations, whereas inference traverses partially masked intermediate states. For low-rank compression, this raises two questions. First, can low-rank optimality still be characterized...
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