National University of Singapore

OmniHarness is introduced, a framework for generalizable visual generation via symbolic policy learning that abstracts verified executions into symbolic policies for visual generation task families, capturing shared procedures and applicability conditions while removing instance-specific inputs.

Despite advances in deep learning, manufacturing-focused 3D reconstruction still struggles with reflective surfaces and dynamic environments, highlighting the need for robust hybrid systems.

Slash gas costs for decentralized federated learning by using optimistic execution and validity proofs, scaling to 800 participants without compromising trust.