A coherent stack toward human-like perception, structure, and continual learning.
Problem: Models recognize objects but fail to reason over hierarchy, attributes, and affordances.
Key idea: A cognitively grounded taxonomy and evaluation of scene reasoning.
Outcome: Diagnostic signals that reveal where perception breaks and how structure helps.
Problem: Parts are segmented without semantics or named without structure.
Key idea: Joint part segmentation and naming as set alignment with scalable annotation.
Outcome: Reusable, consistent part semantics that scale under constrained data.
Problem: Perception lacks grounded 3D world models for reasoning and synthesis.
Key idea: Analysis by synthesis pipelines that reconstruct structured 3D worlds.
Outcome: World models that support part-level reasoning and compositional inference.
Problem: Continual adaptation is compute-heavy and prone to forgetting.
Key idea: Shared low-dimensional update geometry for reuse, merging, and strict continual learning.
Outcome: Sustainable adaptation that scales beyond privileged compute and data.