5 papers
Pretrain on Small Synthetic Data, Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction
Yin Jun Phua
Logical rule induction seeks interpretable rules that transfer across propositional schemas. This requires respecting symmetries: atom naming, example order, polarity flips, and la…
A Foundation Model for Zero-Shot Logical Rule Induction
Yin Jun Phua
Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and req…
Can Transformers Learn to Verify During Backtracking Search?
Yin Jun Phua, Tony Ribeiro, Tuan Nguyen +1
Backtracking search underlies classical constraint solvers, planners, and theorem provers. Recent transformer-based reasoning systems explore search trees over their own intermedia…
Can We Test Consciousness Theories on AI? Ablations, Markers, and Robustness
Yin Jun Phua
The search for reliable indicators of consciousness has fragmented into competing theoretical camps (Global Workspace Theory (GWT), Integrated Information Theory (IIT), and Higher-…
Future-Proofing Class-Incremental Learning
Quentin Jodelet, Xin Liu, Yin Jun Phua +1
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable. Methods relying on frozen feature extractors have drawn attention recen…