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Equivariant Neural Belief Propagation
Zehua Cheng, Wei Dai, Jiahao Sun
Probabilistic inference over spatially embedded variables requires beliefs that respect symmetry, yet existing equivariant networks produce only scalars and vectors -- not…
In-Context Graphical Inference
Zehua Cheng, Wei Dai, Jiahao Sun
Marginal inference in discrete graphical models forces a choice between exactness and scalability: exact algorithms are intractable for high-treewidth graphs, while iterative appro…
Invariant Gradient Alignment for Robust Reasoning Distillation
Zehua Cheng, Wei Dai, Jiahao Sun
Large language models (LLMs) suffer from shortcut learning: they systematically fail on out-of-distribution (OOD) inputs whose semantic surface differs from training data, even whe…
CasualSynth: Generating Structurally Sound Synthetic Data
Zehua Cheng, Wei Dai, Jiahao Sun +1
Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the target domain. We introduce Cau…
Scaling Decentralized Learning with FLock
Zehua Cheng, Rui Sun, Jiahao Sun +1
Fine-tuning the large language models (LLMs) are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized scheme…