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This paper proposes a nonparametric variational learning framework to simultaneously infer interaction kernels and environmental forces in collective dynamics, validated on benchmark models with a model-selection procedure.
This paper introduces Perturbed Parameter Policy Optimization (3PO), a family of parameter-space exploration methods for LLM reinforcement learning, showing consistent improvements over GRPO on math and code tasks.
Proposes Asymmetric Mutual Variational Learning (AMVL) to resolve train-inference mismatch in multimodal continuous reasoning by using bidirectional calibration to prevent answer leakage and improve latent-space stability, achieving significant gains on the BLINK benchmark.
This paper introduces Data-Driven Variational Basis Learning (DVBL), a non-neural framework that learns basis functions directly from data through variational optimization, offering interpretability and mathematical transparency compared to neural networks.