BayesG learns dynamic sparse interaction structures via Bayesian variational inference on ego-graphs for decentralized networked MARL, showing better performance than baselines on traffic control with up to 167 agents.
Generalized incremental learning under concept drift across evolving data streams
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CPO++ adapts reinforcement fine-tuning of MLLMs to endogenous multi-modal concept drift through counterfactual reasoning and preference optimization, yielding better coherence and cross-domain robustness in safety-critical settings.
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Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning
BayesG learns dynamic sparse interaction structures via Bayesian variational inference on ego-graphs for decentralized networked MARL, showing better performance than baselines on traffic control with up to 167 agents.
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Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning
CPO++ adapts reinforcement fine-tuning of MLLMs to endogenous multi-modal concept drift through counterfactual reasoning and preference optimization, yielding better coherence and cross-domain robustness in safety-critical settings.