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cs.LG2026
ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training
Omatharv Bharat Vaidya, Ashwin Vinod, Pedram Akbarian +3
Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part changes during training. With a…
cs.MA2026
Bayesian Partner Modelling enables Adaptive Replanning for LLM Coordination
Harsh Goel, Aditya Sai Ellendula, Vaishnav Tadiparthi +3
Multi-agent Large Language Model (LLM) systems often struggle to collaborate with new teammates whose strategies shift mid-task. Because agents execute multi-step or temporally ext…
cs.LG2026
Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
Aditya Sai Ellendula, Yi Wang, Chandrajit Bajaj
Risk-aware navigation should be selective: a policy should expose evasive degrees of freedom only when the local scene admits a lower-risk feasible maneuver, and suppress them when…