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Activation steering manipulates large language model behavior by intervening on internal activations, but the mechanistic basis of these interventions remains poorly understood. We decompose refusal steering into component-level interventions across four open-weight models, identifying the sparse subsets of attention a...
Partial differential equations (PDEs) govern dynamical systems in hydrodynamics, where classical solvers face well-known difficulties with nonlinearity and computational cost. Lagrangian neural surrogates such as GNS and SEGNN learn directly from particle-based simulations, but local message passing alone is limited in...
This work introduces ACLArena, a framework for comprehensively studying, analyzing, and evaluating Agent Continual Learning, and proposes a new ACL recipe that combines offline replay over high-quality trajectories with a routed network of multiple LoRA experts each specialized via RL, substantially improving the agent...
A Hybrid Lagrangian-Eulerian neural simulator that augments Lagrangian dynamics with an Eulerian representation and substantially suppresses error accumulation is proposed, establishing a new state-of-the-art for accuracy and rollout stability in Lagrangian fluid simulation.
RetroAgent is introduced, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory, enabling informed decisions grounded in both global progress and domain knowledge in multi-step retrosynthesis planning.
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