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This work introduces EXAM, an EXtremist Agent Modeling framework that capable of configuring extremist agents for specific tasks and precisely controlling their population proportion, spatial distribution, interaction protocols, and measurement interfaces.
Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While elastic architectures enable flexible model deployment and sparsely activated models allow input-adaptive computation, existing approaches tre...
StepLearn is introduced, a nonparametric framework that separates immediate use from persistent trust, while requiring prospective validation before reuse across episodes, and turns informative transitions into hypotheses that can guide the next step.
On-Policy Omni Distillation (OPOD), which consolidates text, image, and audio teachers into one omni model, and surpasses the base model and pooled RL training on all twelve benchmarks, and ranks first or second on eleven even when the teachers are included.
This work introduces a generalizable evaluation framework that maps native MAS traces into a shared space of unified collaboration graphs, enabling different methods to be evaluated under the same representation, reference set, and metric panel.
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