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A diagram illustrating the interaction between two AI agents with opposing goals.
research

Experience Orchestrator (EO): A Control-Theoretic Governance Layer That Prevents Conversational Collapse Between Opposing AI Agents

Researchers introduced the Experience Orchestrator (EO), a control-theoretic governance layer that prevents conversations between AI agents with opposing goals from collapsing. Tested in a simulated financial services environment, the EO successfully kept interactions productive where ungoverned conversations fail.

A diagram showing the stages of AI model training and alignment.
research

AI Output Homogeneity Traced to Pretraining, Not Just Alignment, New Study Finds

A new ArXiv study finds that semantic convergence in large language models begins during the pretraining phase, not just the alignment process. The research shows that output homogeneity is observed from the first alignment stage (instruction-tuning/SFT), suggesting it is learned early and only magnified later. This challenges the common assumption that diversity loss is primarily an alignment problem.

via ArXiv cs.CL#Research#Alignment