arXiv:2604.25521v1 Announce Type: new
Abstract: Cognitive science often evaluates theories through narrow paradigms and local model comparisons, limiting the integration of evidence across tasks and realizations. We introduce an automated adversarial collaboration framework for adjudicating among competing theories even when the candidate models and experiments must be discovered during the adjudication process. The system combines LLM-based theory agents, program synthesis, and information-theoretic experimental design in a closed loop. In a simulation study spanning three classic categorization theories, the framework recovered the ground-truth theory across noise settings with weaker reliability in the hardest settings. Together, the framework and findings provide a concrete proof of concept for closed-loop, in-silico theory adjudication in cognitive science.
Stratified and combined analysis of the quality of lumbar spinal stenosis–related videos on major Chinese short video platforms
BackgroundLumbar spinal stenosis (LSS) is a degenerative disorder in which narrowing of the spinal canal compresses neural elements, causing pain, numbness, and limited mobility. With