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This work proposes Test-Time Adaptation via Stability-Aware Confidence Optimization (TASCO), a framework that incorporates local stability into confidence-based test-time adaptation while keeping the LLM frozen and improves reasoning accuracy and token efficiency across diverse LLMs and reasoning benchmarks.
Merging slow and fast-thinking models can cut reasoning verbosity by over 24% without sacrificing accuracy, revolutionizing LLM-based recommendations.
Personalized communication skills can transform agentic recommender systems, leading to significant improvements in recommendation accuracy by leveraging diverse user insights.
