Research

The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance

Research finds that detailed LLM explanations often fail to improve or actively harm human-AI team performance, contradicting assumptions that transparency automatically strengthens collaboration.

Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

Researchers investigate why LLM-generated explanations paradoxically fail to improve—or may even harm—human-AI team performance. The study challenges assumptions that transparency and explainability automatically strengthen collaborative effectiveness.

Read original at arXiv CS.CL (Computation & Language)

Token Arena: A Continuous Benchmark Unifying Energy and Cognition in AI Inference

Token Arena benchmark unifies energy efficiency and inference performance in a single metric, enabling AI systems to be evaluated on the critical capability-versus-computational-cost tradeoff.