3 papers
q-bio.NC2026
Reward function compression facilitates goal-dependent reinforcement learning
Gaia Molinaro, Anne G. E. Collins
Humans can uniquely assign value to novel, abstract outcomes to support reinforcement learning. However, this flexibility is cognitively costly and reduces learning efficiency. We…
cs.CL2026
Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task
Gaia Molinaro, Dave August, Danielle Perszyk +1
Whether in agentic workflows, social studies, or chat settings, large language models (LLMs) are increasingly being asked to replace humans in choosing which goals to pursue, rathe…
cs.HC2026
Toward Human-AI Complementarity Across Diverse Tasks
Yuzheng Xu, Annya Dahmani, Matthew D. Blanchard +13
Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. How…