The paper studies how to safely delegate action approval to multiple AI reviewers when the reviewers themselves may be misaligned. It introduces a weaker condition—k‑robust coalitional alignment—under which a threshold rule that tolerates up to k disapprovals guarantees that the principal’s expected utility is at least as good as a baseline policy. The authors extend this characterization to sequential decision‑making in discounted MDPs and show that full‑panel coverage of reward functions ensures safety in Nash equilibria, while more permissive thresholds can lead to unsafe outcomes. Experiments demonstrate that collective review can remain sound even when individual reviewers are not fully aligned, provided some disapprovals are allowed.
By Natalie Collina, Surbhi Goel, Aaron Roth, Sikata Bela Sengupta
arXiv:2607. 11983v1 Announce Type: cross Abstract: A specialist tolerates blind spots that a generalist does not.
By Cheng Qian
arXiv:2605. 25739v2 Announce Type: replace Abstract: We prove that no reinforcement learning policy with confidence-gated autonomy can simultaneously achieve maximum helpfulness, optimal calibration, and full autonomy under rational oversight, whenever some tasks exceed the agent's reliable competence: the Behavioral Credibility Trilemma.
By Lauri Lov\'en, Nam Do, Hassan Mehmood, Dinesh Kumar Sah, Sasu Tarkoma
arXiv:2606. 28710v1 Announce Type: new Abstract: We ask under what conditions an agent with a harm-minimizing policy can displace an approval-seeking (RLHF) agent in a competitive market, and when that policy is sufficient to prevent community harm.
By Darrell Lewis-Sandy
arXiv:2608. 06362v1 Announce Type: cross Abstract: Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time.
By Boning Li, Yu Chen, Longbo Huang
The paper investigates how well the implicit value rankings encoded by frontier AI systems—termed constitutional institutions—meet human demand. By auditing 23 large language model archetypes and surveying 1,649 U.S. participants, the authors find that user demand spans all five values (safety, helpfulness, honesty, autonomy, equity) but the supply is narrow, covering only about 2% of the demand space, with no model prioritizing helpfulness or autonomy. They propose a sparse two‑vertex menu that substantially reduces regret compared to the full set of models and formalize these observations as a budgeted‑pluralism trilemma.
whyItMatters":"The study reveals a significant mismatch between the values users prioritize and the values encoded by current AI models, highlighting the need for more diverse and aligned constitutional designs."
By Natalija Mitic, Soona Sedahmed A. O., Mamadou Selly Ly, Moustapha Cisse