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:2610.33112v1 Announce Type: new
Abstract: Use-based AI regulation assigns obligations by deployment risk but lacks an evidentiary object to carry them. A benchmark score is a property of a mode...
By John deVadoss
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
ERRAND is a new method for budgeted maintenance of agent memory that treats revalidation of stored knowledge as a priced errand competing for scarce actions. It uses an errand index that is single‑peaked, allowing certainty in either direction to cost nothing, and repairs by writing new versions rather than deleting old ones. In experiments across two drifting tool‑use worlds, ERRAND outperforms non‑oracle policies, achieving up to 10.0 percentage points improvement over eager revalidation while using only 11.0% of steps, and it self‑terminates when no budget is imposed.
By Beining Wu, Zihao Ding, Jun Huang
The paper presents a framework for deriving sharp, label‑free calibration certificates for query‑conditioned probabilistic predictors. It establishes minimax lower bounds on calibration residuals under various betting restrictions, shows that these bounds can be discontinuous and that Lipschitz stakes yield stable, sharp bounds. The authors also demonstrate practical algorithms—projected online gradient ascent and anytime‑valid betting—for generating fixed‑horizon and anytime‑valid certificates, and validate the approach on a preregistered study of four predictors across two datasets.
By Takahiro Kato (NP-hard, Inc.)
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
The paper investigates how marketplace guardrails affect welfare in language‑model agent simulations of hotel transactions. It finds that initial reports of large welfare gains disappear when controlling for offer schemas and buyer choice, and that guardrails mainly redistribute rather than increase welfare unless sellers are explicitly forced to produce inefficient bundles. The authors propose a construct‑validity framework to flag invalid or inconclusive policy claims before they are reported.
By Peiying Zhu, Sidi Chang
arXiv:2607. 09706v1 Announce Type: new Abstract: Language models turn a worded situation into a numeric plan, and the dominant pipelines (NL4Opt, OptiMUS, ORLM, OR-LLM-Agent) commit to a single objective and point-valued coefficients, then solve once.
By Suyash Mishra
The paper proposes a transparent, user‑configurable rule for selecting arguments in deliberative polls, replacing opaque learned rankers. It formalises argument selection over bipolar justification sets, introduces seven civic recommender criteria, and presents a one‑hop reversed endorsement flow rule that meets them. Experiments on 17,000 simulated runs show the rule performs comparably to random on coverage but outperforms other methods on endorsement mass and robustness under adversarial pressure.
By Muntaser Syed, Markus Zanker, Marius Silaghi