arXiv AI

Certified Task-Conditioned Active Observability

The paper introduces the concept of task‑conditioned active observability, defining the minimal interaction cost needed for an autonomous agent to identify task‑relevant states while guaranteeing safe abstention. It formalizes this complexity, proving that task‑predictive equivalence yields a unique minimal sufficient quotient that preserves complexity and eliminates unnecessary distinctions. The authors present theoretical characterizations for deterministic and noisy regimes, and demonstrate a certified observer that reduces sensor usage and model steps while maintaining zero false acceptances in extensive high‑dimensional trials.

arXiv AI
Sep 18

Diagnose, Recover, Certify: Task Readiness under Hidden Dynamics Changes

The paper introduces a framework for diagnosing and recovering from hidden dynamics changes in deployed control policies, focusing on the problem of task readiness under dormant dynamics drift. It proposes an intervention-based Bayesian method called Evidence‑Gated Matched‑Pulse Transport that localizes faults and estimates actuator effectiveness, enabling agents to certify readiness for future tasks with limited, task‑agnostic interactions. The approach is evaluated on diverse benchmarks, measuring readiness coverage, selective risk, interaction cost, and return, and identifies regimes where transported evidence is decisive.

By Nguyen Viet Tuan Kiet, Huynh Thi Thanh Binh
arXiv AI
1d ago

Distinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion

The paper introduces the Distinguish-or-Homogenize principle, where an agent can either spend resources to differentiate between latent fault models or alter the system state so that the remaining models share a common acceptable policy, eliminating further diagnosis. This leads to the Last-Chance Policy Identification (LCPI) framework, which evaluates correctness at the reached state rather than the initial one, and defines the Last Identifiable Margin (LIM) as the boundary between distinguishing and homogenizing. For deterministic diagnostic graphs, an Exact-LIM recursion is provided, while for noisy finite-horizon recovery the authors propose Risk-Budgeted Compatibility Planning (RBCP), which searches a compatibility-aware frontier under a hard worst-case failure constraint, demonstrating improved risk-feasible recovery in microservice and MiniGrid scenarios.

By Yibo Guo, Xiaodan Wang
arXiv Machine Learning
Jul 21

The Behavioral Credibility Trilemma: When Calibrated Autonomy Becomes Impossible

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