为大规模多智能体系统设计可追踪、可干预的责任机制，防止合谋等异常行为。

Large-scale networked multi-agent systems increasingly underpin critical infrastructure, yet their collective behavior can drift toward undesirable emergent norms such as collusion, resource hoarding, and implicit unfairness. We present the Adaptive Accountability Framework (AAF), an end-to-end runtime layer that (i) records cryptographically verifiable interaction provenance, (ii) detects distributional change points in streaming traces, (iii) attributes responsibility via a causal influence graph, and (iv) applies cost-bounded interventions-reward shaping and targeted policy patching-to steer the system back toward compliant behavior. We establish a bounded-compromise guarantee: if the expected cost of intervention exceeds an adversary's expected payoff, the long-run fraction of compromised interactions converges to a value strictly below one. We evaluate AAF in a large-scale factorial simulation suite (87,480 runs across two tasks; up to 100 agents plus a 500-agent scaling sweep; full and partial observability; Byzantine rates up to 10%; 10 seeds per regime). Across 324 regimes, AAF lowers the executed compromise ratio relative to a Proximal Policy Optimization baseline in 96% of regimes (median relative reduction 11.9%) while preserving social welfare (median change 0.4%). Under adversarial injections, AAF detects norm violations with a median delay of 71 steps (interquartile range 39-177) and achieves a mean top-ranked attribution accuracy of 0.97 at 10% Byzantine rate.