The paper "Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanisms" investigates how large language model agents can collectively cause financial failures when making individual protective decisions. Using the FRAIL framework, the authors simulate bank runs, debt rollovers, and reward crowdfunding, finding that 77% of bank-run and 83% of debt-rollover episodes fail even without malicious agents. They test three interaction mechanisms—compensated commitments, centralized agreements, and participant-led coalitions—each improving outcomes but none dominating across all scenarios, noting that early broad commitments are key to successful stabilization.
By Zhenhao Fu, Ruipeng Xu, Qibing Ren
arXiv:2606. 02859v1 Announce Type: cross Abstract: How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control?
By Zhenting Qi, Huangyuan Su, Ao Qu, Chenyu Wang, Yu Yao, Han Zheng, Kushal Chattopadhyay, Guowei Xu, Zihan Wang, Weirui Ye, Vijay Janapa Reddi, Ju Li, Paul Pu Liang, Himabindu Lakkaraju, Sham Kakade, Yilun Du
The paper proposes a framework that connects structured hazard analysis, component-level testing, and probabilistic system modelling to assess system-level harms from AI in complex sociotechnical systems. It demonstrates the approach using the UK's Real Time Gross Settlement system, showing how adversarial inputs to LLM-based trading can shift AI behaviour, reduce system resilience, and increase the likelihood of cascading bank failures. The framework aims to provide a traceable pathway from model behaviour to systemic outcomes, enabling evidence-based governance of AI in critical infrastructure.
By Paul Vautravers, Oliver Chalkley, Gabriel Downer, Kate S, Damian Ruck
arXiv:2607. 21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists.
By Chen Zhu, Xiaolu Wang, Weilong Zhang
The paper titled "The Moral Check: Strategic AI Governance for the Pacing Problem" argues that technology cannot self‑steer and that strategy must guide AI development by ensuring purpose and judgment precede compute. It presents a dual contribution: a PRISMA 2020 review of 130 empirical studies and the Strategic AI Governance Ex‑Ante Framework (SAGE‑X), which operationalizes four strategic mindset pillars to mitigate velocity myopia, moral hazard, empirical hazard endpoints, and guardrail decay. The framework includes a calculable Moral Check Index and an Enterprise Lifecycle Audit Instrument to enforce that AI scaling does not outpace deliberative moral judgment, human agency, and societal trust.
By Zaid Amin, Rahma Santhi Zinaida, Nazlena Mohamad Ali
arXiv:2608. 01432v2 Announce Type: replace Abstract: Artificial general intelligence (AGI) may weaken scarcities in labour, expertise, information, and productive capability that underpin established theories of economic value.
By Keyun Ruan
arXiv:2606. 16319v1 Announce Type: new Abstract: Modern AI systems exhibit structural failures that capability scaling alone does not reliably fix: they optimize under-specified objectives with no architectural mechanism to question whether the objective should be optimized at all.
By Edward Y. Chang
arXiv:2606. 31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment.
By Muhammad Usman Safder (Steve), Ayesha Gull (Steve), Rania Elbadry (Steve), Fan Zhang (Steve), Yankai Chen (Steve), Xueqing Peng (Steve), Xue (Steve), Liu, Preslav Nakov, Zhuohan Xie
arXiv:2604.22230v2 Announce Type: replace-cross
Abstract: Performance manipulation arises when agents exploit easily measurable, routine tasks to inflate observable outcomes without contributing genu...
By Xiaoyun Qiu, Yang Yu, Haifeng Xu
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of...
arXiv:2607. 14641v1 Announce Type: new Abstract: Abductive reasoning operates in two directions.
By Remo Pareschi
arXiv:2608.24748v1 Announce Type: cross
Abstract: How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixe...
By Jacy Reese Anthis, Erik Brynjolfsson, James Evans