Stress-Testing Coordination: Adversarial Scenarios for Exposing Scaling Law Failures

TL;DR: Let’s find out what really breaks these agent system scaling laws—by designing “nightmare” scenarios that deliberately provoke coordination breakdowns. For example, inject adversarial agents or simulate abrupt distributional shifts, then measure which scaling principles hold and where they shatter.

Research Question: Under what adversarial or pathologically difficult conditions do the observed agent system scaling laws and coordination strategies fail, and what new failure modes emerge?

Hypothesis: Scaling laws and coordination topologies that perform well on standard benchmarks may fail dramatically in the presence of adversarial behavior, reward hacking, or abrupt environment shifts, revealing unanticipated error amplification or coordination collapse.

Experiment Plan: Create adversarial testbeds: Introduce “Byzantine” agents that sabotage coordination, or engineer tasks with abrupt, unannounced changes in rules or resources. Instrument both tool-heavy and coordination-heavy tasks, as in the original study. Track error amplification, coordination gaps, and recovery times, comparing to baseline scaling predictions. Analyze which coordination architectures (centralized, decentralized, hybrid) retain robustness, and which fail, under each stress-test. Document new failure modes for future mitigation.

References:

• Manheim, D. (2018). Overoptimization Failures and Specification Gaming in Multi-agent Systems. Big Data and Cognitive Computing.
• Wang, Q., Wang, T., Tang, Z., Li, Q., Chen, N., Liang, J., & He, B. (2024). MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs. Annual Meeting of the Association for Computational Linguistics.
• Li, W., Lin, B., Li, T., Yue, J., Shi, M., & Qin, K. (2023). Neural Network-based Adaptive Scaled Consensus Control of Uncertain Fractional-order Multi-agent Systems. Chinese Control and Decision Conference.