arXiv:2604.05129v2 Announce Type: replace-cross Abstract: We investigate the strategic surplus obtainable against a Follow-the-Regularized-Leader (FTRL) learner with constant step size $\eta$ in $n\times m$ two-player zero-sum games played over $T$ rounds against a clairvoyant optimizer. In contrast with prior analysis, we show that the extraction of such regret-scale surplus is an inherent feature of the FTRL family, rather than an artifact of specific instantiations. First, for a fixed max-min optimizer, we establish a sweeping law of order $\Omega(N_{\mathrm{sub}}/\eta)$, proving that utili
The paper, published in 2026, details advances in understanding AI agent dynamics, crucial for developing more effective and manipulable AI systems, aligning with current research momentum in AI strategy and learning theory.
This research reveals inherent vulnerabilities in common AI learning algorithms, suggesting that advanced strategic actors could exploit these dynamics to extract significant 'surplus' or advantage, impacting AI-driven markets and interactions.
Our understanding of AI agent robustness and exploitability is enhanced, providing a theoretical foundation for designing more resilient AI systems or, conversely, for creating systems adept at extracting maximum value from predictable counterparts.
- · AI ethicists and safety researchers
- · Organizations developing advanced AI agents
- · Game theory researchers
- · Developers of robust AI systems
- · Users of basic FTRL-based AI agents
- · Competitors with less sophisticated AI strategies
- · Organizations unaware of AI exploitability
Increased focus on adversarial robustness and exploitability in AI system design.
Development of a new class of AI agents specifically designed to exploit the predictable behaviors of standard learning algorithms.
Potential for significant value transfer in AI-mediated markets and strategic interactions, favoring those with superior understanding of agent dynamics.
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