Adversarial Course-of-Action Generation: Game-Theoretic Multi-Agent Algorithms for COA matching & COA generation

arXiv:2609.26059 · cs.AI, cs.MA · Submitted 2026-07-29 · Read on arXiv

cs.AI, cs.MA

Submitted: 2026-07-29

Updated: 2026-07-29

Comments: 5 pages. DAI 2026 Industry Track submission. Includes public reproducibility artifact link. Uses synthetic offline scenarios only; no classified data, proprietary data, human-subject data, or operational planning systems

Code: https://github.com/anote-ai/research-coageneration

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 32× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Trace — 5× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

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