Title: The Cost of Knowing: Hallucination Quest Game in Resource-Constrained Multi-Agent Systems
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Multi-Agent System, Hallucination

Abstract: Current LLM hallucination benchmarks are predominantly static, focusing on
factuality while ignoring the computational resources consumed. This creates
a distorted view of performance, as costly mitigation strategies can obscure the
inherent capabilities of more efficient models. This limitation is especially critical
in multi-agent systems (MAS), where resource efficiency and strategic interaction
are paramount. To address this gap, we introduce MAS-HQ (Multi-Agent System
Hallucination Quest Game), a dynamic, game-theoretic framework that evaluates
MAS hallucination under strict resource constraints and direct adversarial competi-
tion. Within MAS-HQ, agents compete to produce low-hallucination summaries
while minimizing resource use. Success is measured by a multi-dimensional metric
that explicitly balances factual accuracy against resource penalties, forcing a trade-
off between quality and efficiency. We instantiate this competition with Q-Agent, a
modular agent architecture designed for strategic play, within a setting that features
partial observability to drive tactical decision-making. Our experiments reveal
the emergence of diverse winning strategies—some prioritizing high factuality,
others superior resource efficiency—and demonstrate adaptive agent behaviors
driven by the competitive dynamics. MAS-HQ establishes a principled paradigm
for benchmarking hallucination in MAS and provides crucial insights into agent
strategies under adversarial, resource-constrained conditions.