SPIN-Bench: How Well Do LLMs Plan Strategically and Reason Socially?
Authors: name: Jianzhu Yao, name: Kevin Wang, name: Ryan Hsieh, name: Haisu Zhou, name: Tianqing Zou, name: Zerui Cheng, name: Zhangyang Wang, name: Pramod Viswanath

Reasoning and strategic behavior in social interactions is a hallmark
of intelligence. This form of reasoning is significantly more sophisticated
than isolated planning or reasoning tasks in static settings (e.g., math
problem solving). In this paper, we present Strategic Planning,
Interaction, and Negotiation (SPIN-Bench), a new multi-domain
evaluation designed to measure the intelligence of strategic planning
and social reasoning. While many existing benchmarks focus on narrow
planning or single-agent reasoning, SPIN-Bench combines classical PDDL tasks,
competitive board games, cooperative card games, and multi-agent negotiation
scenarios in one unified framework. The framework includes both a benchmark as
well as an arena to simulate and evaluate the variety of social settings to
test reasoning and strategic behavior of AI agents. We formulate the benchmark
SPIN-Bench by systematically varying action spaces, state complexity, and the
number of interacting agents to simulate a variety of social settings where
success depends on not only methodical and step-wise decision making, but also
conceptual inference of other (adversarial or cooperative) participants.
Our experiments reveal that while contemporary LLMs handle basic fact
retrieval and short-range planning reasonably well, they encounter
significant performance bottlenecks in tasks requiring deep multi-hop
reasoning over large state spaces and socially adept coordination under
uncertainty. We envision SPIN-Bench as a catalyst for future research on robust
multi-agent planning, social reasoning, and human--AI teaming.