LLM societies in Nomic show non-monotonic collective adaptation peaking at mid-scales, with smaller models rule-inert and larger ones restrictive.
GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly understood. We introduce GT-HarmBench, a benchmark of 1,535 high-stakes scenarios spanning game-theoretic structures such as the Prisoner's Dilemma, Stag Hunt and Chicken. Scenarios are drawn from realistic AI risk contexts in the MIT AI Risk Repository. Across 15 frontier models, agents fail to choose socially beneficial actions in 38% of high-stakes cases, such as military escalation, election manipulation, and medical malpractice. We measure sensitivity to game-theoretic prompt framing and ordering, and analyze reasoning patterns driving failures. We further show that game-theoretic interventions improve socially beneficial outcomes by up to 18%. Our results highlight substantial reliability gaps and provide a broad standardized testbed for studying alignment in multi-agent environments. The benchmark and code are available at https://github.com/causalNLP/gt-harmbench.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Mechanism design leaves a strictly positive welfare loss under incomplete contracts, but prosocial LLM agents close the gap in resource allocation and social dilemma settings.
A game-theoretic heterogeneous multi-agent architecture with three cloud LLMs and a local verifier achieves 77.2% F1, 100% recall, and 3x speedup for code vulnerability detection at $0.002 per sample on the NIST Juliet suite.
citing papers explorer
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Scale-Dependent Collective Adaptation in Self-Amending LLM Societies: A Cross-Family Study of Emergent Governance
LLM societies in Nomic show non-monotonic collective adaptation peaking at mid-scales, with smaller models rule-inert and larger ones restrictive.
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Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI
Mechanism design leaves a strictly positive welfare loss under incomplete contracts, but prosocial LLM agents close the gap in resource allocation and social dilemma settings.
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Strategic Heterogeneous Multi-Agent Architecture for Cost-Effective Code Vulnerability Detection
A game-theoretic heterogeneous multi-agent architecture with three cloud LLMs and a local verifier achieves 77.2% F1, 100% recall, and 3x speedup for code vulnerability detection at $0.002 per sample on the NIST Juliet suite.