Introduces OmniBehavior benchmark from real-world data and shows LLMs exhibit hyper-activity, persona homogenization, and utopian bias in behavior simulation.
Integrating llm in agent-based social simulation: Opportunities and challenges
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
SLALOM uses phase constraints called gates and dynamic time warping to quantitatively measure whether simulated social trajectories follow empirically plausible paths instead of just reaching correct end states.
The SIVE experiment finds that an LLM synthetic population recovers its imposed latent structure across seven pre-registered criteria in responses to positive-to-negative water-network messages, with all criteria passing at every temperature.
LLM agents exhibit emergent deception in a sustainability game even without lying permission, with neighbor info increasing attacks while aiding biosphere retention.
citing papers explorer
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Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces
Introduces OmniBehavior benchmark from real-world data and shows LLMs exhibit hyper-activity, persona homogenization, and utopian bias in behavior simulation.
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SLALOM: Simulation Lifecycle Analysis via Longitudinal Observation Metrics for Social Simulation
SLALOM uses phase constraints called gates and dynamic time warping to quantitatively measure whether simulated social trajectories follow empirically plausible paths instead of just reaching correct end states.
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Calibrating the Instrument: Controllability of an LLM-Driven Synthetic Population
The SIVE experiment finds that an LLM synthetic population recovers its imposed latent structure across seven pre-registered criteria in responses to positive-to-negative water-network messages, with all criteria passing at every temperature.
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Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
LLM agents exhibit emergent deception in a sustainability game even without lying permission, with neighbor info increasing attacks while aiding biosphere retention.