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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.30258 |
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| _version_ | 1866917544710897664 |
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| author | Sarangi, Sneheel Touzel, Maximilian Puelma Bück-Kaeffer, Aurélien Yang, Zachary Godbout, Jean-François Rabbany, Reihaneh |
| author_facet | Sarangi, Sneheel Touzel, Maximilian Puelma Bück-Kaeffer, Aurélien Yang, Zachary Godbout, Jean-François Rabbany, Reihaneh |
| contents | LLMs are increasingly deployed to simulate social interactions, yet many of the existing simulators remain ad hoc and monolithic. This lack of architectural standardization prevents reproducible research and complicates downstream evaluation. We advance a rigorous science of LLM-based multi-agent simulation by modularizing core components into Environments, Agents, Simulation engines, and Evaluation metrics (EASE). We demonstrate the utility of EASE configuration by wrapping it in an experimental study schema for orchestrating workflows centered around answering explicit research questions in generated scenarios. We contribute SiliSocS, an open-source, research-ready Silicon Society Sandbox implementing a study-structured EASE configuration to enable highly configurable and reproducible LLM-based social simulations. Using SiliSocS and EASE, we present three case studies, showcasing the system's comprehensive assessment of existing questions, ability to dive deeper into complex questions, and elaboration of existing studies, respectively. Together, these case studies highlight the limitations of current modeling approaches and isolate the impacts of design choices on key results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30258 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations Sarangi, Sneheel Touzel, Maximilian Puelma Bück-Kaeffer, Aurélien Yang, Zachary Godbout, Jean-François Rabbany, Reihaneh Multiagent Systems LLMs are increasingly deployed to simulate social interactions, yet many of the existing simulators remain ad hoc and monolithic. This lack of architectural standardization prevents reproducible research and complicates downstream evaluation. We advance a rigorous science of LLM-based multi-agent simulation by modularizing core components into Environments, Agents, Simulation engines, and Evaluation metrics (EASE). We demonstrate the utility of EASE configuration by wrapping it in an experimental study schema for orchestrating workflows centered around answering explicit research questions in generated scenarios. We contribute SiliSocS, an open-source, research-ready Silicon Society Sandbox implementing a study-structured EASE configuration to enable highly configurable and reproducible LLM-based social simulations. Using SiliSocS and EASE, we present three case studies, showcasing the system's comprehensive assessment of existing questions, ability to dive deeper into complex questions, and elaboration of existing studies, respectively. Together, these case studies highlight the limitations of current modeling approaches and isolate the impacts of design choices on key results. |
| title | EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2605.30258 |