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Maximilian Puelma Touzel

puelmatm@mila.quebec
Scientifique de recherche, AMLRT, Solution IA et technologie
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Publications

A Framework for Context Enrichment and Selective Robustness in LLM Social Agents
Mattia Greiche
Reihaneh Rabbany
Maximilian Puelma Touzel
Researchers use large language models as agents in social simulations, where agents are prompted with observations and background informatio… (voir plus)n intended to shape their actions. Yet LLM agents have been shown to substantially change their behavior under meaning-preserving prompt variations, creating a validation problem for social simulation. One plausible hypothesis is that this brittleness reflects lack of grounding and can be reduced by enriching the context provided to the agents. Whether richer context improves robustness remains unclear, in part because context richness and robustness are rarely defined in ways that support controlled comparisons. We address the first need with controlled context enrichment, a set of prompt substitution rules that systematically enrich distinct types of context: neutral padding, generic social background, descriptive decision grounding, and normative decision grounding. We address the second by introducing selective robustness as a validation criterion: insensitivity to meaning-preserving prompt variation combined with a directional response to decision-relevant changes in the social world. We demonstrate the framework in a Prisoner's Dilemma-like game, showing how it can be used to identify which forms of context enrichment produce more robust agent behavior. We find that these forms of enrichment produce distinct behavioral patterns that vary between the two models tested.
2026-10-04
SocialAgent @ Neural Information Processing Systems (poster)
openreview.net
openreview.net
EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations
Sneheel Sarangi
Maximilian Puelma Touzel
Aurélien Bück-Kaeffer
Zachary Yang
Jean-François Godbout
Reihaneh Rabbany
LLMs are increasingly deployed to simulate social interactions, yet many of the existing simulators remain ad hoc and monolithic. This lack … (voir plus)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.
2026-05-27
arXiv (prépublication)
doi.org
arxiv.org
The $\textit{Silicon Society}$ Cookbook: Design Space of LLM-based Social Simulations
Aurélien Bück-Kaeffer
Sneheel Sarangi
Maximilian Puelma Touzel
Reihaneh Rabbany
Zachary Yang
Jean-François Godbout
Studies attempting to simulate human behavior with …
2026-04-29
arXiv (prépublication)
doi.org
arxiv.org
Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions
Matthew Riemer
Tommaso Tosato
Amin Memarian
Maximilian Puelma Touzel
Glen Berseth
Irina Rish
Guillaume Dumas
This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and sho… (voir plus)uld be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of- thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive economic outcomes without any evidence of conspiracy or intent. Thus, certification based on observed behavior in representative situations is necessary to prevent collusion. We provide preliminary evidence that such agents can be steered in a generalizable way toward efficient competitive equilibria. However, developing a comprehensive behavioral certification will be required before these models can be deployed in real-world markets while ensuring their stability and efficiency.
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
proceedings.mlr.press
Position: Time to Close The Validation Gap in LLM Social Simulations
Maximilian Puelma Touzel
Sneheel Sarangi
Aurélien Bück-Kaeffer
Zachary Yang
Jean-François Godbout
Reihaneh Rabbany
LLM-based social simulations—in which many language model agents interact over multiple turns—are rapidly proliferating across policy an… (voir plus)alysis, epidemiology, and computational social science. Yet the field lacks consensus on how to validate these simulations, with evaluation methods that are sparse, inconsistent, and rarely shared across disciplinary silos. We argue this creates a serious risk: premature deployment of unvalidated simulators in high-stakes domains. Our position is that the field must pivot from expansion to consolidation, prioritizing methodological standardization—shared benchmarks, open data, and reproducible evaluation protocols grounded in social science and complex systems research. We outline a concrete research program organized around specific learning problems/benchmarks, providing a path toward answering the fundamental question: when are LLM social simulations useful modelling objects?
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net
$\texttt{BluePrint}$: A Social Media User Dataset for LLM Persona Evaluation and Training
Aur'elien Buck-Kaeffer
Je Qin Chooi
Dan Zhao
Maximilian Puelma Touzel
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
Zachary Yang
Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethi… (voir plus)cally or logistically challenging with human subjects. However, the field lacks standardized data resources for fine-tuning and evaluating LLMs as realistic social media agents. We address this gap by introducing SIMPACT, the SIMulation-oriented Persona and Action Capture Toolkit, a privacy respecting framework for constructing behaviorally-grounded social media datasets suitable for training agent models. We formulate next-action prediction as a task for training and evaluating LLM-based agents and introduce metrics at both the cluster and population levels to assess behavioral fidelity and stylistic realism. As a concrete implementation, we release BluePrint, a large-scale dataset built from public Bluesky data focused on political discourse. BluePrint clusters anonymized users into personas of aggregated behaviours, capturing authentic engagement patterns while safeguarding privacy through pseudonymization and removal of personally identifiable information. The dataset includes a sizable action set of 12 social media interaction types (likes, replies, reposts, etc.), each instance tied to the posting activity preceding it. This supports the development of agents that use context-dependence, not only in the language, but also in the interaction behaviours of social media to model social media users. By standardizing data and evaluation protocols, SIMPACT provides a foundation for advancing rigorous, ethically responsible social media simulations. BluePrint serves as both an evaluation benchmark for political discourse modeling and a template for building domain specific datasets to study challenges such as misinformation and polarization.
2025-09-26
ArXiv (prépublication)
doi.org
arxiv.org
SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents
Maximilian Puelma Touzel
Sneheel Sarangi
Gayatri Krishnakumar
Busra Tugce Gurbuz
Austin Welch
Zachary Yang
Andreea Musulan
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Camille Thibault
Reihaneh Rabbany
Jean-François Godbout
Dan Zhao
Kellin Pelrine
The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approa… (voir plus)ches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Social system simulations with agents modelled using Large Language Models (LLMs) are a promising alternative approach and a growing area of research. However, existing simulators lack features needed to capture the complex information-sharing dynamics of platform-based social networks. To bridge this gap, we present SandboxSocial, a new simulator that includes several key innovations, mainly: (1) a virtual social media platform (modelled as Mastodon and mirrored in an actual Mastodon server) that enables a realistic setting in which agents interact; (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. We make the simulator more useful to researchers by providing measurement and analysis tools that track simulation dynamics and compute evaluation metrics to compare experimental results.
2025-08-15
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)
doi.org
Veracity: An Open-Source AI Fact-Checking System
Taylor Lynn Curtis
Maximilian Puelma Touzel
William Garneau
Manon Gruaz
Mike Pinder
Li Wei Wang
Sukanya Krishna
Luda Cohen
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (voir plus) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.
2025-08-15
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection
Hao Yu
Shenyang Huang
Zachary Yang
Maximilian Puelma Touzel
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
2025-07-24
colmweb.org/COLM/2025/Workshop/SoLaR (poster)
openreview.net
openreview.net
A Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri K
Dan Zhao
Zachary Yang
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (voir plus)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.
2024-10-16
ArXiv (prépublication)
doi.org
arxiv.org
Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri K
Dan Zhao
Zachary Yang
Hao Yu
Tom Gibbs
Ethan Kosak-Hine
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (voir plus)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. Through a variety of means we then improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys of the agents' political positions. We demonstrate the simulator with a tailored example of how partisan manipulation of agents can affect election results.
2024-10-11
NeurIPS.cc/2024/Workshop/SafeGenAi (poster)
openreview.net
openreview.net
Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada
Zachary Yang
Anne Imouza
Maximilian Puelma Touzel
C'ecile Amadoro
Gabrielle Desrosiers-Brisebois
Kellin Pelrine
Sacha Lévy
Jean-François Godbout
Reihaneh Rabbany
Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (voir plus)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.
2024-07-02
ArXiv (prépublication)
doi.org
arxiv.org