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Maximilian Puelma Touzel

6 accepted papers

2026

Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions

Matthew Riemer, Tommaso Tosato, Maximilian Puelma Touzel, Amin Memarian, Guillaume Dumas, Glen Berseth +1
ICML 2026poster

This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society c…

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2026

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
ICML 2026poster

LLM-based social simulations—in which many language model agents interact over multiple turns—are rapidly proliferating across policy analysis, epidemiology, and computational social science. Yet the field lacks consensus on how to validate these simulations, with evaluation methods that are sparse,…

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2025

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

Maximilian Puelma Touzel, Sneheel Sarangi, Gayatri Krishnakumar, Busra Tugce Gurbuz, Austin Welch, Zachary Yang +9
IJCAI 2025

The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approaches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Socia

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2025

Veracity: An Open-Source AI Fact-Checking System

Taylor Lynn Curtis, Maximilian Puelma Touzel, William Garneau, Manon Gruaz, Mike Pinder, Li Wei Wang +5
IJCAI 2025

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veraci

Cited by 3Source ↗PDF ↗Scholar ↗
2022

Continual Learning In Environments With Polynomial Mixing Times

Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Raaj Subbaraj, Maximilian Puelma Touzel, Irina Rish
NeurIPS 2022accept

The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mixing times on learning in continual reinforcement learning (RL) remains underexplored. In this paper, we characterize problems that are of long-term interest to…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2019

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio +1
NeurIPS 2019poster

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics an…

Cited by 80Source ↗PDF ↗code ↗Scholar ↗