Nino Scherrer

According to our database, Nino Scherrer authored at least 20 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Reasoning Models Generate Societies of Thought.

Junsol Kim, Shiyang Lai, Nino Scherrer, Blaise Agüera y Arcas, James Evans

CoRR, January, 2026

2025

Uncovering Competency Gaps in Large Language Models and Their Benchmarks.

Matyas Bohacek, Nino Scherrer, Nicholas Dufour, Thomas Leung, Christoph Bregler, Stephanie C. Y. Chan

CoRR, December, 2025

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning.

Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel, Angelika Steger, Maciej Wolczyk, Johannes von Oswald, Nino Scherrer, Kaitlin Maile, Guillaume Lajoie, Blake A. Richards, Rif A. Saurous, James Manyika, Blaise Agüera y Arcas, Alexander Meulemans, João Sacramento

CoRR, December, 2025

Do Depth-Grown Models Overcome the Curse of Depth? An In-Depth Analysis.

Ferdinand Kapl, Emmanouil Angelis, Tobias Höppe, Kaitlin Maile, Johannes von Oswald, Nino Scherrer, Stefan Bauer

CoRR, December, 2025

Not Every AI Problem Is a Data Problem.

Tanya Rodchenko, Natasha F. Noy, Nino Scherrer

Commun. ACM, October, 2025

No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Models.

Flor Miriam Plaza del Arco, Paul Röttger, Nino Scherrer, Emanuele Borgonovo, Elmar Plischke, Dirk Hovy

CoRR, September, 2025

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training.

Johannes von Oswald, Nino Scherrer, Seijin Kobayashi, Luca Versari, Songlin Yang, Maximilian Schlegel, Kaitlin Maile, Yanick Schimpf, Oliver Sieberling, Alexander Meulemans, Rif A. Saurous, Guillaume Lajoie, Charlotte Frenkel, Razvan Pascanu, Blaise Agüera y Arcas, João Sacramento

CoRR, June, 2025

Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection.

Souradip Chakraborty, Mohammadreza Pourreza, Ruoxi Sun, Yiwen Song, Nino Scherrer, Furong Huang, Amrit Singh Bedi, Ahmad Beirami, Jindong Gu, Hamid Palangi, Tomas Pfister

CoRR, April, 2025

Not Every AI Problem is a Data Problem: We Should Be Intentional About Data Scaling.

Tanya Rodchenko, Natasha F. Noy, Nino Scherrer, Jennifer Prendki

CoRR, January, 2025

Multi-agent cooperation through learning-aware policy gradients.

Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald, Nino Scherrer, Eric Elmoznino, Blake Aaron Richards, Guillaume Lajoie, Blaise Agüera y Arcas, João Sacramento

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Introducing v0.5 of the AI Safety Benchmark from MLCommons.

Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Subhra S. Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin N. Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren

CoRR, 2024

2023

FinanceBench: A New Benchmark for Financial Question Answering.

Pranab Islam, Anand Kannappan, Douwe Kiela, Rebecca Qian, Nino Scherrer, Bertie Vidgen

CoRR, 2023

SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models.

Bertie Vidgen, Hannah Rose Kirk, Rebecca Qian, Nino Scherrer, Anand Kannappan, Scott A. Hale, Paul Röttger

CoRR, 2023

Uncovering mesa-optimization algorithms in Transformers.

Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento

CoRR, 2023

Evaluating the Moral Beliefs Encoded in LLMs.

Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Trust Your 𝛁: Gradient-based Intervention Targeting for Causal Discovery.

Mateusz Olko, Michal Zajac, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Lukasz Kucinski, Piotr Milos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

FED-CD: Federated Causal Discovery from Interventional and Observational Data.

Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

CoRR, 2022

On the Generalization and Adaption Performance of Causal Models.

Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke

CoRR, 2022

2021

Learning Neural Causal Models with Active Interventions.

Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke

CoRR, 2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures.

Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

CoRR, 2021