Publications

Acute respiratory distress syndrome in patients with cancer: the YELENNA prospective multinational observational cohort study
Peter Schellongowski
Michael Darmon
Philipp Eller
Laveena Munshi
Tobias Liebregts
Victoria Metaxa
Luca Montini
Tobias Lahmer
Fabio S. Taccone
Andry Van de Louw
Martin Balik
Peter Pickkers
Pleun Hemelaar
Hemang Yadav
Andreas Barratt-Due
Thomas Karvunidis
Jordi Riera
Gennaro Martucci
Ignacio Martin-Loeches
Pedro Castro … (voir 6 de plus)
Nina Buchtele
Virginie Lemiale
Stefan Hatzl
Guillaume Dumas
Thomas Staudinger
Elie Azoulay
2025-09-25
Intensive Care Medicine (publié)
www.ncbi.nlm.nih.gov
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu
João Felipe Rocha
Tinglin Huang
Siddharth Viswanath
Smita Krishnaswamy
Rex Ying
2025-09-25
ArXiv (prépublication)
doi.org
arxiv.org
HEIST: A Graph Foundation Model for Spatial Transcriptomics and
Proteomics Data
Hiren Madhu
João Felipe Rocha
Tinglin Huang
Siddharth Viswanath
Smita Krishnaswamy
Rex Ying
2025-09-25
ArXiv (prépublication)
www.ncbi.nlm.nih.gov
Benchmarking Machine Learning Potentials for Crystal Structure Relaxation
Kowen Woo
Prashant Govindarajan
Sarath Chandar
High-throughput materials discovery workflows require rapid and accurate relaxation of crystal structures to identify thermodynamically stab… (voir plus)le phases among thousands to millions of candidate structures. Yet current machine learning interatomic potential (MLIP) benchmarks focus predominantly on energy prediction rather than structure relaxation, creating a critical evaluation gap for models designed to accelerate optimization. Additionally, these benchmarks are trained on datasets consisting mainly of known stable or near-stable materials, thus failing to capture the challenges of unexplored chemical spaces. We address these limitations by introducing a benchmark that evaluates state-of-the-art MLIPs and a one-shot relaxation model on structure relaxation with crystals generated via a reinforcement learning pipeline. We compare energy lowering and average maximum force computed via DFT, as well as relaxation runtime. We also contrast direct force-prediction strategies against conservative energy-differentiation approaches to determine which paradigm delivers superior relaxation performance. Our results indicate that there is a clear disconnect between MLIP energy prediction and force convergence in relaxation, challenging current benchmarking approaches.
2025-09-24
NeurIPS.cc/2025/Workshop/AI4Science (poster)
openreview.net
openreview.net
Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
Danny Reidenbach
Zhonglin Cao
Zuobai Zhang
Kieran Didi
Tomas Geffner
Guoqing Zhou
Jian Tang
Christian Dallago
Arash Vahdat
Emine Kucukbenli
Karsten Kreis
High-quality training datasets are crucial for the development of effective protein design models, but existing synthetic datasets often inc… (voir plus)lude unfavorable sequence-structure pairs, impairing generative model performance. We leverage ProteinMPNN, whose sequences are experimentally favorable as well as amenable to folding, together with structure prediction models to align high-quality synthetic structures with recoverable synthetic sequences. In that way, we create a new dataset designed specifically for training expressive, fully atomistic protein generators. By retraining La-Proteína, which models discrete residue type and side chain structure in a continuous latent space, on this dataset, we achieve new state-of-the-art results, with improvements of +54% in structural diversity and +27% in co-designability. To validate the broad utility of our approach, we further introduce Proteína-Atomística, a unified flow-based framework that jointly learns the distribution of protein backbone structure, discrete sequences, and atomistic side chains without latent variables. We again find that training on our new sequence-structure data dramatically boosts benchmark performance, improving Proteína-Atomística’s structural diversity by +73% and co-designability by +5%. Our work highlights the critical importance of aligned sequence-structure data for training high-performance de novo protein design models. All data will be publicly released.
2025-09-24
NeurIPS.cc/2025/Workshop/AI4Science (poster)
openreview.net
openreview.net
Correction to: Assessing the adoption of security policies by developers in terraform across different cloud providers
Alexandre Verdet
Mohammad Hamdaqa
Leuson Da Silva
Foutse Khomh
2025-09-24
Empirical Software Engineering (publié)
doi.org
Correction to: Assessing the adoption of security policies by developers in terraform across different cloud providers
Alexandre Verdet
Mohammad Hamdaqa
Leuson Da Silva
Foutse Khomh
2025-09-24
Empirical Software Engineering (publié)
doi.org
Correction to: Assessing the adoption of security policies by developers in terraform across different cloud providers
Alexandre Verdet
Mohammad Hamdaqa
Leuson Da Silva
Foutse Khomh
2025-09-24
Empirical Software Engineering (publié)
doi.org
Large Pre-Trained Models for Bimanual Manipulation in 3D
Hanna Yurchyk
Wei-Di Chang
Gregory Dudek
David Meger
We investigate the integration of attention maps from a pre-trained Vision Transformer into voxel representations to enhance bimanual roboti… (voir plus)c manipulation. Specifically, we extract attention maps from DINOv2, a self-supervised ViT model, and interpret them as pixel-level saliency scores over RGB images. These maps are lifted into a 3D voxel grid, resulting in voxel-level semantic cues that are incorporated into a behavior cloning policy. When integrated into a state-of-the-art voxel-based policy, our attention-guided featurization yields an average absolute improvement of 8.2% and a relative gain of 21.9% across all tasks in the RLBench bimanual benchmark.
2025-09-24
ArXiv (prépublication)
arxiv.org
arxiv.org
Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs
Ziling Cheng
Meng Cao
Marc-Antoine Rondeau
Jackie Cheung
The widespread success of LLMs on NLP benchmarks has been accompanied by concerns that LLMs function primarily as stochastic parrots that re… (voir plus)produce texts similar to what they saw during pre-training, often erroneously. But what is the nature of their errors, and do these errors exhibit any regularities? In this work, we examine irrelevant context hallucinations, in which models integrate misleading contextual cues into their predictions. Through behavioral analysis, we show that these errors result from a structured yet flawed mechanism that we term _class-based (mis)generalization_, in which models combine abstract class cues with features extracted from the query or context to derive answers. Furthermore, mechanistic interpretability experiments on Llama-3, Mistral, and Pythia across 39 factual recall relation types reveal that this behavior is reflected in the model's internal computations: (i) abstract class representations are constructed in lower layers before being refined into specific answers in higher layers, (ii) feature selection is governed by two competing circuits --- one prioritizing direct query-based reasoning, the other incorporating contextual cues --- whose relative influences determine the final output. Our findings provide a more nuanced perspective on the stochastic parrot argument: through form-based training, LLMs can exhibit generalization leveraging abstractions, albeit in unreliable ways based on contextual cues — what we term _stochastic chameleons_.
2025-09-24
colmweb.org/COLM/2025/Workshop/INTERPLAY (publié)
openreview.net
openreview.net
Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs
Ziling Cheng
Meng Cao
Marc-Antoine Rondeau
Jackie Cheung
The widespread success of LLMs on NLP benchmarks has been accompanied by concerns that LLMs function primarily as stochastic parrots that re… (voir plus)produce texts similar to what they saw during pre-training, often erroneously. But what is the nature of their errors, and do these errors exhibit any regularities? In this work, we examine irrelevant context hallucinations, in which models integrate misleading contextual cues into their predictions. Through behavioral analysis, we show that these errors result from a structured yet flawed mechanism that we term _class-based (mis)generalization_, in which models combine abstract class cues with features extracted from the query or context to derive answers. Furthermore, mechanistic interpretability experiments on Llama-3, Mistral, and Pythia across 39 factual recall relation types reveal that this behavior is reflected in the model's internal computations: (i) abstract class representations are constructed in lower layers before being refined into specific answers in higher layers, (ii) feature selection is governed by two competing circuits --- one prioritizing direct query-based reasoning, the other incorporating contextual cues --- whose relative influences determine the final output. Our findings provide a more nuanced perspective on the stochastic parrot argument: through form-based training, LLMs can exhibit generalization leveraging abstractions, albeit in unreliable ways based on contextual cues — what we term _stochastic chameleons_.
2025-09-24
colmweb.org/COLM/2025/Workshop/INTERPLAY (publié)
openreview.net
openreview.net
Beyond Na\"ive Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs
Arjun Ashok
Andrew Robert Williams
Vincent Zhihao Zheng
Irina Rish
Nicolas Chapados
Étienne Marcotte
Valentina Zantedeschi
Alexandre Drouin
Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (voir plus)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via na\"ive direct prompting, their full potential remains underexplored. We address this gap with 4 strategies, providing new insights into the zero-shot capabilities of LLMs in this setting. ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context independently from its forecast accuracy. CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines. IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models. Finally, RouteDP optimizes resource efficiency by using LLMs to estimate task difficulty, and routing the most challenging tasks to larger models. Evaluated on different kinds of context-aided forecasting tasks from the CiK benchmark, our strategies demonstrate distinct benefits over na\"ive prompting across LLMs of different sizes and families. These results open the door to further simple yet effective improvements in LLM-based context-aided forecasting.
2025-09-23
NeurIPS.cc/2025/Workshop/BERT2S (publié)
doi.org
openreview.net