Publications

Learning the Principles of T Cell Antigen Discernment
François X. P. Bourassa
Sooraj Achar
Grégoire Altan-Bonnet
Paul François
T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable… (see more) specificity and sensitivity. Quantitatively understanding how T cell receptors discern among antigens requires biophysical models and theoretical analyses of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the context of modern experimental and computational advances. Antigen potency spans a continuum and exhibits nonlinear effects within complex mixtures, challenging discrete classification and simple threshold-based models. This complexity motivates the development of models, such as adaptive kinetic proofreading, that integrate both activating and inhibitory signals. Advances in high-throughput technologies now generate large-scale, quantitative data sets, enabling the refinement of such models through statistical and machine learning approaches. This convergence of theory, data, and computation promises deeper insights into immune decision-making and opens new avenues for rational immunotherapy design.
2026-02-04
Annual Review of Biophysics (published)
doi.org
Logarithmic-time Schedules for Scaling Language Models with Momentum
Damien Ferbach
Courtney Paquette
Gauthier Gidel
Katie Everett
Elliot Paquette
In practice, the hyperparameters …
2026-02-04
ArXiv (preprint)
arxiv.org
arxiv.org
Mining Generalizable Activation Functions
Alex Vitvitskyi
Michael Boratko
Matej Grcic
Razvan Pascanu
Deep Shah
Petar Veličković
2026-02-04
ArXiv (preprint)
arxiv.org
arxiv.org
QMAP: A Benchmark for Standardized Evaluation of Antimicrobial Peptide MIC and Hemolytic Activity Regression
Anthony Lavertu
Jacques Corbeil
Pascal Germain
Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics, but progress in computational AMP discovery has been d… (see more)ifficult to quantify due to inconsistent datasets and evaluation protocols. We introduce QMAP, a domain-specific benchmark for predicting AMP antimicrobial potency (MIC) and hemolytic toxicity (HC50) with homology-aware, predefined test sets. QMAP enforces strict sequence homology constraints between training and test data, ensuring that model performance reflects true generalization rather than overfitting. Applying QMAP, we reassess existing MIC models and establish baselines for MIC and HC50 regression. Results show limited progress over six years, poor performance for high-potency MIC regression, and low predictability for hemolytic activity, emphasizing the need for standardized evaluation and improved modeling approaches for highly potent peptides. We release a Python package facilitating practical adoption, and with a Rust-accelerated engine enabling efficient data manipulation, installable with pip install qmap-benchmark.
2026-02-03
bioRxiv (preprint)
doi.org
Reporting and Reviewing LLM-Integrated Systems in HCI: Challenges and Considerations
Karla Felix Navarro
Eugene Syriani
Ian Arawjo
What should HCI scholars consider when reporting and reviewing papers that involve LLM-integrated systems? We interview 18 authors of LLM-in… (see more)tegrated system papers on their authoring and reviewing experiences. We find that norms of trust-building between authors and reviewers appear to be eroded by the uncertainty of LLM behavior and hyperbolic rhetoric surrounding AI. Authors perceive that reviewers apply uniquely skeptical and inconsistent standards towards papers that report LLM-integrated systems, and mitigate mistrust by adding technical evaluations, justifying usage, and de-emphasizing LLM presence. Authors'views challenge blanket directives to report all prompts and use open models, arguing that prompt reporting is context-dependent and justifying proprietary model usage despite ethical concerns. Finally, some tensions in peer review appear to stem from clashes between the norms and values of HCI and ML/NLP communities, particularly around what constitutes a contribution and an appropriate level of technical rigor. Based on our findings and additional feedback from six expert HCI researchers, we present a set of guidelines and considerations for authors, reviewers, and HCI communities around reporting and reviewing papers that involve LLM-integrated systems.
2026-02-03
ArXiv (preprint)
arxiv.org
arxiv.org
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim
Minsu Kim
Celine ROGET
D. Biton
Louis Vaillancourt
Yves V. Brun
Yoshua Bengio
Alex Hernandez-Garcia
2026-02-03
arXiv (preprint)
doi.org
arxiv.org
A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures
Basile Terver
Randall Balestriero
Megi Dervishi
David Fan
Quentin Garrido
Tushar Nagarajan
Koustuv Sinha
Wancong Zhang
Michael G. Rabbat
Yann Lecun
Amir Bar
We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEP… (see more)As). JEPAs learn to predict in representation space rather than pixel space, avoiding the pitfalls of generative modeling while capturing semantically meaningful features suitable for downstream tasks. Our library provides modular, self-contained implementations that illustrate how representation learning techniques developed for image-level self-supervised learning can transfer to video, where temporal dynamics add complexity, and ultimately to action-conditioned world models, where the model must additionally learn to predict the effects of control inputs. Each example is designed for single-GPU training within a few hours, making energy-based self-supervised learning accessible for research and education. We provide ablations of JEA components on CIFAR-10. Probing these representations yields 91% accuracy, indicating that the model learns useful features. Extending to video, we include a multi-step prediction example on Moving MNIST that demonstrates how the same principles scale to temporal modeling. Finally, we show how these representations can drive action-conditioned world models, achieving a 97% planning success rate on the Two Rooms navigation task. Comprehensive ablations reveal the critical importance of each regularization component for preventing representation collapse. Code is available at https://github.com/facebookresearch/eb_jepa.
2026-02-02
ArXiv (preprint)
arxiv.org
arxiv.org
Observational Study of Maternal and Fetal Outcome in Posterior Reversible Encephalopathy Syndrome in Eclamptic Women in a Tertiary Care Institute
Prerna Kailashchand Gupta
Meenal Shailesh Sarmalkar
Madhuri A Mehendale
Aishwarya Agrawal
2026-02-02
Journal of South Asian Federation of Obstetrics and Gynaecology (published)
doi.org
Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
Aneri Muni
Vincent Taboga
Esther Derman
Pierre-Luc Bacon
Erick Delage
Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastr… (see more)ophic events. Unlike risk-neutral objectives, the static CVaR of the return depends on entire trajectories without admitting a recursive Bellman decomposition in the underlying Markov decision process. A classical resolution relies on state augmentation with a continuous variable. However, unless restricted to a specialized class of admissible value functions, this formulation induces sparse rewards and degenerate fixed points. In this work, we propose a novel formulation of the static CVaR objective based on augmentation. Our alternative approach leads to a Bellman operator with: (1) dense per-step rewards; (2) contracting properties on the full space of bounded value functions. Building on this theoretical foundation, we develop risk-averse value iteration and model-free Q-learning algorithms that rely on discretized augmented states. We further provide convergence guarantees and approximation error bounds due to discretization. Empirical results demonstrate that our algorithms successfully learn CVaR-sensitive policies and achieve effective performance-safety trade-offs.
2026-02-02
ArXiv (preprint)
arxiv.org
arxiv.org
Robust Intervention Learning from Emergency Stop Interventions
Ethan Pronovost
Khimya Khetarpal
Siddhartha Srinivasa
2026-02-02
ArXiv (preprint)
arxiv.org
arxiv.org
How Notations Evolve: A Historical Analysis with Implications for Supporting User-Defined Abstractions
jingyue zhang
J.D. Zamfirescu-Pereira
Elena L. Glassman
Damien Masson
Ian Arawjo
2026-02-01
ArXiv (preprint)
arxiv.org
arxiv.org
Sub-optimality bounds for certainty equivalent policies in partially observed systems
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical… (see more) certainty equivalence principle for linear systems with output feedback and quadratic costs is as follows: the optimal action at each time is obtained by evaluating the optimal state-feedback policy of the stochastic linear system at the minimum mean square error (MMSE) estimate of the state. Motivated by this interpretation, we consider certainty equivalent policies for general (non-linear) partially observed stochastic systems that allow for any state estimate rather than restricting to MMSE estimates. In such settings, the certainty equivalent policy is not optimal. For models where the cost and the dynamics are smooth in an appropriate sense, we derive upper bounds on the sub-optimality of certainty equivalent policies. We present several examples to illustrate the results.
2026-02-01
ArXiv (preprint)
arxiv.org
arxiv.org