Publications

Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks
Ezekiel Williams
Alexandre Payeur
Guillaume Lajoie
Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information tha… (voir plus)t can plausibly be used during learning. A common strategy to satisfy these constraints is to modify gradient descent by neglecting non-local terms to varying degrees, as in random feedback local online (RFLO) learning and truncated backpropagation through time (tBPTT). However, the learning dynamics of these algorithms, and how they compare with BPTT, remain poorly understood. We apply dynamical systems theory to data-aligned linear RNNs -- whose dynamics can be separated into orthogonal modes -- to compare stationary solutions, stability properties, and convergence rates, finding qualitatively distinct behaviour for RFLO versus BPTT and one-step tBPTT. We further observe that the solutions learned by RFLO are restricted to low-rank perturbations of initial parameters, a result which holds beyond the data-aligned setting. Our work provides analytical insight into how locality constraints shape learning dynamics, with implications for neuroscientific models of learning and alternative optimization approaches for RNNs.
2026-05-28
arXiv (prépublication)
doi.org
arxiv.org
MosaicLeaks:Privacy Risks in Querying-in-the-Open for Deep Research Agents
Alexander Gurung
Spandana Gella
Alexandre Drouin
Issam H. Laradji
Perouz Taslakian
Rafael Pardinas
Deep research agents increasingly combine private local documents with external tools like web retrieval, creating a privacy risk: an agent'… (voir plus)s external queries may leak sensitive information from its local context. This risk is amplified by the mosaic effect, where individual queries may appear harmless but become revealing in aggregate. We introduce MosaicLeaks, a benchmark of 1,001 multi-hop deep research tasks that chain private enterprise documents and a public web corpus, forcing agents to make external queries that depend on local information. We evaluate leakage with an adversary LLM that observes only the agent's external queries and attempts to infer private information at three levels: the agent's research intent, answers to specific private questions and verifiable claims about the enterprise documents. We find that models across families and sizes frequently leak at all three levels, that zero-shot privacy prompting reduces but does not eliminate leakage and that reinforcement learning for task performance alone worsens leakage. To address this, we propose Privacy-Aware Deep Research (PA-DR), an RL framework that combines situational rewards for task success with a learned privacy classifier to provide dense credit assignment over both per-query and mosaic-level leakage. Training Qwen3-4B-Instruct with PA-DR improves accuracy from 48.7% to 58.7% and reduces answer and full-information leakage from 34.0% to 9.9%.
2026-05-28
arXiv (prépublication)
doi.org
arxiv.org
Quantitative Equational Logic
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon Plotkin
We develop a quantitative analogue of equational reasoning, which we call quantitative equational logic. The quantitative equations use, ins… (voir plus)tead of classical equality, quantitative equalities, which are equalities indexed with nonnegative reals. Thus, s = ε t means that “ s and t are points in a metric space and their distance is less than ε”. Quantitative equalities will be used to encode behavioural distances, with ε being an upper bound on the measure of dissimilarity between two terms. We develop the metatheory of this subject. We define a notion of quantitative algebra, which is the quantitative analogue of universal algebra. We prove a completeness theorem for quantitative equational logic, and we show that we obtain monads on suitable categories of metric spaces. We present a set of examples where the free algebra of a quantitative equational theory corresponds to some well-known structure. These examples are: Hausdorff metrics from quantitative semilattices; p -Wasserstein metrics (hence also the Kantorovich metric), and the total variation metric.
2026-05-28
Journal of the ACM (publié)
doi.org
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
Jonathan Colaço Carr
Prakash Panangaden
Doina Precup
Benjamin Van Roy
Reinforcement learning problems typically define the goal as maximizing the expected value of a scalar reward function. But, pairwise prefer… (voir plus)ences are often easier to specify than scalar rewards, and they express certain goals that scalar rewards cannot. Methods for reinforcement learning with pairwise preferences have thus received growing interest. Unfortunately, these methods are inefficient in problems with long time horizons, and they lack guarantees on the performance of Markov policies relative to history-dependent policies, which bridge the theory and practice of reinforcement learning. We therefore propose the \textit{Markov decision contest} as a new problem model for reinforcement learning with pairwise preferences. We prove that stationary Markov policies are optimal among all history-dependent policies, that solving a Markov decision contest exactly is in P, and that a simple iterative algorithm converges to an optimal policy at a sublinear rate. Lastly, in a set of high-dimensional decision problems with long time horizons, we show that our approximate algorithm is significantly more learning-efficient than prior work.
2026-05-28
arXiv (prépublication)
doi.org
arxiv.org
Beyond Go/No-Go Decisions: A Regional Selection Framework for Uncertainty-Aware Molecule Screening
Weihua Shi
Yixuan Li
Tian Bai
Yijie Zhang
Kaiqiong Zhao
Marc‐André Legault
Hui Peng
Yue Zhao
Eric D. Kolaczyk
Xiang Yu
Archer Y. Yang
In drug discovery, quantitative structure–activity relationship (QSAR) models are widely used to guide Go/No-Go decisions within the Desig… (voir plus)n–Make–Test–Analyze (DMTA) cycle. However, conventional decision heuristics typically rely on a single cutoff, leading to a rigid binary select/discard paradigm. This approach is particularly ill-suited for borderline compounds near the decision boundary, where screening decisions are especially sensitive to prediction uncertainty and premature choices may either discard viable leads or advance likely failures, thereby increasing downstream assay costs. To address this limitation, we propose Regional Selection (RS), an uncertainty-aware three-way decision framework that partitions compounds into Predicted Pass, Predicted Fail, and Predicted Indeterminate regions. By explicitly reserving high-uncertainty compounds for targeted follow-up, RS avoids the pitfalls of premature binary classification. We formalize this framework through Regional Selection Inference (RSI), which casts region assignment as a multiple-hypothesis testing problem. We develop two imple- mentations of RSI: an empirical calibration-based method (RSI-EC), which thresholds uncertainty-normalized scores via empirical calibration, and a conformal selectionbased method (RSI-CS), which constructs conformal p-values for region assignment. RSI-EC is supported by large-sample calibration arguments, whereas RSI-CS provides finite-sample, distribution-free guarantees under exchangeability. Extensive evaluations across 15 high-dimensional QSAR benchmarks show that both RSI procedures reliably control the false discovery rate while maintaining high screening power. In limited-data regimes, RSI-CS yields particularly stable FDR control, whereas RSI-EC can be slightly less conservative; both perform strongly as sample sizes increase. We further study a cost-aware extension that incorporates asymmetric downstream costs through the score construction while keeping the nominal FDR target fixed. This extension introduces a tuning parameter that can reduce realized downstream cost, with dataset-dependent trade-offs against screening power. Overall, RSI offers a mathematically grounded and resource-aware alternative to single-threshold screening, allowing discovery teams to better balance decision confidence with assay budgets.
2026-05-27
ChemRxiv (accepté)
doi.org
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
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Yu Li
Jun Ding
Yue Li
Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) enables investigation of open chromatin landscapes at si… (voir plus)ngle-cell resolution, but its analysis remains challenging because of sparsity, noise, and dataset-specific peak vocabularies. Genome Foundation Models (GFMs), pre-trained on large DNA sequence corpora, offer a potential source of transferable sequence information for scATAC-seq modeling. We introduce the Genome Foundation Embedded Topic Model (\model{}), an interpretable framework that combines GFMs with the Embedded Topic Model (ETM) for sequence-informed scATAC-seq analysis. By integrating GFM-derived DNA sequence embeddings into a topic-model decoder, \model{} improves clustering quality on standard benchmarks and captures cell-state-specific transcription factor activity through motif scoring and attention-based interpretation.
2026-05-27
FM4LS @ International Conference on Machine Learning (poster)
openreview.net
openreview.net
ImmunoFoundation: A Multimodal Foundation Model for Immunogenicity Prediction and Peptide Optimization
João Felipe Rocha
Hiren Madhu
Jenny Yongjia Liu
Apurva Mishra
Chen Liu
Rishabh Anand
Rex Ying
Smita Krishnaswamy
Peptide immunogenicity, whether a peptide presented by an MHC molecule elicits a T-cell response, is central to designing vaccines, cancer i… (voir plus)mmunotherapy, and therapeutic proteins. Existing tools rely on a single modality, such as peptide sequences or peptide-MHC interactions, and often ignore the T-cell response that depends on the TCR-peptide-MHC complex (TCR-pMHC) and its three-dimensional structure. The scarcity of labeled TCR-pMHC data with known structures makes it difficult to build a model that captures how all components of the TCR-pMHC contribute to immunogenicity. However, a foundation model of TCR-pMHCs can learn transferable representations across components, which can be adapted to immunogenicity, binding, and TCR specificity tasks, even with limited labeled data. We introduce **ImmunoFoundation**, a self-supervised multimodal backbone for protein-complex representation, fine-tuned for peptide--MHC immunogenicity. The model couples an ESM-2 sequence encoder with a graph transformer over structure, fused via cross-modal attention. Pretraining follows a curriculum that progressively introduces structural inductive bias. **ImmunoFoundation** ourperforms prior multimodal class-I predictors on cancer neoepitope and infectious-disease tasks.
2026-05-27
FM4LS @ International Conference on Machine Learning (poster)
openreview.net
openreview.net
Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
Aditi Khandelwal
Marius Mosbach
Verna Dankers
Siva Reddy
Golnoosh Farnadi
Mixture-of-Experts (MoE) models are widely used to scale language models, yet their expert routing behavior and adaptation in a multilingual… (voir plus) setting remain underexplored. In this work, we study multilingual routing dynamics during continual pre-training of an English-centric MoE model on a multilingual corpus, analyzing how expert usage varies across languages. We find that continual multilingual pre-training leads to diffused, language-agnostic routing in early and middle layers, with language specialization primarily emerging in the final layers. We also show that token-level vocabulary overlap between languages plays an important role in how languages are routed. Motivated by these findings, we propose a parameter-efficient adaptation strategy that updates language-specific and shared experts in the final MoE layers. Experiments on MultiBLiMP and Belebele show that our method achieves a strong performance-efficiency trade-off, attaining competitive performance relative to fine-tuning complete final layers, while updating less than 2% of the parameters. Overall, our findings provide insights into where and how language specialization emerges in MoEs during continual pre-training and provide practical insights for low-resource multilingual adaptation. Our code is available at https://github.com/aditi184/moe-routing-adaptation.
2026-05-27
arXiv (prépublication)
doi.org
arxiv.org
Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth
Gaurav Sahu
Laurent Charlin
Christopher Pal
We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human referen… (voir plus)ce list as an evaluation target. First, we implement a Deep Research pipeline that processes the full query paper and expands the retrieved results breadth-first along their bibliographies, and show that it substantially outperforms vanilla API-only search, raising recall on RollingEval-Jun25 (a 250-paper literature-search benchmark) from below 20% to above 80%. Second, we use a neutral LLM-as-a-judge to determine if human references are sound ground truth for the task. We find significant limitations: only 51% of human citations are judged moderately relevant or higher, against 86--88% for the strongest AI-based re-rankers. We study this gap on the OpenAlex co-authorship graph, finding that humans are 2.5x more likely than the best AI re-rankers to cite a direct collaborator. Together, our results argue against single-axis literature-search evaluation: recall, topical-relevance scoring, ranked-list diversity, and a co-authorship-distance diagnostic each measure complementary properties of citation quality and should be reported jointly.
2026-05-27
arXiv (prépublication)
doi.org
arxiv.org
STING dampens the unfolded protein response to enable the presentation of self-antigens on MHC-I during inflammation
Ahmed M. Fahmy
Ali Ahmadi
Joël Lanoix
Tyler Cannon
moustafa Nouh Badr Elemeery
Camberly Hernandez Paredes
Benoit Barrette
Éric Bonneil
Yong Zhong Xu
Maha Ibrahim
Guillermo Arango-Duque
Éric Audemard
Sébastien Lemieux
Éric Chevet
Erwin Schurr
P. Pierre
Samantha Gruenheid
Pierre Thibault
Heidi M. McBride
Michel Desjardins
Summary A growing body of evidence supports the contribution of the long-lasting adaptive immune system in Parkinson’s disease (PD). We sh… (voir plus)owed that the PD-associated protein PINK1 negatively regulates the presentation of mitochondrial antigens (MitAP) on MHC-I molecules. In vivo evidence indicated that MitAP activation in mice, in the absence of PINK1, led to cytotoxic CD8 + T cell stimulation and severe motor impairments, reversible by L-DOPA. We show here that following TLR4 activation, MitAP is engaged through a pathway involving cGAS-STING, which acts as a rheostat to dampen the unfolded protein response (UPR). Without STING, the stress response is amplified, leading to a translational attenuation that inhibits the expression of XBP1s, a transcription factor required for MitAP. STING activity also regulates the repertoire of peptides displayed at the cell surface during inflammation, highlighting a potential role in immunosurveillance. These findings establish STING and the UPR as key immune regulators targetable for therapeutic intervention during autoimmune diseases and PD.
2026-05-27
bioRxiv (prépublication)
doi.org
UniSafe: Modality-Agnostic Hateful Content Detection via Shared-Space Projection
Siam Shibly Antar
Syem Shibly Ador
Steven H. H. Ding
Benjamin C. M. Fung
2026-05-27
ACM Web Conference (publié)
doi.org