Publications

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… (see more)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 (accepted)
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 … (see more)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 (preprint)
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… (see more)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… (see more)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… (see more) 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 (preprint)
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… (see more)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 (preprint)
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… (see more)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 (accepted)
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 (published)
doi.org
BlabberSeg: Semantic Perception for Reliable Open-Vocabulary UAV Safe Landing
Haechan Mark Bong
Giovanni Beltrame
Reliable robot autonomy requires semantic perception that remains both informative and fast enough for closed-loop safety decisions. We pres… (see more)ent BlabberSeg, an optimized CLIPSeg-based open-vocabulary segmentation pipeline for UAV emergency landing. The method targets semantic reliability under edge constraints by reusing prompt, positional, and image features and deploying floating-point 16 ONNX (TensorRT) inference. In a DOVESEI-based safe-landing workflow, BlabberSeg reaches 16.78Hz on Jetson Orin AGX (64GB), a 927.41% speed increase over the original CLIPSeg (1.81Hz), with limited degradation in segmentation agreement (2.1% relative area difference) and mIoU (9%). At the task level, safe-landing success is preserved (76/100, matching baseline) while mission time is substantially reduced. These results support semantic open-vocabulary perception as a practical component for reliable autonomous landing.
2026-05-26
SRRA @ IEEE International Conference on Robotics and Automation (poster)
openreview.net
openreview.net
Dr-CiK: A Testbed for Foresight-Driven Agents
Yihong Tang
Andrew Williams
Arjun Ashok
Vincent Zhihao Zheng
Lijun Sun
Alexandre Drouin
Issam H. Laradji
Étienne Marcotte
Valentina Zantedeschi
Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be … (see more)actively discovered from noisy, heterogeneous information sources. Yet existing context-aided forecasting benchmarks typically assume that the supporting context is already provided, leaving open whether agents can identify it on their own. Therefore, we introduce Dr-CiK, a benchmark for evaluating whether agents can retrieve forecasting-relevant supporting context from a document corpus, filter out distractors, distill the retrieved context into forecast-useful evidence, and generate forecasts supported by that evidence. Through context ablations and evaluations of state-of-the-art deep research and forecasting methods paired together, we show that high-quality context substantially improves forecasting performance in Dr-CiK. However, most existing DR agents recover only a small fraction of the ground-truth supporting evidence (usually <5%), are frequently misled by distractors (>80% distractor citations), and can cause forecasters to perform worse with retrieved context than without context. Our results motivate research on foresight-driven agents that search for the right context to predict the future.
2026-05-26
arXiv (preprint)
doi.org
arxiv.org
Dynamics of Stochastic Momentum with Sparse Updates in High Dimensions
Katie Everett
Elliot Paquette
Existing theory of momentum assumes that gradients arrive at every parameter at a roughly constant rate, an assumption violated in practice … (see more)by heavy-tailed data distributions and modern architectures. We theoretically analyze the dynamics of two tractable models of momentum under sparse updates: a least squares model with sparse inputs and a logistic regression model with a rare class. Both admit exact closed-form second-moment dynamics whose high-dimensional limits we characterize across three scaling exponents for sparsity, batch size, and momentum decay. The phase structure on both problems is governed by the ratio of two intrinsic timescales: a momentum retention timescale (how many active updates the buffer survives) and a learning timescale (how many active updates it takes to reduce the squared error). When learning is much slower than retention, the limit matches SGD; when learning is faster, the system is unstable; where the timescales coincide, we recover classical heavy-ball dynamics. The oscillatory dynamics occur at different momentum values for different token sparsity, creating a spectral conflict for global momentum across token frequencies.
2026-05-26
arXiv (preprint)
doi.org
arxiv.org
Online Sketched Newton-Raphson
Jean-Luc Lupien
Yuen-Man Pun
Youssef Diouane
Iman Shames
Antoine Lesage-Landry
In online convex optimization (OCO), a decision-maker is confronted with an unknown environment and seeks to play an optimal sequence of dec… (see more)isions on a short time-scale using only past information. Recent advances in second-order OCO methods have demonstrated tighter regret bounds and improved empirical performance over traditional first-order methods. However, this performance comes at a cost: a matrix inversion is now required, which scales with the cube of the size of the problem. In this work, we propose sketching to mitigate this limitation. Specifically, we present the online sketched Newton-Raphson method (OSNR) which preserves the tight regret bounds obtained with second-order methods while presenting a strict computational improvement in terms of complexity. We discuss three application scenarios of OSNR: online root finding, unconstrained OCO, and time-varying equality-constrained OCO, and present their respective regret and a constraint violation bound for the latter. In all three applications, OSNR achieves sublinear dynamic regret bounds. For the equality-constrained case, the extension OSNR with equality constraints OSNR-EC is shown to yield sublinear cumulative constraint violation. Finally, we illustrate the performance of OSNR and OSNR-EC on two numerical examples, viz., online position tracking and optimal power flow, and observe that OSNR and OSNR-EC exhibit high performance even at low sampling rates.
2026-05-26
arXiv (preprint)
doi.org
arxiv.org