Publications

OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community
Maxime C. Cohen
Tinglong Dai
Georgia Perakis
Narendra Agrawal
Gad Allon
Robert N. Boute
Gérard P. Cachon
Zhe Chen
Morris A. Cohen
Rares Cristian
Vinayak Deshpande
Francis de Véricourt
Jan C. Fransoo
Joren Gijsbrechts
Pavithra Harsha
Ming Hu
Pınar Keskinocak
Caleb Kwon
Hau L. Lee
Sheng Liu … (see 22 more)
Konstantina Mellou
Ishai Menache
Jason W. Miller
Serguei Netessine
Tava Lennon Olsen
Jeevan Pathuri
Robert Peels
Yongzhi Qi
Ananth Raman
Anne G. Robinson
Zuo-Jun Max Shen
Masha Shunko
David Simchi-Levi
Hannah K. Smalley
Jing-Sheng Song
Jayashankar M. Swaminathan
Christopher S. Tang
Sridhar Tayur
Maximiliano Udenio
Jan Van Mieghem
Yuqian Xu
Dennis Zhang
Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impac… (see more)t depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain management: intelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decision making, risk management, and human–machine collaboration and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AI’s promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to human–AI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.
2026-03-25
Manufacturing & Service Operations Management (published)
doi.org
On the Objective and Feature Weights of Minkowski Weighted k-Means
Renato Cordeiro De Amorim
Vladimir Makarenkov
The Minkowski weighted k-means (mwk-means) algorithm extends classical k-means by incorporating feature weights and a Minkowski distance. De… (see more)spite its empirical success, its theoretical properties remain insufficiently understood. We show that the mwk-means objective can be expressed as a power-mean aggregation of within-cluster dispersions, with the order determined by the Minkowski exponent p. This formulation reveals how p controls the transition between selective and uniform use of features. Using this representation, we derive bounds for the objective function and characterise the structure of the feature weights, showing that they depend only on relative dispersion and follow a power-law relationship with dispersion ratios. This leads to explicit guarantees on the suppression of high-dispersion features. Finally, we establish convergence of the algorithm and provide a unified theoretical interpretation of its behaviour.
2026-03-25
arXiv (preprint)
doi.org
arxiv.org
Unboundedness in Bilevel Optimization
Bárbara Rodrigues
Margarida Carvalho
Miguel Anjos
Nagisa Sugishita
Abstract Bilevel optimization has garnered growing interest over the past decade. However, little attention has been paid to detecting and d… (see more)ealing with unboundedness in these problems, with most research assuming a bounded high-point relaxation. In this paper, we address unboundedness in bilevel and multilevel optimization by studying its computational complexity. We show that deciding whether an optimistic linear bilevel problem is unbounded is strongly NP-complete, even without coupling constraints. Furthermore, we extend the hardness result to the linear multilevel case, by showing that for each extra level added, the decision problem of checking unboundedness moves up a level in the polynomial hierarchy. Deciding unboundedness of a mixed-integer multilevel problem is shown to be one level higher in the polynomial complexity hierarchy than the decision problem for linear multilevel problem with the same number of levels. Finally, we introduce two algorithmic approaches to determine whether a linear bilevel problem is unbounded and, if so, return a certificate of unboundedness. This certificate consists of a direction of unboundedness and corresponding bilevel feasible point. We present a proof of concept of these algorithmic approaches on some relevant examples, and provide a brief computational comparison.
2026-03-25
Mathematical Programming (published)
doi.org
Multiscale reorganization of brain and behavior under large-scale electrical perturbation
Sarah Kreuzer
Juergen Dukart
Justine Y. Hansen
Hoang K. Nguyen
Michael Bentsch
Sophia Zieger
Katrin Sakreida
Thomas C. Baghai
Caroline Nothdurfter
Michael Groezinger
Bogdan Draganski
Bratislav Misic
Danilo Bzdok
Simon B. Eickhoff
Timm B. Poeppl
2026-03-24
bioRxiv (preprint)
doi.org
PrismBench: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
The rapid advancement of LLMs' code generation capabilities is outpacing traditional evaluation methods. Static benchmarks fail to capture t… (see more)he depth and breadth of LLM capabilities and eventually become obsolete, while most dynamic approaches either rely too heavily on LLM-based evaluation or remain constrained by predefined test sets. To address these issues, we introduce PrismBench, a multi-agent, dynamic benchmarking framework designed to systematically expose and analyze LLM failure modes in code generation tasks. We formulate evaluation as a Markov Decision Process over a structured tree of coding challenges, leveraging a customized Monte Carlo Tree Search algorithm to traverse this tree and discover high-failure scenarios. Our multi-agent setup orchestrates task generation, model response, and analysis, enabling scalable assessment across diverse coding challenges. Additionally, we propose metrics that combine structural traversal patterns with performance across different tasks and difficulty levels to enable diagnostic and systematic comparison of LLMs' performance. We conduct extensive experiments on eight state-of-the-art LLMs and analyze how model architecture and scale influence code generation performance across varying coding tasks. All code, evaluation trees, and a public leaderboard are available at https://prismbench.github.io/Demo/
2026-03-24
Transactions on Machine Learning Research (accepted)
openreview.net
openreview.net
Scenes partitioning and annotations of Super Mario Bros. levels
Hugo Delhaye
Yann Harel
Basile Pinsard
Lune Bellec
2026-03-24
Zenodo (unknown)
doi.org
Estimating Individual Tree Height and Species from UAV Imagery
J. Endres
Étienne Laliberté
David Rolnick
Arthur Ouaknine
Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aeria… (see more)l Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.
2026-03-23
arXiv (preprint)
doi.org
arxiv.org
Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction
Dimitrios Sinodinos
Bahareh Nikpour
Jack Y. Wei
Sushant Sinha
Xiaoping Ma
Kashif Rehman
Stephen Yue
Narges Armanfard
Accurate prediction of mechanical properties of steel during hot rolling processes, such as Thin Slab Direct Rolling (TSDR), remains challen… (see more)ging due to complex interactions among chemical compositions, processing parameters, and resultant microstructures. Traditional empirical and experimental methodologies, while effective, are often resource-intensive and lack adaptability to varied production conditions. Moreover, most existing approaches do not explicitly leverage the strong correlations among key mechanical properties, missing an opportunity to improve predictive accuracy through multitask learning. To address this, we present a multitask learning framework that injects multitask awareness into the prior of TabPFN--a transformer-based foundation model for in-context learning on tabular data--through novel fine-tuning strategies. Originally designed for single-target regression or classification, we augment TabPFN's prior with two complementary approaches: (i) target averaging, which provides a unified scalar signal compatible with TabPFN's single-target architecture, and (ii) task-specific adapters, which introduce task-specific supervision during fine-tuning. These strategies jointly guide the model toward a multitask-informed prior that captures cross-property relationships among key mechanical metrics. Extensive experiments on an industrial TSDR dataset demonstrate that our multitask adaptations outperform classical machine learning methods and recent state-of-the-art tabular learning models across multiple evaluation metrics. Notably, our approach enhances both predictive accuracy and computational efficiency compared to task-specific fine-tuning, demonstrating that multitask-aware prior adaptation enables foundation models for tabular data to deliver scalable, rapid, and reliable deployment for automated industrial quality control and process optimization in TSDR.
2026-03-23
arXiv (preprint)
doi.org
arxiv.org
CanViT: Toward Active-Vision Foundation Models
Yohaï-Eliel Berreby
Sabrina Du
Audrey Durand
B. Suresh Krishna
Active computer vision promises efficient, biologically plausible perception through sequential, localized glimpses, but lacks scalable gene… (see more)ral-purpose architectures and pretraining pipelines. As a result, Active-Vision Foundation Models (AVFMs) have remained unexplored. We introduce CanViT, the first task- and policy-agnostic AVFM. CanViT uses scene-relative RoPE to bind a retinotopic Vision Transformer backbone and a spatiotopic scene-wide latent workspace, the canvas. Efficient interaction with this high-capacity working memory is supported by Canvas Attention, a novel asymmetric cross-attention mechanism. We decouple thinking (backbone-level) and memory (canvas-level), eliminating canvas-side self-attention and fully-connected layers to achieve low-latency sequential inference and scalability to large scenes. We propose a label-free active vision pretraining scheme, policy-agnostic passive-to-active dense latent distillation: reconstructing scene-wide DINOv3 embeddings from sequences of low-resolution glimpses with randomized locations, zoom levels, and lengths. We pretrain CanViT-B from a random initialization on 13.2 million ImageNet-21k scenes -- an order of magnitude more than previous active models -- and 1 billion random glimpses, in 166 hours on a single H100. On ADE20K segmentation, a frozen CanViT-B achieves 38.5% mIoU in a single low-resolution glimpse, outperforming the best active model's 27.6% with 19.5x fewer inference FLOPs and no fine-tuning, as well as its FLOP- or input-matched DINOv3 teacher. Given additional glimpses, CanViT-B reaches 45.9% ADE20K mIoU. On ImageNet-1k classification, CanViT-B reaches 81.2% top-1 accuracy with frozen teacher probes. CanViT generalizes to longer rollouts, larger scenes, and new policies. Our work closes the wide gap between passive and active vision on semantic segmentation and demonstrates the potential of AVFMs as a new research axis.
2026-03-22
arXiv (preprint)
doi.org
arxiv.org
MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data
Xingzhi Sun
João Felipe Rocha
Brett Phelan
Dhananjay Bhaskar
Guillaume Huguet
Yanlei Zhang
D. S. Magruder
Alexander Tong
Ke Xu
Oluwadamilola Fasina
Mark Gerstein
Guy Wolf
Natalia Ivanova
Christine L. Chaffer
Smita Krishnaswamy
Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disea… (see more)se. A key challenge is inferring continuous trajectories from discrete snapshots. Biological complexity stems from stochastic cell fate decisions, temporal proliferation changes, and spatial environmental influences. Current methods often use deterministic interpolations treating cells in isolation, failing to capture the probabilistic branching, population shifts, and niche-dependent signaling driving real biological processes. We introduce Manifold Interpolating Optimal-Transport Flow (MIOFlow) 2.0. This framework learns biologically informed cellular trajectories by integrating manifold learning, optimal transport, and neural differential equations. It models three core processes: (1) stochasticity and branching via Neural Stochastic Differential Equations; (2) non-conservative population changes using a learned growth-rate model initialized with unbalanced optimal transport; and (3) environmental influence through a joint latent space unifying gene expression with spatial features like local cell type composition and signaling. By operating in a PHATE-distance matching autoencoder latent space, MIOFlow 2.0 ensures trajectories respect the data's intrinsic geometry. Empirical comparisons show expressive trajectory learning via neural differential equations outperforms existing generative models, including simulation-free flow matching. Validated on synthetic datasets, embryoid body differentiation, and spatially resolved axolotl brain regeneration, MIOFlow 2.0 improves trajectory accuracy and reveals hidden drivers of cellular transitions, like specific signaling niches. MIOFlow 2.0 thus bridges single-cell and spatial transcriptomics to uncover tissue-scale trajectories.
2026-03-22
arXiv (preprint)
doi.org
arxiv.org
Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
Masoumeh Sharafi
Muhammad Zeeshan
Soufiane Belharbi
Alessandro L. Koerich
Marco Pedersoli
Eric Granger
Facial expression recognition (FER) in videos requires model personalization to capture the considerable variations across subjects. Vision-… (see more)language models (VLMs) offer strong transfer to downstream tasks through image-text alignment, but their performance can still degrade under inter-subject distribution shifts. Personalizing models using test-time adaptation (TTA) methods can mitigate this challenge. However, most state-of-the-art TTA methods rely on unsupervised parameter optimization, introducing computational overhead that is impractical in many real-world applications. This paper introduces TTA through Cache Personalization (TTA-CaP), a cache-based TTA method that enables cost-effective (gradient-free) personalization of VLMs for video FER. Prior cache-based TTA methods rely solely on dynamic memories that store test samples, which can accumulate errors and drift due to noisy pseudo-labels. TTA-CaP leverages three coordinated caches: a personalized source cache that stores source-domain prototypes, a positive target cache that accumulates reliable subject-specific samples, and a negative target cache that stores low-confidence cases as negative samples to reduce the impact of noisy pseudo-labels. Cache updates and replacement are controlled by a tri-gate mechanism based on temporal stability, confidence, and consistency with the personalized cache. Finally, TTA-CaP refines predictions through fusion of embeddings, yielding refined representations that support temporally stable video-level predictions. Our experiments on three challenging video FER datasets, BioVid, StressID, and BAH, indicate that TTA-CaP can outperform state-of-the-art TTA methods under subject-specific and environmental shifts, while maintaining low computational and memory overhead for real-world deployment.
2026-03-21
arXiv (preprint)
doi.org
arxiv.org
Ca2+ transient detection and segmentation with the Astronomically motivated algorithm for Background Estimation And Transient Segmentation (Astro-BEATS)
Bi Fan
Anthony Bilodeau
Frédéric Beaupré
Theresa Wiesner
Christian Gagné
Flavie Lavoie-Cardinal
Renée Hložek
Fluorescence-based Ca…
2026-03-18
arXiv (preprint)
doi.org