Publications

A "fine-cuts" approach disentangling psychopathic, autistic and alexithymic traits in their associations with affective, cognitive and motor empathy
Julia Ayache
Nikki Stevenson
Elisha Patel
Alexander Sumich
Guillaume Dumas
Nadja Heym
2025-10-31
Personality and Individual Differences (publié)
doi.org
fLSA: Learning Semantic Structures in Document Collections Using Foundation Models
Weijia Xu
Nebojsa Jojic
Nicolas Roux
Humans can learn to solve new tasks by inducing high-level strategies from example solutions to similar problems and then adapting these str… (voir plus)ategies to solve unseen problems. Can we use large language models to induce such high-level structure from example documents or solutions? We introduce fLSA, a foundation-model-based Latent Semantic Analysis method that iteratively clusters and tags document segments based on document-level contexts. These tags can be used to model the latent structure of given documents and for hierarchical sampling of new texts. Our experiments on story writing, math, and multi-step reasoning datasets demonstrate that fLSA tags are more informative in reconstructing the original texts than existing tagging methods. Moreover, when used for hierarchical sampling, fLSA tags help expand the output space in the right directions that lead to correct solutions more often than direct sampling and hierarchical sampling with existing tagging methods. Code: https://github.com/microsoft/fLSA
2025-10-31
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
Pierre-Yves Lajoie
Benjamin Ramtoula
Daniele De Martini
Giovanni Beltrame
Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) techniques often struggle to identify map overlaps due to signifi… (voir plus)cant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust loop closing approach that leverages these models to establish inter-robot measurements. In contrast to resource-intensive methods requiring full 3D reconstruction within a centralized map, our approach integrates foundation models into existing SLAM pipelines, yielding scalable and robust multi-robot mapping. Our contributions include: 1) integrating 3D foundation models to reliably estimate relative poses from monocular image pairs within decentralized C-SLAM; 2) introducing robust outlier mitigation techniques critical to the use of these relative poses and 3) developing specialized pose graph optimization formulations that efficiently resolve scale ambiguities. We evaluate our method against state-of-the-art approaches, demonstrating improvements in localization and mapping accuracy, alongside significant gains in computational and memory efficiency. These results highlight the potential of our approach for deployment in large-scale multi-robot scenarios.
2025-10-31
IEEE Robotics and Automation Letters (publié)
doi.org
arxiv.org
Intersectionality in Surgical Care in LMICs: A Systematic Scoping Review
Ayla Gerk
Shreenik Kundu
Elena Guadagno
Justina Seyi-Olajide
Dunya Moghul
Joaquim Bustorff-Silva
Cristina Camargo
Dan Poenaru
2025-10-31
American Journal of Surgery (publié)
doi.org
Multilingual Language Model Pretraining using Machine-translated Data
Jiayi Wang
Yao Lu
Maurice Weber
Max Ryabinin
David Ifeoluwa Adelani
Yihong Chen
Raphael Tang
Pontus Stenetorp
2025-10-31
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
Le Zhang
Bo Wang
Xipeng Qiu
Siva Reddy
Aishwarya Agrawal
We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, sign… (voir plus)ificantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular information retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results underscore the effectiveness of our approach and highlight how reinforcement learning can enhance LLM reasoning capabilities in reranking.
2025-10-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Reassessing Speech Translation for Low-Resource Languages: Do LLMs Redefine the State-of-the-Art Against Cascaded Models?
Jonah Dauvet
Min Ma
Jessica Ojo
David Ifeoluwa Adelani
2025-10-31
MRL @ International Conference on Learning Representations (publié)
doi.org
Simulate intelligently: Causal incremental reinforcement learning for streamlined industrial chemical process design optimization
Eslam G. Al-Sakkari
Ahmed Ragab
Mohamed Ali
Hanane Dagdougui
Daria C. Boffito
2025-10-31
Journal of Environmental Chemical Engineering (publié)
doi.org
The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities
Saeid Jamshidi
Negar Shahabi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Carol Fung
2025-10-31
Internet of Things (publié)
doi.org
Training of LLM-Based List-Wise Multilingual Reranker
Hao Yu
David Ifeoluwa Adelani
2025-10-31
Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025) (publié)
doi.org
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation
Rabiul Awal
Mahsa Massoud
Aarash Feizi
Zichao Li
Suyuchen Wang
Christopher Pal
Aishwarya Agrawal
David Vázquez
Siva Reddy
Juan A. Rodriguez
Perouz Taslakian
Spandana Gella
Sai Rajeswar
We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing inv… (voir plus)olving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models'abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.
2025-10-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
openreview.net
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrovi'c
Lazar Atanackovic
Viggo Moro
Kacper Kapu'sniak
.Ismail .Ilkan Ceylan
Michael M. Bronstein
Avishek Bose
Alexander Tong
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Suc… (voir plus)h models rely on key assumptions about the underlying process in order to enable faithful learning of governing dynamics that mimic the actual system behavior. The de facto assumption in current approaches relies on the principle of least action that results in gradient field dynamics and leads to trajectories minimizing an energy functional between two probability measures. However, many real-world systems, such as cell cycles in single-cell RNA, are known to exhibit non-gradient, periodic behavior, which fundamentally cannot be captured by current state-of-the-art methods such as flow and bridge matching. In this paper, we introduce Curly Flow Matching (Curly-FM), a novel approach that is capable of learning non-gradient field dynamics by designing and solving a Schr\"odinger bridge problem with a non-zero drift reference process -- in stark contrast to typical zero-drift reference processes -- which is constructed using inferred velocities in addition to population snapshot data. We showcase Curly-FM by solving the trajectory inference problems for single cells, computational fluid dynamics, and ocean currents with approximate velocities. We demonstrate that Curly-FM can learn trajectories that better match both the reference process and population marginals. Curly-FM expands flow matching models beyond the modeling of populations and towards the modeling of known periodic behavior in physical systems. Our code repository is accessible at: https://github.com/kpetrovicc/curly-flow-matching.git
2025-10-29
ArXiv (prépublication)
doi.org
arxiv.org