Publications

A systematic review of hyperscanning in clinical encounters
Lena Adel
Lisane Moses
Elisabeth Irvine
Kyle T Greenway
Guillaume Dumas
Michael Lifshitz
2025-05-31
Neuroscience and Biobehavioral Reviews (published)
doi.org
ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings
Tibor Kubík
Franccois Guibault
Michal vSpanvel
Hervé Lombaert
We introduce ToothForge, a spectral approach for automatically generating novel 3D teeth, effectively addressing the sparsity of dental shap… (see more)e datasets. By operating in the spectral domain, our method enables compact machine learning modeling, allowing the generation of high-resolution tooth meshes in milliseconds. However, generating shape spectra comes with the instability of the decomposed harmonics. To address this, we propose modeling the latent manifold on synchronized frequential embeddings. Spectra of all data samples are aligned to a common basis prior to the training procedure, effectively eliminating biases introduced by the decomposition instability. Furthermore, synchronized modeling removes the limiting factor imposed by previous methods, which require all shapes to share a common fixed connectivity. Using a private dataset of real dental crowns, we observe a greater reconstruction quality of the synthetized shapes, exceeding those of models trained on unaligned embeddings. We also explore additional applications of spectral analysis in digital dentistry, such as shape compression and interpolation. ToothForge facilitates a range of approaches at the intersection of spectral analysis and machine learning, with fewer restrictions on mesh structure. This makes it applicable for shape analysis not only in dentistry, but also in broader medical applications, where guaranteeing consistent connectivity across shapes from various clinics is unrealistic. The code is available at https://github.com/tiborkubik/toothForge.
2025-05-31
arXiv (published)
doi.org
arxiv.org
Unpacking Softmax: How Temperature Drives Representation Collapse, Compression, and Generalization
Wojciech Masarczyk
Mateusz Ostaszewski
Tin Sum Cheng
Tomasz Trzci'nski
Aurélien Lucchi
Razvan Pascanu
The softmax function is a fundamental building block of deep neural networks, commonly used to define output distributions in classification… (see more) tasks or attention weights in transformer architectures. Despite its widespread use and proven effectiveness, its influence on learning dynamics and learned representations remains poorly understood, limiting our ability to optimize model behavior. In this paper, we study the pivotal role of the softmax function in shaping the model's representation. We introduce the concept of rank deficit bias - a phenomenon in which softmax-based deep networks find solutions of rank much lower than the number of classes. This bias depends on the softmax function's logits norm, which is implicitly influenced by hyperparameters or directly modified by softmax temperature. Furthermore, we demonstrate how to exploit the softmax dynamics to learn compressed representations or to enhance their performance on out-of-distribution data. We validate our findings across diverse architectures and real-world datasets, highlighting the broad applicability of temperature tuning in improving model performance. Our work provides new insights into the mechanisms of softmax, enabling better control over representation learning in deep neural networks.
2025-05-31
arXiv (published)
doi.org
arxiv.org
Visual symbolic mechanisms: Emergent symbol processing in vision language models
Rim Assouel
Declan Campbell
Taylor Webb
To accurately process a visual scene, observers must bind features together to represent individual objects. This capacity is necessary, for… (see more) instance, to distinguish an image containing a red square and a blue circle from an image containing a blue square and a red circle. Recent work has found that language models solve this'binding problem'via a set of symbol-like, content-independent indices, but it is unclear whether similar mechanisms are employed by vision language models (VLMs). This question is especially relevant, given the persistent failures of VLMs on tasks that require binding. Here, we identify a set of emergent symbolic mechanisms that support binding in VLMs via a content-independent, spatial indexing scheme. Moreover, we find that binding errors can be traced directly to failures in these mechanisms. Taken together, these results shed light on the mechanisms that support symbol-like processing in VLMs, and suggest possible avenues for addressing the persistent binding failures exhibited by these models.
2025-05-31
arXiv (published)
doi.org
arxiv.org
Weak Supervision for Real World Graphs
Pratheeksha Nair
Reihaneh Rabbany
2025-05-31
arXiv (published)
doi.org
arxiv.org
Graph Representation Learning for the Prediction of Medication Usage in the UK Biobank Based on Pharmacogenetic Variants
Bill Qi
Yannis Trakadis
2025-05-30
Bioengineering (published)
doi.org
Manifold Learning for Olfactory Habituation to Strongly Fluctuating Backgrounds
François X. P. Bourassa
Paul François
Gautam Reddy
Massimo Vergassola
Animals rely on their sense of smell to survive, but important olfactory cues are mixed with confounding background odors that fluctuate due… (see more) to atmospheric turbulence. It is unclear how the olfactory system habituates to such stochastic backgrounds to detect behaviorally important odors. Here, we explicitly consider the high-dimensional nature of odor coding, the natural statistics of odor fluctuations, and the architecture of the early olfactory pathway. We show that their combination favors a manifold learning mechanism for olfactory habituation over alternatives based on predictive filtering. Manifold learning is implemented in our model by a biologically plausible network of inhibitory interneurons in the early olfactory pathway. We demonstrate that plasticity rules based on the Intrator, Bienenstock, Cooper, and Munro (IBCM) model or an online principal components analysis algorithm are effective at implementing this mechanism in turbulent conditions and outperform previous models relying on mean background subtraction. Interneurons with an IBCM plasticity rule acquire selectivity to independently varying odors. This manifold learning mechanism offers a path toward distinguishing plasticity rules in experiments and could be leveraged by other biological circuits facing fluctuating environments.
2025-05-29
bioRxiv (published)
doi.org
Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Ronan Legin
Maximiliano Isi
Kaze W. K. Wong
Yashar Hezaveh
Laurence Perreault-Levasseur
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from t… (see more)his idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to ``clean'' non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations; alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade.
2025-05-28
The Astrophysical Journal Letters (published)
doi.org
arxiv.org
Nuclear Patterning of Developing Cells in Murine Ventricular Heart Walls
Tabish A Syed
Sophie Ellwood
Drisya Dileep
S. Subha
Minhajuddin Sirajuddin
Kaleem Siddiqi
2025-05-28
Lecture Notes in Computer Science (published)
doi.org
Calibrated Value-Aware Model Learning with Probabilistic Environment Models
Claas Voelcker
Anastasiia Pedan
Arash Ahmadian
Romina Abachi
Igor Gilitschenski
Amir-massoud Farahmand
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcem… (see more)ent learning. The MuZero loss, which penalizes a model's value function prediction compared to the ground-truth value function, has been utilized in several prominent empirical works in the literature. However, theoretical investigation into its strengths and weaknesses is limited. In this paper, we analyze the family of value-aware model learning losses, which includes the popular MuZero loss. We show that these losses, as normally used, are uncalibrated surrogate losses, which means that they do not always recover the correct model and value function. Building on this insight, we propose corrections to solve this issue. Furthermore, we investigate the interplay between the loss calibration, latent model architectures, and auxiliary losses that are commonly employed when training MuZero-style agents. We show that while deterministic models can be sufficient to predict accurate values, learning calibrated stochastic models is still advantageous.
2025-05-27
ArXiv (preprint)
doi.org
proceedings.mlr.press
Jailbreak Distillation: Renewable Safety Benchmarking
Jingyu Zhang
Ahmed Elgohary
Xiawei Wang
A S M Iftekhar
Ahmed Magooda
Benjamin Van Durme
Daniel Khashabi
Kyle Jackson
JBDistill Benchmark JBDistill Benchmark
Marah Ihab Abdin
Jyoti Aneja
Harkirat Singh Behl
Sébastien Bubeck
Ronen Eldan
S. Gunasekar
Michael Harrison
Russell J. Hewett
Mojan Javaheripi
Piero Kauffmann
James R. Lee … (see 484 more)
Yin Tat Lee
Yuanzhi Li
Weishung Liu
C. C. T. Mendes
Anh Nguyen
Eric Price
Gustavo de Rosa
Olli Saarikivi
Adil Salim
Tim Beyer
Sophie Xhonneux
Simon Geisler
Gauthier Gidel
Leo Schwinn
Stephan Günnemann. 2025
Blake Bullwinkel
Amanda Minnich
Shiven Chawla
Gary Lopez
Martin Pouliot
Whitney Maxwell
Patrick Chao
Edoardo Debenedetti
Alexander Robey
Maksym Andriushchenko
Francesco Croce
Vikash Sehwag
Edgar Dobriban
Nicolas Flammarion
George J. Pappas
Florian Tramèr
Hamed Hassani
Eric Wong
Jailbreakbench
Zora Che
Stephen Casper
Robert Kirk
Anirudh Satheesh
Stewart Slocum
Lev E McKinney
Rohit Gandikota
Aidan Ewart
Domenic Rosati
Zichu Wu
Zikui Cai
Daya Guo
Dejian Yang
Haowei Zhang
Jun-Mei Song
Ruoyu Zhang
Runxin Xu
Qihao Zhu
Shirong Ma
Peiyi Wang
Xiaoling Bi
Xiaokang Zhang
Xingkai Yu
Yu Wu
Z. F. Wu
Zhibin Gou
Zhihong Shao
Zhuoshu Li
Ziyi Gao
A. Liu
Bing Xue
Bingxuan Wang
Bo WU
Bei Feng
Chenggang Lu
Chenggang Zhao
Chengqi Deng
Chenyu Zhang
C. Ruan
Damai Dai
Deli Chen
Dong-Li Ji
Erhang Li
Fangyun Lin
Fucong Dai
Fuli Luo
Guangbo Hao
Guanting Chen
Guowei Li
Han Bao
Hanwei Xu
Haocheng Wang
Honghui Ding
Huajian Xin
Huazuo Gao
Hui Qu
Hui Li
Jianzhong Guo
Jiashi Li
Jiawei Wang
Jingchang Chen
Jingyang Yuan
Junjie Qiu
Junlong Li
Jinbo Cai
Jia Ni
Jian Liang
Jin Chen
Kai Dong
Kai Hu
Kaige Gao
Kang Guan
Kexin Huang
Kuai Yu
Lean Wang
Lecong Zhang
Liang Zhao
Litong Wang
Liyue Zhang
Lei Xu
Leyi Xia
Mingchuan Zhang
Minghua Zhang
Min Tang
Meng Li
Miaojun Wang
Mingming Li
Ning Tian
Panpan Huang
Meng Wang
Qiancheng Wang
Qinyu Chen
Qiushi Du
Ruiqi Ge
Ruisong Zhang
Ruizhe Pan
Runji Wang
R. J. Chen
Rong Jin
Ruyi Chen
Shanghao Lu
Shangyan Zhou
Shanhuang Chen
Shengfeng Ye
Shiyu Wang
Shuiping Yu
Shunfeng Zhou
Shuting Pan
S. S. Li
Shuang Zhou
Shao-Ping Wu
Tao Yun
Tian Pei
Tianyu Sun
T. Wang
Wangding Zeng
Wanjia Zhao
Wen Liu
Wenfeng Liang
Wenjun Gao
Wen-Xuan Yu
Wentao Zhang
Wei Xiao
Wei An
Xiaodong Liu
Xiaohan Wang
Xiaokang Chen
Xiaotao Nie
Xin Cheng
Jian Li
Xinfeng Xie
Xingchao Liu
Xinyu Yang
Xinyuan Li
Xuecheng Su
Xuheng Lin
Xiangyu Jin
Xi-Cheng Shen
Xiaosha Chen
Xiaowen Sun
Xiaoxi-ang Wang
Xinnan Song
Xinyi Zhou
Xianzu Wang
Xinxia Shan
Y. K. Li
Y. Q. Wang
Y. X. Wei
Yang Zhang
Yan-Hong Xu
Yao Zhao
Yaofeng Sun
Yaohui Wang
Yi Yu
Yichao Zhang
Yifan Shi
Yi Xiong
Ying He
Yishi Piao
Yisong Wang
Yi Chern Tan
Yiyang Ma
Yiyuan Liu
Yongqiang Guo
Yuan Ou
Yuduan Wang
Yue Gong
Yuheng Zou
Yuzi He
Yunfan Xiong
Yuxiang Luo
Yuxiang You
Yu-mei You
Yuxuan Liu
Yuyang Zhou
Y. X. Zhu
Yanping Huang
Yaohui Li
Yang Li
Yi Zheng
Yunxiang Ma
Ying Tang
Yukun Zha
Yuting Yan
Z. Z. Ren
Zehui Ren
Zhangli Sha
Zhe Fu
Zhean Xu
Zhenda Xie
Zhengyan Zhang
Zhewen Hao
Zhicheng Ma
Zhigang Yan
Zhiyu Wu
Zihui Gu
Zijia Zhu
Zijun Liu
Zi-An Li
Ziwei Xie
Ziyang Song
Deep Ganguli
Liane Lovitt
Jackson Kernion
Amanda Askell
Yuntao Bai
Saurav Kadavath
Benjamin Mann
Ethan Perez
Nicholas Schiefer
Kamal Ndousse
Andy Jones
Sam Bowman
Anna Chen
Tom Con-erly
Nova Dassarma
Dawn Drain
Nelson Elhage Sheer
Stanislav Fort
Zac Hatfield-Dodds
T. Henighan
Danny Hernandez
Tristan Hume
Josh Jacobson
Scott Johnston
Shauna Kravec
Catherine Olsson
Sam Ringer
Eli Tran-Johnson
Dario Amodei
Tom Brown
Nicholas Joseph
Sam McCandlish
Chris Olah
Jared Kaplan
Jack Clark. 2022. Red
Aaron Grattafiori
Abhimanyu Dubey
Abhinav Jauhri
Abhinav Pandey
Abhishek Kadian
Ahmad Al-Dahle
Aiesha Letman
Akhil Mathur
Alan Schel-ten
Alex Vaughan
Amy Yang
Angela Fan
Anirudh Goyal
A. Hartshorn
Aobo Yang
Archi Mitra
Archie Sravankumar
Artem Korenev
Arthur Hinsvark
Arun Rao
Aston Zhang
Aurelien Ro-driguez
Austen Gregerson
Ava Spataru
Baptiste Rozière
Bethany Biron
Binh Tang
Bobbie Chern
Charlotte Caucheteux
Chaya Nayak
Chloe Bi
Chris Marra
Chris McConnell
Christian Keller
Christophe Touret
Chunyang Wu
Corinne Wong
Cris-tian Cantón Ferrer
Cyrus Nikolaidis
Damien Al-lonsius
Daniel Song
Danielle Pintz
Danny Livshits
Danny Wyatt
David Esiobu
Dhruv Choudhary
Dhruv Mahajan 0001
Diego Garcia-Olano
Diego Perino
Dieuwke Hupkes
Egor Lakomkin
Ehab A. AlBadawy
Elina Lobanova
Emily Dinan
Eric Michael Smith
Filip Radenovic
Francisco Guzmán
Frank Zhang
Gabriele Synnaeve
Gabrielle Lee
Georgia Lewis
G. Thattai
Graeme Nail
Gregoire Mi-alon
Guan Pang
Guillem Cucurell
Hailey Nguyen
Han-nah Korevaar
Hu Xu
Hugo Touvron
Imanol Iliyan Zarov
Arrieta Ibarra
Is-abel Kloumann
Ishan Misra
Ivan Evtimov
Jack Zhang
Jade Copet
Jaewon Lee
Jan Geffert
Jana Vranes
Jason Park
Jay Mahadeokar
Jeet Shah
Jelmer van der Linde
Jennifer Billock
Jenny Hong
Jenya Lee
Jeremy Fu
J. Fu
Jianfeng Chi
Jianyu Huang
Jiawen Liu
Jie Wang
Jiecao Yu
Joanna Bitton
Joe Spisak
Jongsoo Park
Joseph Rocca
J. Johnstun
Joshua Saxe
Junteng Jia
Kalyan Vasuden Alwala
Karthik Prasad
Kartikeya Upasani
Kate Plawiak
Keqian Li
Kenneth Heafield
Kevin R. Stone
Khalid El-Arini
Krithika Iyer
Kshitiz Malik
Kuen-ley Chiu
Kunal Bhalla
Kushal Lakhotia
Lauren Rantala-Yeary
Laurens van der Maaten
Lawrence Chen
Liang Tan
Liz Jenkins
Louis Martin
Lovish Madaan
Lubo Malo
Lukas Blecher
Lukas Landzaat
Luke de Oliveira
Madeline Muzzi
Mahesh Pasupuleti
Mannat Singh
Manohar Paluri
Marcin Kardas
Maria Tsimpoukelli
Mathew Oldham
Mathieu Rita
Maya Pavlova
Melanie Kam-badur
Mike Lewis
Mitesh Min Si
Kumar Singh
Mona Hassan
Naman Goyal
Narjes Torabi
Niko-lay Bashlykov
Nikolay Bogoychev
Niladri S. Chatterji
Ning Zhang
Olivier Duchenne
Onur Çelebi
Patrick Alrassy
Petar Pengwei Li
Peter Weng
Prajjwal Bhargava
Pratik Dubal
Punit Praveen Krishnan
Singh Koura
Puxin Xu
Qing He
Qingxiao Dong
Ragavan Srinivasan
Raj Ganapathy
Ramon Calderer
Ricardo Silveira Cabral
Robert Stojnic
Roberta Raileanu
Rohan Maheswari
Rohit Girdhar
Rohit Patel
Ro-main Sauvestre
Ron-nie Polidoro
Roshan Sumbaly
Ross Taylor
Ruan Silva
Rui Hou
Rui Wang
S. Hosseini
Sa-hana Chennabasappa
Sanjay Singh
Sean Bell
Seo-hyun Sonia Kim
Sergey Edunov
Shaoliang Nie
Sharan Narang
Sharath Chandra Raparthy
Sheng Shen
Shengye Wan
Shruti Bhosale
Shun Zhang
Simon Van-denhende
Soumya Batra
Spencer Whitman
Sten Sootla
Stephane Collot
Suchin Gururangan
S. Borodinsky
Tamar Herman
Tara Fowler
Tarek Sheasha
Thomas Georgiou
Thomas Scialom
Tobias Speckbacher
Todor Mihaylov
Tong Xiao
Ujjwal Karn
Vedanuj Goswami
Vibhor Gupta
Vignesh Ramanathan
Viktor Kerkez
Vincent Gonguet
Vir-ginie Do
Vish Vogeti
Vitor Albiero
Vladan Petro-vic
Weiwei Chu
Wenhan Xiong
Wenyin Fu
2025-05-27
ArXiv (preprint)
doi.org
arxiv.org
One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
Jinbang Huang
Yixin Xiao
Zhanguang Zhang
Mark J. Coates
Jianye HAO
Yingxue Zhang
Pre-trained Large Language Models (LLMs) have shown promise in solving planning problems but often struggle to ensure plan correctness, espe… (see more)cially for long-horizon tasks. Meanwhile, traditional robotic task and motion planning (TAMP) frameworks address these challenges more reliably by combining high-level symbolic search with low-level motion planning. However, TAMP relies on the availability of planning domains that typically involve substantial manual effort and domain expertise, limiting its generalizability. We introduce Planning Domain Derivation with LLMs (PDDLLM), a novel approach that combines simulated physical interaction with LLM reasoning to improve planning performance. The method reduces reliance on humans by inferring planning domains from a single annotated task-execution demonstration. Unlike prior domain-inference methods that rely on partially predefined or language descriptions of planning domains, PDDLLM constructs domains entirely from scratch and automatically integrates them with low-level motion planning skills, enabling fully automated long-horizon planning. PDDLLM is evaluated on over 1,200 diverse tasks spanning nine environments and benchmarked against six LLM-based planning baselines, demonstrating superior planning performance, lower token costs, and successful deployment on multiple robot platforms.
2025-05-27
thecvf.com/CVPR/2025/Workshop/FMEA (oral)
openreview.net
openreview.net