Publications

Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA
Rishabh Maheshwary
Masoud Hashemi
Khyati Mahajan
Shiva Krishna Reddy Malay
Sai Rajeswar Mudumba
Sathwik Tejaswi Madhusudhan
Spandana Gella
Vikas Yadav
2026-05-10
Language Resources and Evaluation Conference (publié)
doi.org
Exploring Token-Space Manipulation in Latent Audio Tokenizers
Francesco Paissan
Luca Della Libera
Mirco Ravanelli
Cem Subakan
Neural audio codecs provide compact discrete representations for speech generation and manipulation. However, most codecs organize tokens as… (voir plus) frame-level sequences, making it difficult to study or intervene on global factors of variation. In this work, we propose the Latent Audio Tokenizer for Token-space Editing (LATTE) that appends a fixed set of learnable latent tokens to the audio feature sequence and retains only these tokens for quantization and decoding. This design produces a compact, non-temporally aligned bottleneck in which each token can aggregate global information across the full utterance. We show that the resulting tokenizer preserves competitive reconstruction quality in low-bitrate speech coding settings while enabling simple token-space interventions. In particular, we find that swapping selected latent token positions between utterances can modify global attributes, such as speaker identity and background noise, and we evaluate these interventions on voice conversion and denoising tasks. Our results suggest that compact latent audio tokenizers can support controllable audio manipulation without supervision in task-specific editing models.
2026-05-10
arXiv (prépublication)
doi.org
arxiv.org
FRASE: Frame-based Structured Representations for Generalizable SPARQL Query Generation
Papa Abdou Karim Karou Diallo
Amal Zouaq
2026-05-10
Language Resources and Evaluation Conference (publié)
doi.org
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
Haechan Mark Bong
Giovanni Beltrame
Safe autonomous Uncrewed Aerial Vehicle (UAV) navigation in urban environments requires real-time path planning that avoids obstacles. MaxCo… (voir plus)nvNet is a potential-field planner that leverages properties of Maxwell's equations to generate a path to the goal without local minima. We extend the 2D MaxConvNet magnetic field planner to 3D, using a convolutional autoencoder to predict obstacle-aware potential fields from LiDAR-derived 101^3 voxel grids. Evaluation across 100 randomized closed-loop trials in two distinct Cosys-AirSim urban environments, a dense night-time cityscape and a suburban district shows a 100% path planning success rate on both maps without retraining. In offline path planning, 3DMaxConvNet produces path lengths comparable to A* on unseen maps while reducing runtime from 0.155--0.17s to 0.087--0.089s, or about 1.7--1.95 times faster than A*. Against RRT*(3k), 3DMaxConvNet achieves similar path quality while reducing planning runtime from 17.2--17.5s to about 0.09s, which is roughly 193--201 times faster than RRT*(3k).
2026-05-10
arXiv (prépublication)
doi.org
arxiv.org
Is the representation of fear distributed across the whole brain?
Vincent Taschereau‐Dumouchel
Marjorie Côté
Darius Valevicius
Lisa‐Marie Davignon
Marie-France Marin
2026-05-10
Research Square (accepté)
doi.org
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Ahmed Mehdi Inane
Vincent Quirion
Gintare Karolina Dzugaite
Ioannis Mitliagkas
Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destro… (voir plus)ys model utility, particularly for large-scale deletion requests. While leveraging public data is a standard technique in differential privacy to relax this tension, its role in unlearning remains unexplored. We address this gap by introducing Asymmetric Langevin Unlearning (ALU), a framework that uses public data to mitigate privacy costs. We prove that public data injection suppresses the unlearning cost by a factor of
2026-05-10
arXiv (prépublication)
doi.org
arxiv.org
Phases of Muon: When Muon Eclipses SignSGD
Elliot Paquette
Noah Marshall
Lucas Benigni
Guangyuan Wang
Atish Agarwala
Courtney Paquette
Recently, Muon and related spectral optimizers have demonstrated strong empirical performance as scalable stochastic methods, often outperfo… (voir plus)rming Adam. Yet their behaviour remains poorly understood. We analyze stochastic spectral optimizers, including Muon, on a high-dimensional matrix-valued least squares problem. We derive explicit deterministic dynamics that provide a tractable framework for studying learning behaviour with a focus on (stochastic) SignSVD, which Muon approximates, and (stochastic) SignSGD, the latter serving as a proxy for Adam. Our analysis shows that for large batch size, SignSVD performs a square-root preconditioning with respect to the data covariance spectrum, while for small batch size smaller eigenmodes behave like SGD, slowing down convergence. We contrast with SignSGD which for generic covariance performs no preconditioning and has no transition, leading to different optimal learning rates and convergence characteristics. The two methods match up to a constant factor with isotropic data, but behave differently with anisotropic data. An analysis of a power law covariance model with data exponent
2026-05-09
arXiv (prépublication)
doi.org
arxiv.org
Bootstrap Sampling Improves Model Soup Performance via Increased Model Diversity for Pneumonia Classification
Sara Early
Omata I. Ehizokhale
Nils D. Forkert
Samira Ebrahimi Kahou
Model soups combine multiple trained neural network checkpoints through weight averaging, often outperforming individual models and achievin… (voir plus)g performance comparable to deep ensembles without increasing inference cost. However, their effectiveness depends critically on checkpoint diversity, and when models are trained on the same dataset, optimization trajectories may converge toward similar regions of parameter space, limiting this diversity. In this work, we investigate bootstrap resampling as a simple data-level mechanism for increasing checkpoint diversity. Using a binary pneumonia classification task and 644 radiographs from the National Institutes of Health (NIH) ChestXray14 dataset, we train pools of convolutional neural networks under varying bootstrap ratios and construct greedy model soups. While checkpoint models trained on the full dataset achieve the highest mean individual accuracy, they are highly similar and offer little complementary signal, limiting the effectiveness of greedy selection. Bootstrap sampling introduces variability in the training data, producing more diverse checkpoints that, although individually weaker, enable greedy soup construction to combine complementary representations and achieve superior overall performance. The strongest model soup, obtained with 70\% bootstrap sampling, achieves a test accuracy of 0.650, representing a 9.8 percentage point improvement over the mean individual checkpoint accuracy (0.551) under the same condition. While absolute performance is limited by the small cohort size and training-from-scratch setting, this result highlights the substantial gains achievable through diversity-driven weight averaging.
2026-05-08
Medical Imaging with Deep Learning (poster)
openreview.net
openreview.net
Challenges in Using LLM Agents to Validate Agent Governance
Ayana Hussain
Ulrich Aivodji
Héber Hwang Arcolezi
The increasing deployment of Large Language Models (LLMs) as autonomous agents has intensified the need for credible and trustworthy methods… (voir plus) to evaluate governance interventions. Motivated by recent research, this work considers the use of LLM and agent-based simulations to evaluate AI agent governance mechanisms before real-world deployment. While conceptually appealing, this approach introduces various challenges. We examine three such problems: (1) obtaining ground truth for validation, (2) determining whether observed behaviors represent actual agent operations or simulation artifacts, and (3) obtaining consent for data use, and addressing ethical concerns about computational surrogates replacing real users. We also outline considerations based on documented limitations, aiming to catalyze workshop discussion on trustworthy and reliable evaluation methods for agent governance.
2026-05-08
PoliSim @ ACM Conference on Human Factors in Computing Systems (publié)
openreview.net
openreview.net
Actor-Critic Algorithm for Dynamic Expectile and CVaR
Yudong Luo
Erick Delage
Optimizing dynamic risk with stochastic policies is challenging in both policy updates and value learning. The former typically requires tra… (voir plus)nsition perturbation, while the latter may rely on model-based approaches. To address these challenges, we propose a surrogate policy gradient without transition perturbation under softmax policy parameterization. We further develop model-free value learning methods for dynamic expectile and conditional value-at-risk by leveraging elicitability. Finally, inspired by Expected SARSA and Expected Policy Gradient, a model-free off-policy actor-critic algorithm is constructed. Empirical results in domains with verifiable risk-averse behavior show that our algorithm can learn risk-averse policy and consistently outperforms other existing methods.
2026-05-07
arXiv (prépublication)
doi.org
arxiv.org
A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
Vincent Guan
Lazar Atanackovic
Kirill Neklyudov
The population dynamics of molecules, cells, and organisms are governed by a number of unknown forces. In the last decade, population dynami… (voir plus)cs have predominantly been modeled with Wasserstein gradient flows. However, since gradient flows minimize free energy, they fail to capture important dynamical properties, such as periodicity. In this work, we propose a change in perspective by considering dynamics that minimize a population-level action under a damped Wasserstein Lagrangian. By deriving the corresponding Hamiltonian equations of motion, we formalize Wasserstein Lagrangian Mechanics, a structured class of second-order dynamics that encompasses classical mechanics, quantum mechanics, and gradient flows. We then propose WLM as the first algorithm that learns these second-order dynamics from observed marginals, without specifying the Lagrangian. By directly learning the population mechanics, WLM can both forecast and interpolate unseen marginals, and outperforms existing gradient flow and flow matching methods across a wide range of dynamics, including vortex dynamics, embryonic development, and flocking.
2026-05-07
arXiv (prépublication)
doi.org
arxiv.org
Estimation of head motion in structural MRI and its impact on cortical morphometry
Charles Bricout
Samira Ebrahimi Kahou
Sylvain Bouix
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical … (voir plus)thickness. These biases can interfere with statistical analysis which is a major concern as motion has been shown to be more prominent in certain populations such as children or individuals with ADHD. Manual review cannot objectively quantify motion in anatomical scans, and existing quantitative automated approaches often require specialized hardware or custom acquisition protocols. Here, we train a 3D convolutional neural network to estimate a summary motion metric in retrospective routine research scans by leveraging a large training dataset of synthetically motion-corrupted volumes. We validate our method with one held-out site from our training cohort and with 14 fully independent datasets, including one with manual ratings, achieving a Spearman Rank correlation of 0.71 vs. manual labels. We also tested the correlation of our predicted motion score with morphometric measurements known to be impacted by motion, achieving significant correlation on most datasets. Furthermore, our predicted motion correlates with subject age in line with prior studies. Our approach shows good generalization across scanner brands and protocols, enabling objective, scalable motion assessment in structural MRI studies without prospective motion correction. Finally, we provide empirical evidence that our motion estimator significantly improve model fitness when studying cortical thickness and volume. Our final model is made openly and freely available through “Agitation," a tool usable as a CLI, python package and integrated in Nipoppy and Boutiques. By providing reliable motion estimates, our method offers researchers a tool to assess and account for potential biases in cortical morphometric analyses.
2026-05-07
Frontiers in Neuroscience (publié)
doi.org