Publications

OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages
David Guzmán
Luel Hagos Beyene
Jesujoba Oluwadara Alabi
Yejin Jeon
Dietrich Klakow
David Ifeoluwa Adelani
Recent advances in neural text-to-speech (TTS) and multilingual speech generation have substantially improved synthetic speech quality, yet … (see more)these gains remain unevenly distributed across the world's languages. Existing models are still dominated by a small set of high-resource languages, while many studies of low-resource TTS are simulated on artificially downsampled high-resource corpora that do not reflect the orthographic variation and limited phonetic coverage encountered in genuinely underrepresented settings. As such, we introduce OpenBibleTTS, which is a large-scale benchmark for low-resource speech synthesis spanning 37 underrepresented languages. Moreover, a systematic comparison of various TTS architectures and large-scale speech generation models is conducted across in-domain Biblical text and out-of-domain material. Results show that no single system dominates across languages and metrics: Gemini-TTS achieves the highest listener ratings on most evaluated languages, but monolingual EveryVoice models trained on OpenBibleTTS remain strongest for intelligibility and are preferred in several African languages, while open from-scratch systems degrade sharply on out-of-domain text, revealing a persistent gap between broad multilingual coverage and reliable synthesis quality in underserved linguistic communities. We complement automatic evaluation with subjective human judgments, and open-source all processed datasets, alignments, and trained models to support future low-resource TTS research.
2026-06-07
arXiv (preprint)
doi.org
arxiv.org
Perturbative study of Supercritical Crossover in Noncommutative-corrected Spacetime
Ankit Anand
Shoucheng Wang
We analytically study the Widom line and supercritical crossover of noncommutative charged AdS black holes. Treating the noncommutative para… (see more)meter
2026-06-07
arXiv (preprint)
doi.org
arxiv.org
SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI
Nathan Molinier
Adrian A. Marth
Reto Sutter
Christoph Germann
Jacob A. Connolly
Mathieu Guay-Paquet
Nathan D. Schilaty
Kenneth A. Weber II
Julien Cohen-Adad
Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challengin… (see more)g. In clinical practice, analysis is predominantly performed in two dimensions (2D), as manual three-dimensional (3D) assessment is time-consuming. However, 2D measurements suffer from limited reproducibility, particularly when anatomical structures are not aligned with the imaging plane. Existing automated approaches are often restricted to 2D, rely on discrete grading, or lack robustness and interpretability. We introduce SpineReport, an open-source, fully automated framework for comprehensive 3D morphometric analysis of lumbar spine MRI. Leveraging robust anatomical segmentations, the method extracts quantitative metrics from key structures, including the spinal canal, spinal cord, vertebrae, intervertebral discs, and foramina. These include both morphological and signal-based features, enabling cross-subject and longitudinal assessment. SpineReport further generates subject-specific reports that allow comparison with cohort distributions, improving interpretability and objective characterization of spinal morphology. Clinical relevance was evaluated against radiologist-reported severity grades for central canal, lateral recess, and foraminal stenosis. Metrics showed strong associations with central canal stenosis severity, with T2-weighted CSF signal providing the highest performance (AUC = 0.95). Canal AP diameter and area ratios also demonstrated strong correlations and high discriminative ability (AUC>0.80). For lateral recess stenosis, associations were moderate, with lateral CSF signal being the most informative (AUC = 0.73). No significant associations were observed for foraminal stenosis despite robust region-of-interest extraction. SpineReport is released as an open-access tool: https://ivadomed.github.io/SpineReport/
2026-06-07
arXiv (preprint)
doi.org
arxiv.org
Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning
Alvaro Gonzalez
M. H. Hasan Shovo
Ali Ayub
Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noi… (see more)sy conditions. Existing approaches often rely on complex spatio-temporal models, which can be computationally expensive and sensitive to environmental variability. In this paper, we propose GLOBE, a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning. The framework performs sequential prediction efficiently while selectively invoking LLM reasoning only when the model confidence is low. To evaluate performance under realistic conditions, we introduce HOMER-Noise, a noisy extension of the HOMER+ dataset that simulates structured disturbances such as object movements caused by humans, pets, and toddlers. Experimental results show that GLOBE achieves competitive performance with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings. The framework is further validated through a proof-of-concept integration with a Stretch 3 mobile manipulator, demonstrating its potential application in real-world human-robot interaction scenarios.
2026-06-06
arXiv (preprint)
doi.org
arxiv.org
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Stéphane Eilles-Chan Way
Hugo Percot
Quentin Cappart
Tias Guns
Louis-Martin Rousseau
Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-speci… (see more)fied models. However, its practical deployment is often hindered by high computational costs and limited scalability, as it requires solving a constrained optimization problem for each training instance at every iteration. To address these challenges, we propose a novel framework that incorporates Lagrangian decomposition into the decision-focused learning paradigm. Specifically, we introduce a new surrogate objective along with two loss functions for evaluating and training the underlying prediction model. We further propose two variants of our approach, which offer different trade-offs between computational efficiency and solution quality. Our framework can be seamlessly integrated with standard decision-focused learning methods, including Smart Predict-then-Optimize (SPO+) and Implicit Maximum Likelihood Estimation (IMLE). Through experiments on two standard benchmarks, the multi-dimensional knapsack problem and quadratic portfolio optimization, we demonstrate that our approach achieves competitive performance while remaining amenable to parallelization. In particular, it consistently outperforms traditional decision-focused learning methods on large-scale instances, involving up to eight times more variables than those typically considered in related work. The implementation is available at https://github.com/corail-research/DFL-LD.
2026-06-06
arXiv (preprint)
doi.org
arxiv.org
Trajectory-Refined Distillation
Li Jiang
Haoran Xu
Yichuan Ding
Amy Zhang
On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher sup… (see more)ervision along the student's own rollouts. In this work, we identify a common structural cause underlying OPD, which we call prefix failure. Under prefix failure, dense per-token supervision induces a bimodal teacher mixture and fragmented gradients that token-level loss truncation or reweighting fail to address. This observation motivates us to move beyond token-level loss interventions toward trajectory-level output corrections. We thus propose Trajectory-Refined Distillation (TRD), a trajectory-level correction method that revises the student's rollout under the teacher guidance while within on-policy support. By correcting problematic prefixes before distillation, TRD mitigates prefix failure at its source. Moreover, TRD improves the exploration by exposing the student to alternative valid derivations under teacher guidance, even when the original rolls are already correct. TRD can also be applied to on-policy self-distillation (OPSD), a parameter-sharing variant that uses the student model conditioned on privileged informations as the teacher. Across a wide range of benchmarks and base models at multiple scales, TRD consistently outperforms prior baselines, improving single-attempt accuracy and broadening reasoning coverage. Code is available at https://github.com/louieworth/trd
2026-06-06
arXiv (preprint)
doi.org
arxiv.org
IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts
Ayana Hussain
Soumya Sharma
Golnoosh Farnadi
Nicholas Vincent
Héber Hwang Arcolezi
Ulrich Aivodji
Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a maj… (see more)or challenge for their design and evaluation. Prior work focuses mainly on individual-level risks, overlooking \textbf{interdependent privacy (IDP)}--where one person's data may be revealed by others without their knowledge or consent. We address this gap by introducing \textbf{IDP-Bench}: the first LLM benchmark for IDP scenarios, grounded in the Contextual Integrity (CI) framework. We evaluate eight open-source LLMs on their understanding of IDP scenarios across three levels of IDP reasoning using two LLM judges. Results show strong co-ownership recognition (6/8 models exceed 90\%) but persistent weaknesses in identifying CI parameters (information attribute, primary subject) and IDP-specific parameters such as secondary subjects, where 7/8 models score below 74\%. Models also struggle to judge sharing appropriateness (5/8 scoring below 77\%). While the ability to judge the appropriateness of sharing improves with scale, performance tends to decline in smaller models, and prompt sensitivity remains high on IDP-specific questions--highlighting the need for more targeted study of IDP in LLM privacy research. Data \& code available \href{https://github.com/tisl-lab/Interdependent_Privacy_Bench}{here}.
2026-06-05
arXiv (preprint)
doi.org
arxiv.org
Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
Katelyn Xiaoying Mei
Yi-Li Hsu
Minjoon Choi
Zongwan Cao
Chenjun Xu
Bingbing Wen
Su Lin Blodgett
Lucy Lu Wang
Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these eva… (see more)luations depend on transparent and well-documented protocols -- details that are frequently missing in current practice. In this work, we conduct a large-scale analysis of human evaluation protocols for evaluating long-form generation tasks in *CL conference publications from 2023--2025, including a full manual review of 284 papers and LLM-assisted analysis for another 1.8k+ papers. We define a set of 20 reportable criteria related to reproducibility of human evaluation studies, and apply these criteria to systematically examine reporting norms and practices within the community. We find widespread under-reporting of important aspects of human evaluation study design, leading to ambiguity about what was measured and how, who contributed judgments, and how judgments should be interpreted. Based on these findings, we outline actionable recommendations to support more transparent and reproducible reporting in future research. Our analysis code and annotated dataset can be found at: https://github.com/larchlab/Illusions-of-the-Gold-Standard
2026-06-05
arXiv (preprint)
doi.org
arxiv.org
Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology
Yujing Zou
Laya Rafiee Sevyeri
Farhood Farahnak
George Shenouda
Marie Duclos
Tomás Yokoo Teodoro de Souza
Khalil Sultanem
Parsa Bagherzadeh
Farhad Maleki
Shirin A. Enger
Privacy regulations and limited expert-validation constrain the deployment of large language models (LLMs) for electronic health record stru… (see more)cturing. We evaluated locally deployed LLMs to extract 30 prognostic variables from 1,360 head and neck cancer reports (882 patients) using zero-shot prompting. A stratified 50-case subset was reviewed by three radiation oncologists (50 cases, 30 fields, 3 reviewers; 4,500 decisions) to form a majority-vote reference for Llama3.3-70B, which achieved 98.6% F1 with high clinician agreement and processed reports in 53 s/report. Among seven additional models (2.6B-70B) benchmarked against this reference, GPT-OSS-20.9B (F1 89.4%) and MedGemma-27B (F1 88.5%) performed best. Integrating LLM-extracted HPV status, smoking history, and Charlson Comorbidity Score into a multivariate Cox Proportional Hazards model (age, sex, T/N stage) improved disease-free survival (likelihood ratio test p = 0.014; ΔC-index + 0.071) and locoregional failure-free survival (p = 0.026; ΔC-index + 0.108) with 1,000-bootstrap internal validation. This clinician-AI collaborative evaluation shows that on-premises LLMs enable privacy-preserving and efficient tumour board support, longitudinal data curation, and outcome prediction.
2026-06-05
npj Precision Oncology (published)
doi.org
Rules of the game: Legislative exits in four Westminster systems
Alex B. Rivard
Marc André Bodet
Jean-François Godbout
By leveraging over 150 years of electoral and biographical data in the Canadian provinces of Ontario, Quebec, New Brunswick, and Nova Scot… (see more)ia, we argue that voluntary exit is best understood as a cost-benefit calculation shaped by positional and institutional incentives in the legislative arena. We show that institutional changes that make seeking re-election costlier are associated with an increased likelihood of a legislator voluntarily exiting the legislative arena. We also find that the determinants of exit vary across age cohorts: younger legislators are more sensitive to institutional and positional cost-benefit incentives, reflecting greater professional mobility and outside career opportunities. Overall, our results indicate that positional and institutional in part explain a legislator’s decision to not seek re-election, but that their impact of these incentives is mediated by life-cycle and retirement-horizon considerations.
2026-06-05
International Political Science Review (published)
doi.org
Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs
Omar Mahmoud
Aly M. Kassem
Thommen George Karimpanal
Buddhika Laknath Semage
Negar Rostamzadeh
Golnoosh Farnadi
Santu Rana
Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to sp… (see more)ecific triggers or behaviors. We show this view is incomplete. Across diverse backdoor behaviors, we identify a shared latent mechanism that can be detected, causally controlled, and suppressed. Using sparse autoencoders (SAEs) on residual-stream activations, we find a small set of latent features consistently activated across jailbreaking, refusal manipulation, password-locking, bias induction, sentiment misclassification, and country-conditioned harmful advice. These features generalize across Qwen3, Gemma~3, and Llama~3.1 models from 4B to 32B parameters, and across both fine-tuning and weight-editing attacks. Through bidirectional activation steering, we show these features are causal: suppressing them reduces attack success, while amplifying them induces target behaviors on clean prompts. We further train lightweight SAE-feature classifiers that generalize zero-shot to unseen backdoors and outperform residual-stream and weight-diffing baselines. Finally, we introduce Concept Ablation Fine-Tuning (CAFT), which suppresses backdoor formation by ablating the shared latent subspace during training. Together, our results suggest that many backdoors rely on a transferable latent mechanism, enabling unified detection and mitigation.
2026-06-05
arXiv (preprint)
doi.org
arxiv.org
SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows
Amine El hattami
Nicolas Chapados
Christopher Pal
AI agents increasingly turn past experience into reusable artifacts such as code, workflows, and procedural memories. Reuse can improve effi… (see more)ciency, but it also creates a lifecycle reliability problem: artifacts that succeed once may fail under environment drift, underspecified tasks, or changing task distributions, especially in web automation. We introduce SKILL.nb, a framework for governing reusable agent workflows with evidence-calibrated lifecycle policies. SKILL.nb uses selective formalization: execution evidence decides which workflow steps should become executable code, which should remain natural-language guided, and when those choices should be revised. Workflows are stored as auditable, versioned notebooks that interleave natural-language guidance, multi-language executable cells, validation gates, fallback paths, and multimodal evidence such as outputs, screenshots, and error traces. At runtime, gate-conditioned execution lets each step run code when its gates validate, or fall back locally when drift invalidates the executable realization. On WebArena-Verified, SKILL.nb achieves 53.7% single-round success, improving over the strongest baseline by 3.9 percentage points. Across three re-executions, it retains 91.7% of initially successful tasks, 15.5 points above the next best method. Under bounded repair, it recovers 72.9% of subsequent failures while limiting post-repair regressions to 4.2%, compared with 15.0% to 17.0% for persistent baselines. It also leads on Mind2Web cross-website and cross-domain splits. In a GitLab migration test, SKILL.nb preserves performance when reusing frozen state learned on GitLab 15.7, with frozen-versus-fresh target-version gaps of -1.7 points on GitLab 16.11 and +0.6 points on GitLab 18.9. These results identify lifecycle governance and gate-conditioned execution as reliability axes beyond one-shot task success.
2026-06-05
arXiv (preprint)
doi.org
arxiv.org