Publications

CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
Joachim Schaeffer
Thomas Jiralerspong
Alexander Panfilov
Guillaume Lajoie
Jonas Geiping
Yoshua Bengio
Roland S. Zimmermann
AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted mode… (voir plus)l. This partially tampers with the untrusted model's trajectory. If the trusted model detects such an intervention, it may infer properties of the monitor and adapt to evade control. We introduce \textbf{CIAware-Bench}, a benchmark for measuring \textbf{c}ontrol \textbf{i}ntervention (CI) awareness across frontier models. CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention. The benchmark is comprised of a suite of four task domains (essay writing, BigCodeBench, Bash Arena, and SHADE-Arena), while varying trajectory watermarking, side-task presence, and the control protocol. Evaluating eleven frontier models, we find low to moderate CI awareness under default settings (up to 0.87; random chance balanced binary classification accuracy is 0.5) with substantial variation across task domains and model pairs. Detection is generally easier across model families, suggesting that models exploit provider-specific differences in style or post-training. Overall, CI awareness is not a fixed model-level property, and should be measured for each new model release and deployment scenario. We release CIAware-Bench to track CI awareness and inform control protocols whose interventions are harder to detect.
2026-06-08
arXiv (prépublication)
doi.org
arxiv.org
Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses
Derek Newman
Charlotte Maschke
Jordan O‘Byrne
Michele Colombo
Angela Comanducci
Silvia Casarotto
Giuseppe Citerio
M Rosanova
Marcello Massimini
Karim Jerbi
Stefanie Blain‐Moraes
Abstract Identifying which severely brain-injured patients retain the capacity for consciousness remains a major challenge in neurocritical … (voir plus)care. The perturbational complexity index (PCI) provides a reliable assessment of consciousness capacity, but its reliance on transcranial magnetic stimulation and EEG (TMS-EEG) limits bedside scalability. PCI and brain criticality capture complementary dimensions of brain dynamics: PCI quantifies the complexity of the brain’s evoked response to perturbation, whereas criticality characterizes the intrinsic organization of spontaneous activity. Here, we tested whether resting-state EEG signatures of criticality predict PCI max in disorders of consciousness, extending prior findings from anesthesia to severe brain injury. In 26 patients with vascular, traumatic, or anoxic brain injury, multivariate criticality related features did not generalize PCI max prediction across the full heterogeneous cohort. However, criticality features predicted PCI max when analyses were restricted to non-anoxic patients and when restricting analyses to patients with non-zero PCI max values. These findings suggest that spontaneous criticality measures index the brain’s intrinsic dynamical regime that supports complex perturbational responses, while their correspondence with PCI max depends on whether the injured brain retains sufficient capacity to sustain large-scale evoked responses. Together, our results extend the relationship between resting-state criticality and evoked perturbational complexity to disorders of consciousness and support the development of stratified EEG measures in severe brain injury.
2026-06-08
bioRxiv (accepté)
doi.org
Human learning of noninvasive brain–computer interfaces via manifold geometry
Erica L. Busch
E. Chandra Fincke
Guillaume Lajoie
Smita Krishnaswamy
Nicholas B. Turk‐Browne
2026-06-08
Nature Neuroscience (publié)
doi.org
Rank Collapse, Fixed Points, and the Renormalization Group Structure of MLP Residual Networks
Parviz Haggi-Mani
Irina Rish
The analogy between deep neural network forward passes and renormalization group (RG) flows has been repeatedly noted in the literature, but… (voir plus) existing treatments remain qualitative: depth is described as a coarse-graining scale, attention is likened to a partition function, and representations are said to flow toward fixed points. No existing work has defined a measurable RG order parameter, tested it under controlled variation of the input distribution, or made quantitative predictions that are empirically verified. We study the simplest architecture for which the analogy is tractable: a pure MLP residual stack trained on masked token prediction over synthetic Markov chain sequences with known spectral properties. We report three findings. (i) The effective rank of the residual stream decreases monotonically with depth after training, consistent with progressive integration of irrelevant degrees of freedom. (ii) This rank collapse is selective: it occurs for chains with short correlation length approximately 1 but is absent for chains with long correlation length approximately 7, measured at the position level to control for mean-pooling artifacts. The network preserves exactly the degrees of freedom relevant to the prediction task, the content of the RG relevance criterion. (iii) Inter-layer kernel drift is concentrated at one or two specific transitions, with the remainder of the network near a fixed point, consistent with a discrete fixed-point plateau. Together these findings constitute the first quantitative, position-level evidence that MLP residual networks implement a selective coarse-graining procedure governed by the spectral structure of the input distribution.
2026-06-08
arXiv (prépublication)
doi.org
arxiv.org
Unifying Local Communications and Local Updates for LLM Pretraining
Pietro Cagnasso
Eugene Belilovsky
Edouard Oyallon
Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data center… (voir plus)s, and lower-bandwidth links. Many practical methods reduce communication frequency but still rely on synchronous All-Reduce operations that maintain identical model states and tie progress to global collectives. This can become a bottleneck when bandwidth or worker speed is heterogeneous. We introduce GASLoC, a novel decentralized pre-training algorithm that generalizes the notion of communication acceleration to the recently popular"outer optimizer"to allow a practical gossip-based training framework that is compatible with adaptive optimizers, allows for local optimizer steps, and can utilize sparse randomized peer communication. Empirically, on a number of standard LLM training tasks, we demonstrate that GASLoC outperforms state-of-the-art decentralized algorithms in single step per communication setting for a number of topologies and, unlike existing decentralized methods in the LLM setting, it allows to obtain performance competitive with DiLoCo when utilizing multiple local steps. In the heterogeneous bandwidth setting we demonstrate the advantage of GASLoC showing that it can significantly outperform DiLoCo.
2026-06-08
arXiv (prépublication)
doi.org
arxiv.org
Charting Cervical Spinal Cord Morphometry Across the Lifespan
Kurt Schilling
Michael E Kim
Matthew Amandola
Chenyu Gao
Karthik Ramadass
Praitayini Kanakaraj
Sam Bogdanov
G Rudravaram
Nancy R. Newlin
Derek B. Archer
Timothy J Hohman
Angela L Jefferson
Victoria L Morgan
Alexandra Roche
Dario J Englot
Murat Bilgel
Lori L Beason-Held
Luigi Ferrucci
Laurie Cutting
Laura A Barquero … (voir 21 de plus)
Micah D’Archangel
Tin Q Nguyen
Kathryn L Humphreys
Yanbin Niu
Sophia Vinci-Booher
Carissa J. Cascio
Zhiyuan Li
Daniel Moyer
Simon Vandekar
Panpan Zhang
Samuelle St-Onge
Sandrine Bédard
Jan Valosek
Benjamin De Leener
Julien Cohen-Adad
John C Gore
Seth Smith
B A Landman
John C. Gore
Seth Smith
Bennett A. Landman
Abstract Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter-… (voir plus)subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. We leveraged 30 population-based brain MRI datasets, aggregating 78,269 scans from 41,042 individuals (ages 0–100) whose imaging protocols included cervical cord coverage. To overcome contrast variability, we employed a state-of-the-art contrast-agnostic deep learning segmentation method, extracting cross-sectional area (CSA), anteroposterior (AP) and right–left (RL/transverse), and shape indices (compression ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non-linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early-to-mid adulthood (e.g., mid-30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age- and sex-specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.
2026-06-07
bioRxiv (accepté)
doi.org
Difference-Aware Retrieval Policies for Imitation Learning
Quinn Pfeifer
Ethan Pronovost
Paarth Shah
Khimya Khetarpal
Siddhartha Srinivasa
Abhishek Gupta
Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding erro… (voir plus)rs during deployment. We show that reusing the training data during inference via a semi-parametric retrieval-based imitation learning approach can alleviate this challenge. We present Difference-Aware Retrieval Policies for Imitation Learning (DARP), a semi-parametric retrieval-based imitation learning approach that addresses this limitation by reparameterizing the imitation learning problem in terms of local neighborhood structure rather than direct state-to-action mappings. Instead of learning a global policy, DARP trains a model to predict actions based on
2026-06-07
arXiv (prépublication)
doi.org
arxiv.org
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 … (voir plus)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 (prépublication)
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… (voir plus)meter
2026-06-07
arXiv (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
doi.org
arxiv.org