Publications

Bidirectional modulation of pain by neurofeedback: Preliminary findings with fMRI at 7T
Konstantin A. Demin
Jun Seo Hwang
Wonyi Che
Dongho Kim
Wani Woo
Hakwan Lau
Vincent Taschereau‐Dumouchel
Abstract Previous brain decoding studies indicate that an individual’s pain experience can be robustly predicted from distributed patterns… (see more) of brain activity. Two brain decoders have notably been associated respectively with the nociceptive and cognitive aspects of pain experience, the Neurologic Pain Signature (NPS) and the Stimulus-Intensity Independent Pain Signature (SIIPS). Yet, we still do not know if these brain patterns are also causally related to pain experience. To evaluate this possibility, we used high-field (7-Tesla) fMRI to test whether humans can alter their pain experience by bidirectionally modulating their pain-related brain activity in decoded neurofeedback paradigm. In a double-blind design, participants were trained to up- and down-regulate the NPS or the SIIPS. Our results indicate that participants can achieve bidirectional control of both signatures. NPS expression reliably increased during pain stimulation and covaried with both stimulus intensity and subjective ratings. In contrast, SIIPS expression did not show consistent stimulus-locked effects in the primary analyses. Importantly, reduction in pain rating was specific for SIIPS-training, whereas NPS has failed to show any consistent behavioral effect. Based on these preliminary findings, we hereby preregister a follow-up study, with specified rationale, hypotheses, experimental design, and analysis protocols.
2026-05-12
bioRxiv (preprint)
doi.org
A Blueprint for Integrated Climate Intelligence
Carlos Rodriguez-Pardo
Guido Ascenso
Cindy Giselle Azuero Pedraza
Marie‐Lou Bachèlery
Manuela Balzarolo
Sara Beery
Vitus Benson
Laure Berti‐Équille
Annalisa Bracco
Momme Momme Butenschön
Gustau Camps-Valls
Andrea Castelletti
Melissa Chapman
Ioana Colfescu
Italo Epicoco
Chaneil James
Konstantin Klemmer
Raphaela Kotsch
Dr Vipin Kumar
Christian Lessig … (see 10 more)
Amirpasha Mozaffari
S. Karthik Mukkavilli
Arthur Ouaknine
Quentin Paletta
María Pérez‐Ortiz
Esther Rolf
Mary Sanford
Soheil Shayegh
Kasia Tokarksa
Massimo Tavoni
Climate information is advancing faster than the decision systems designed to use it. Emergency response operates on timescales of hours, wh… (see more)ereas societal adaptation unfolds over decades. Yet climate science, impact assessment and policy remain poorly integrated, limiting coherent action across timescales. We argue that artificial intelligence should be developed not only as a domain-specific tool, but as an integration layer linking fragmented physical, social and institutional systems. This Perspective outlines a blueprint for Integrated Climate Intelligence built on three pillars: fairness, which prioritizes vulnerability alongside data availability; speed, which reduces the latency between assessment and action; and robustness, which quantifies uncertainty under extrapolation and high-stakes decision-making. We further propose a Climate AI Trust Index as an evaluation framework for assessing whether climate AI systems are sufficiently rigorous, equitable and decision-relevant for operational use.
2026-05-12
EarthArXiv (preprint)
doi.org
CA2: Code-Aware Agent for Automated Game Testing
Valliappan Chidambaram Adaikkappan
Vincent Martineau
Joshua Romoff
David Meger
Automated game testing is important for verifying game functionality, but it remains a costly and time-consuming process. Manual testing oft… (see more)en misses edge cases, and current automated methods struggle to provide full code coverage. Prior work has explored reinforcement learning (RL) for game testing, but without leveraging internal code signals such as the call stack. We present Code Aware Agent (CA2), which uses call stack information to learn effective testing strategies. The agent receives the current function call trace along with the game state and learns to reach specific target functions. We instrument two types of environments, 1) State-based and 2) Image-based, with support for efficient call stack extraction. Through experimental evaluation, we find that CA2 achieves consistent improvement over the non-code aware baselines, which does not leverage call stack information. Our results show that incorporating code signals like the call stack enables more effective and targeted game testing.
2026-05-12
arXiv (preprint)
doi.org
arxiv.org
Reliability-Gated Source Anchoring for Continual Test-Time Adaptation
Vikash Singh
Debargha Ganguly
Weicong Chen
Sabyasachi Sahoo
Sreehari Sankar
Biyao Zhang
Mohsen Hariri
Shouren Wang
Osama Zafar
Christian Gagné
Vipin Chaudhary
Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a froze… (see more)n source checkpoint. This anchor is useful only when the source remains reliable. On CCC-Hard, however, a ResNet-50 source falls to approximately
2026-05-12
arXiv (preprint)
doi.org
arxiv.org
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
Paul Hoareau
Kuan Yi Wang
Brandon Bujak
Roy Sun
Govind Nair
Irene Cortese
Charidimos Tsagkas
Daniel Reich
Julien Cohen-Adad
INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, … (see more)forcing reliance on sparse 2D slices. Weakly supervised Sparse-to-Dense frameworks bridge this gap, but guidelines remain ambiguous regarding human-centric visual enhancements and transferring optimization strategies across dimensions. We analyze divergent regularization needs for multi-class segmentation of high-resolution ex vivo spinal cord MRI. METHODS | We used 9.4T MRI of multiple sclerosis spinal cords (>104,000 slices) with sparse annotations (428 slices). A 2D Teacher trained on sparse slices generated dense pseudo-labels to train a 3D Student. We systematically evaluated the impact of human-centric preprocessing, spatial augmentation, and soft-label regularization on both architectures. RESULTS | We identified a critical divergence in training dynamics. The 2D Teacher required strong spatial augmentation and soft-labeling to overcome data scarcity, improving White Matter Lesion Dice scores by>11 points. However, propagating these techniques to the 3D Student degraded its performance. Furthermore, human-centric preprocessing (e.g., CLAHE) disrupted global statistical cues, dropping Gray Matter Lesion Dice scores by ~25 points. DISCUSSION | Our study highlights a perception divergence (human-centric contrast enhancement harms machine models) and a regularization conflict across dimensions. 3D architectures trained on dense pseudo-labels exhibit fundamentally different optimization landscapes than 2D counterparts and require distinct, conservative regularization. Code and models: https://github.com/ivadomed/model_seg_sc-gm-lesion_human_ms_exvivo_t2star.
2026-05-11
arXiv (preprint)
doi.org
arxiv.org
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli
Sruthi Kuriakose
Maryam Hashemzadeh
Amal Zouaq
Sarath Chandar
Language models are increasingly used in settings where outputs must satisfy user-specified randomness constraints, yet their generation pro… (see more)babilities are often poorly calibrated to those targets. We study whether this capability can be improved directly through fine-tuning. Concretely, we fine-tune language models on synthetic prompts that require sampling from mathematical distributions, and compare two Calibration Fine-Tuning variants: a soft-target method that converts the desired output distribution into trie-derived next-token targets, and a hard-target method that trains on sampled completions from the same target distribution. Across 12 models spanning four families, both methods substantially improve structured-sampling fidelity on held-out distribution families and unseen parameter settings, showing that probabilistic calibration is a trainable capability. Under our selected training configurations, the two methods exhibit different empirical profiles: hard-target fine-tuning is often strongest on structured numeric sampling, while soft-target fine-tuning performs better on broader stochastic generation benchmarks, including open-ended random generation, multiple-choice answer-position balancing, and NoveltyBench. The gains sometimes reduce downstream capability, especially arithmetic reasoning, with costs varying by model. Overall, our results show that probabilistic calibration can be improved through fine-tuning, with our hard-target configuration favoring exact numeric fidelity and our soft-target configuration favoring broader stochastic transfer. Code is available at https://github.com/chandar-lab/calibration-finetuning.
2026-05-11
arXiv (preprint)
doi.org
arxiv.org
scShapeBench: Discovering geometry from high dimensional scRNAseq data
Andrew J. Steindl
João Felipe Rocha
Brian Tshilengi Di Bassinga
Zachary Warren
Matthew Scicluna
César Miguel Valdez Cordova
Shabarni Gupta
Leire Torices
Daniel Neumann
Timothy J. Mann
Ihuan Gunawan
Dhananjay Bhaskar
John G. Lock
Christine L. Chaffer
Guy Wolf
Smita Krishnaswamy
High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these da… (see more)tasets determine the types of information that can be extracted. For example, clustered data supports cell-type identification, trajectory structures support transition analysis, and archetypal structures capture continua of cellular behaviors. Existing analysis pipelines often assume a specific shape. The standard Seurat pipeline combines UMAP visualization with Louvain clustering and therefore assumes clustered data, while tools such as Monocle and SPADE assume tree-like structures, and flow-based models such as MIOFlow and Conditional Flow Matching target trajectories. Choosing which pipeline to apply is therefore often left to bioinformaticians who visually inspect datasets before selecting an analysis strategy. With the rise of agentic AI scientists, automating shape detection is increasingly important for selecting downstream analysis pipelines. To address this problem, we introduce scShapeBench, a benchmark dataset for shape detection containing both synthetic and expert-annotated single-cell datasets. Synthetic datasets are sampled from ground-truth skeleton graphs with controlled variance. Real single-cell datasets are curated from diverse sources and annotated by experts into four categories: clusters, single trajectory, multi-branching, and archetypal. We additionally introduce scReebTower, a baseline method that uses diffusion geometry to extract Reeb graphs and connect visualization with pipeline selection. We provide topology-aware evaluation metrics and compare scReebTower against PAGA and Mapper on synthetic and real data. Our results indicate that scReebTower outperforms existing baselines. Overall, our contributions span benchmarks, evaluation metrics, and a baseline for automated shape detection in single-cell data.
2026-05-11
arXiv (preprint)
doi.org
arxiv.org
Sleep Spindle-Locked Targeted Memory Reactivation Enhances Declarative Memory Consolidation
Vaishali Mutreja
Prakriti Gupta
Ovidiu Lungu
Latifa Lazzouni
Ella Gabitov
Habib Benali
Hugo Jourde
Giovanni Beltrame
Emily BJ Coffey
Jean-Marc Lina
Geneviève Albouy
Bradley King
Arnaud Boutin
Julie Carrier
Julien Doyon
Abstract Study Objectives Sleep spindles are implicated in memory consolidation. Yet direct evidence linking spindle dynamics to declarative… (see more) memory outcomes remains limited. We thus tested whether targeted memory reactivation (TMR) time-locked to sleep spindles enhances declarative memory, and whether the temporal organization of stimulated spindles–trains versus isolated events–is selectively associated with distinct memory outcomes. Methods Twenty-eight healthy young adults learned image locations from two categories (animals, clothing) in a grid, each paired with a distinct auditory cue. During overnight NREM sleep, one cue was replayed time-locked to spindles detected in real-time using a closed-loop system (TMR condition); the other served as the non-reactivated control (No-TMR condition). Category-cue assignment was counterbalanced. Post-sleep recall, recognition accuracy, and movement time were assessed. Results Recall accuracy was significantly higher in the TMR than the No-TMR condition (93.96% vs. 90.61%, p = .024), whereas recognition accuracy ( p = .139) and movement time ( p = .651) did not differ. Stimulation intensity within spindle trains correlated with the TMR effect on recall (Spearman ρ = .531, p = .004), whereas the proportion of isolated spindle stimulations correlated with the TMR effect on recognition (ρ = .563, p = .002). Cross-associations were not significant. Conclusions Spindle-locked TMR enhances recall-based declarative memory retention. The selective association between spindle temporal clustering and memory outcomes suggests that train-embedded and isolated spindles support different aspects of memory consolidation, highlighting spindle temporal context as a functionally relevant dimension of sleep-dependent memory processing.
2026-05-11
bioRxiv (preprint)
doi.org
A systematic review of human-LLM interactions in computational thinking empirical studies
Yimei Zhang
You Song
Doina Precup
Reihaneh Rabbany
Maria Cutumisu
2026-05-11
Computer Science Education (published)
doi.org
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 (published)
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… (see more) 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 (preprint)
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 (published)
doi.org