Publications

PEACE: Prompt Engineering Automation for CLIPSeg Enhancement for Safe-Landing Zone Segmentation

Rongge Zhang

Antoine Robillard

Safe landing is essential in robotics applications, from industrial settings to space exploration. As artificial intelligence advances, we h… (voir plus)ave developed PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), a system that automatically generates and refines prompts for identifying landing zones in changing environments. Traditional approaches using fixed prompts for open-vocabulary models struggle with environmental changes and can lead to dangerous outcomes when conditions are not represented in the predefined prompts. PEACE addresses this limitation by dynamically adapting to shifting data distributions. Our key innovation is the dual segmentation of safe and unsafe landing zones, allowing the system to refine the results by removing unsafe areas from potential landing sites. Using only monocular cameras and image segmentation, PEACE can safely guide descent operations from 100 meters to altitudes as low as 20 meters. The testing shows that PEACE significantly outperforms the standard CLIP and CLIPSeg prompting methods, improving the successful identification of safe landing zones from 57% to 92%. We have also demonstrated enhanced performance when replacing CLIPSeg with FastSAM. The complete source code is available as an open-source software 1.

2025-10-18

2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

doi.org

arxiv.org

Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments

Miguel Saavedra-Ruiz

Samer B. Nashed

Charlie Gauthier

Liam Paull

Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change… (voir plus) between subsequent robot observations. Most robotic mapping or environment modeling algorithms are incapable of representing dynamic features in a way that enables predicting their future state. Instead, they opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting of future feature states. Specifically, we chain together mixtures of"persistence"and"emergence"filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the states of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find that Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.

2025-10-18

2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

doi.org

arxiv.org

Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models

Anita Kriz

Elizabeth Laura Janes

Xing Shen

Tal Arbel

2025-10-18

ICCVW @ IEEE/CVF International Conference on Computer Vision (publié)

doi.org

arxiv.org

Continuously Learning Bug Locations

Paulina Stevia Nouwou Mindom

Leuson Da Silva

Amin Nikanjam

Foutse Khomh

Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based… (voir plus) techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance of such models tends to degrade over time due to concept drift. Aiming to address this challenge, in this paper, we evaluate the potential of using Continual Learning (CL) techniques in multiple sub-tasks setting for bug localization (each of which operates on either stationary or non-stationary data), comparing it against a bug localization technique that leverages the BERT model, a deep reinforcement learning-based technique that leverages the A2C algorithm, and a DL-based function-level interaction model for semantic bug localization. Additionally, we enhanced the CL techniques by using logistic regression to identify and integrate the most significant bug-inducing factors. Our empirical evaluation across seven widely used software projects shows that CL techniques perform better than DL-based techniques by up to 61% in terms of Mean Reciprocal Rank (MRR), 44% in terms of Mean Average Precision (MAP), 83% in terms of top@1, 56% in terms of top@5, and 66% in terms of top@10 metrics in non-stationary setting. Further, we show that the CL techniques we studied are effective at localizing changesets relevant to a bug report while being able to mitigate catastrophic forgetting across the studied tasks and require up to 5x less computational effort during training. Our findings demonstrate the potential of adopting CL for bug localization in non-stationary settings, and we hope it helps to improve bug localization activities in Software Engineering using CL techniques.

2025-10-17

ACM Transactions on Software Engineering and Methodology (publié)

doi.org

arxiv.org

Hierarchical Differentiable Fluid Simulation

Xiangyu Kong

Arnaud Schoentgen

Damien Rioux‐Lavoie

Paul G. Kry

Derek Nowrouzezahrai

Differentiable simulation is an emerging field that offers a powerful and flexible route to fluid control. In grid‐based settings, high me… (voir plus)mory consumption is a long‐standing bottleneck that constrains optimization resolution. We introduce a two‐step algorithm that significantly reduces memory usage: our method first optimizes for bulk forces at reduced resolution, then refines local details over sub‐domains while maintaining differentiability. In trading runtime for memory, it enables optimization at previously unattainable resolutions. We validate its effectiveness and memory savings on a series of fluid control problems.

2025-10-16

Computer Graphics Forum (publié)

doi.org

Improving autoformalization via cycle consistency and incremental type-checking using language-model probabilistic programs

Mauricio Barba da Costa

Fabian Zaiser

Katherine M. Collins

Romir Patel

Timothy J. O'Donnell

Alexander K. Lew

Joshua B. Tenenbaum

Vikash Mansinghka

Cameron Freer

2025-10-16

NeurIPS.cc/2025/Workshop/MATH-AI (poster)

openreview.net

Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning

Andrew Holliday

Ahmed El-Geneidy

Gregory Dudek

2025-10-16

Transportmetrica B: Transport Dynamics (publié)

doi.org

arxiv.org

Neural Incremental Dynamic Inversion Control of a Multirotor Robotic Airship

Ely Carneiro de Paiva

José Raul Azinheira

Rafael de Angelis Cordeiro

José Reginaldo H. Carvalho

Apolo Marton

Giovanni Beltrame

2025-10-16

International Journal of Intelligent Systems (publié)

doi.org

Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO

Junyi Song

Shadi Zabad

Archer Yang

Simon Gravel

Yuemei Li

2025-10-16

Bioinformatics (publié)

doi.org

Tracking the Evolving Role of Artificial Intelligence in Implementation Science: Protocol for a Living Scoping Review of Applications, Evaluation Approaches and Outcomes

Guillaume Fontaine

Olivia Di Lalla

Susan Michie

Byron J. Powell

Vivian Welch

James Thomas

Jeffery Chan

Samira Abbasgholizadeh-Rahimi

France Légaré

Janna Hastings

Sylvie D. Lambert

Justin Presseau

Sharon E. Straus

Ian D. Graham

Ruopeng An

Daniel N. Elakpa

Meagan Mooney

Alenda Dwiadila Matra Putra

Rachael Laritz

Natalie Taylor

Background Artificial intelligence (AI) offers significant opportunities to improve the field of implementation science by supporting… (voir plus) key activities such as evidence synthesis, contextual analysis, and decision-making to promote the adoption and sustainability of evidence-based practices. This living scoping review aims to: (1) map applications of AI in implementation research and practice; (2) identify evaluation approaches, reported outcomes, and potential risks; and (3) synthesize reported research gaps and opportunities for advancing the use of AI in implementation science. Methods This scoping review will follow the Joanna Briggs Institute (JBI) methodology and the Cochrane guidance for living systematic reviews. A living scoping review is warranted to keep up with the rapid changes in AI and its growing use in implementation science. We will include empirical studies, systematic reviews, grey literature, and policy documents that describe or evaluate applications of AI to support implementation science across the steps of the Knowledge-to-Action (KTA) Model. AI methods and models of interest include machine learning, deep learning, natural language processing, large language models, and related technologies and approaches. A search strategy will be applied to bibliographic databases (MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, Web of Science), relevant journals, conference proceedings, and preprint servers. Two reviewers will independently screen studies and extract data on AI characteristics, specific implementation task according to the KTA Model, evaluation methods, outcome domains, risks, and research gaps. Extracted data will be analyzed descriptively and synthesized narratively using a mapping approach aligned with the KTA Model. Discussion This living review will consolidate the evidence base on how AI is applied across the spectrum of implementation science. It will inform researchers, policymakers, and practitioners seeking to harness AI to improve the adoption, scale-up, and sustainability of evidence-based interventions, while identifying areas for methodological advancement and risk mitigation. Review registration Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV

2025-10-16

F1000Research (publié)

doi.org

AugmenToxic: Leveraging Reinforcement Learning to Optimize LLM Instruction Fine-Tuning for Data Augmentation to Enhance Toxicity Detection.

Arezo Bodaghi

Benjamin C. M. Fung

Ketra A. Schmitt

Addressing the challenge of toxic language in online discussions is crucial for the development of effective toxicity detection models. This… (voir plus) pioneering work focuses on addressing imbalanced datasets in toxicity detection by introducing a novel approach to augment toxic language data. We create a balanced dataset by instructing fine-tuning of Large Language Models (LLMs) using Reinforcement Learning with Human Feedback (RLHF). Recognizing the challenges in collecting sufficient toxic samples from social media platforms for building a balanced dataset, our methodology involves sentence-level text data augmentation through paraphrasing existing samples using optimized generative LLMs. Leveraging generative LLM, we utilize the Proximal Policy Optimizer (PPO) as the RL algorithm to fine-tune the model further and align it with human feedback. In other words, we start by fine-tuning a LLM using an instruction dataset, specifically tailored for the task of paraphrasing while maintaining semantic consistency. Next, we apply PPO and a reward function, to further fine-tune (optimize) the instruction-tuned LLM. This RL process guides the model in generating toxic responses. We utilize the Google Perspective API as a toxicity evaluator to assess generated responses and assign rewards/penalties accordingly. This approach guides LLMs through PPO and the reward function, transforming minority class samples into augmented versions. The primary goal of our methodology is to create a balanced and diverse dataset to enhance the accuracy and performance of classifiers in identifying instances from the minority class. Utilizing two publicly available toxic datasets, we compared various techniques with our proposed method for generating toxic samples, demonstrating that our approach outperforms all others in producing a higher number of toxic samples. Starting with an initial 16,225 toxic prompts, our method successfully generated 122,951 toxic samples with a toxicity score exceeding 30%. Subsequently, we developed various classifiers using the generated balanced datasets and applied a cost-sensitive learning approach to the original imbalanced dataset. The findings highlight the superior performance of classifiers trained on data generated using our proposed method. These results highlight the importance of employing RL and a data-agnostic model as a reward mechanism for augmenting toxic data, thereby enhancing the robustness of toxicity detection models.

2025-10-15

ACM Transactions on the Web (publié)

doi.org

Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts

Maxime Heuillet

Yufei Cui

Boxing Chen

Audrey Durand

Prasanna Parthasarathi

2025-10-15

NeurIPS.cc/2025/Workshop/ER (accepté)

doi.org