Publications

Can GPT4 Generate Effective Feedback on Code Readability?

Xiaotian Su

Yajie Song

Marcus Messer

Jaromir Savelka

Maria Cutumisu

April Wang

2025-06-16

Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2 (published)

doi.org

Less is More: Undertraining Experts Improves Model Upcycling

Stefan Horoi

Guy Wolf

Eugene Belilovsky

Gintare Karolina Dziugaite

Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized dataset… (see more)s. This has led to a proliferation of expert models and adapters, often shared via platforms like HuggingFace and AdapterHub. To leverage these resources, numerous model upcycling methods have emerged, enabling the reuse of fine-tuned models in multi-task systems. A natural pipeline has thus formed to harness the benefits of transfer learning and amortize sunk training costs: models are pre-trained on general data, fine-tuned on specific tasks, and then upcycled into more general-purpose systems. A prevailing assumption is that improvements at one stage of this pipeline propagate downstream, leading to gains at subsequent steps. In this work, we challenge that assumption by examining how expert fine-tuning affects model upcycling. We show that long fine-tuning of experts that optimizes for their individual performance leads to degraded merging performance, both for fully fine-tuned and LoRA-adapted models, and to worse downstream results when LoRA adapters are upcycled into MoE layers. We trace this degradation to the memorization of a small set of difficult examples that dominate late fine-tuning steps and are subsequently forgotten during merging. Finally, we demonstrate that a task-dependent aggressive early stopping strategy can significantly improve upcycling performance.

2025-06-16

arXiv (preprint)

doi.org

arxiv.org

Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control

Florence Cloutier

Cyrus Neary

Adriana Hugessen

Viktor Todosijevic

Zina Kamel

Glen Berseth

As real-world infrastructure systems become increasingly complex and large-scale, there is a growing need for learning-based control strateg… (see more)ies that can make informed decisions in complex and dynamic environments. However, large-scale problems — such as power grid control — introduce high-dimensional action spaces and necessitate transferability across varying grid topologies. We introduce **H**ierarchical **E**xpert-Guided **R**econfiguration **O**ptimization for **G**raph **T**opologies, **HERO-GT**, a model-based planning approach that combines a pretrained graph neural network (GNN) for topology-aware action pruning with a Monte Carlo Tree Search (MCTS) planner for targeted, structured exploration. More specifically, the high-level GNN predicts a promising subset of actions, which the low-level MCTS agent uses to focus its search and reduce computational overhead while remaining adaptable to unseen graph structures. Furthermore, the MCTS planner leverages a given *default policy*---which may be defined, for example, by heuristics, problem relaxations, or rule-based methods---to bias the search and prioritize actions that are expected to improve performance over the default. We deploy HERO-GT in power grid environments, demonstrating that it not only improves over a strong default policy, but also scales to a realistic operational setting where exhaustive search becomes computationally infeasible.

2025-06-16

rl-conference.cc/RLC/2025/Workshop/RL4RS (published)

openreview.net

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning

Martin Klissarov

Akhil Bagaria

Ziyan Luo

George Konidaris

Doina Precup

Marlos C. Machado

Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intellig… (see more)ence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might define what constitutes good structure in the first place, or the kind of problems in which identifying it may be helpful. This work aims to identify the benefits of HRL from the perspective of the fundamental challenges in decision-making, as well as highlight its impact on the performance trade-offs of AI agents. Through these benefits, we then cover the families of methods that discover temporal structure in HRL, ranging from learning directly from online experience to offline datasets, to leveraging large language models (LLMs). Finally, we highlight the challenges of temporal structure discovery and the domains that are particularly well-suited for such endeavours.

2025-06-15

ArXiv (preprint)

doi.org

arxiv.org

Refining sequence-to-expression modelling with chromatin accessibility

Orsolya Lapohos

Gregory Fonseca

Amin Emad

2025-06-15

bioRxiv (preprint)

doi.org

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

Philippe Brouillard

Chandler Squires

Jonas Wahl

Konrad Paul Kording

Karen Sachs

Alexandre Drouin

Dhanya Sridhar

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.

2025-06-14

Proceedings of the Fourth Conference on Causal Learning and Reasoning (published)

doi.org

proceedings.mlr.press

Assessing and Learning Alignment of Unimodal Vision and Language Models

Le Zhang

Qian Yang

Aishwarya Agrawal

How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (see more)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/

2025-06-12

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept)

doi.org

arxiv.org

MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning

Wenhao Gu

Li Gu

Ching Yee Suen

Yang Wang

Recent advancements in handwritten text recognition (HTR) have enabled the effective conversion of handwritten text to digital formats. Howe… (see more)ver, achieving robust recognition across diverse writing styles remains challenging. Traditional HTR methods lack writer-specific personalization at test time due to limitations in model architecture and training strategies. Existing attempts to bridge this gap, through gradient-based meta-learning, still require labeled examples and suffer from parameter-inefficient fine-tuning, leading to substantial computational and memory overhead. To overcome these challenges, we propose an efficient framework that formulates personalization as prompt tuning, incorporating an auxiliary image reconstruction task with a self-supervised loss to guide prompt adaptation with unlabeled test-time examples. To ensure self-supervised loss effectively minimizes text recognition error, we leverage meta-learning to learn the optimal initialization of the prompts. As a result, our method allows the model to efficiently capture unique writing styles by updating less than 1% of its parameters and eliminating the need for time-intensive annotation processes. We validate our approach on the RIMES and IAM Handwriting Database benchmarks, where it consistently outperforms previous state-of-the-art methods while using 20x fewer parameters. We believe this represents a significant advancement in personalized handwritten text recognition, paving the way for more reliable and practical deployment in resource-constrained scenarios.

2025-06-12

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept)

doi.org

arxiv.org

Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments

Christopher Pal

Felix Heide

We introduce Scenario Dreamer, a fully data-driven generative simulator for autonomous vehicle planning that generates both the initial traf… (see more)fic scene - comprising a lane graph and agent bounding boxes - and closed-loop agent behaviours. Existing methods for generating driving simulation environments encode the initial traffic scene as a rasterized image and, as such, require parameter-heavy networks that perform unnecessary computation due to many empty pixels in the rasterized scene. Moreover, we find that existing methods that employ rule-based agent behaviours lack diversity and realism. Scenario Dreamer instead employs a novel vectorized latent diffusion model for initial scene generation that directly operates on the vectorized scene elements and an autoregressive Transformer for data-driven agent behaviour simulation. Scenario Dreamer additionally supports scene extrapolation via diffusion inpainting, enabling the generation of unbounded simulation environments. Extensive experiments show that Scenario Dreamer outperforms existing generative simulators in realism and efficiency: the vectorized scene-generation base model achieves superior generation quality with around 2x fewer parameters, 6x lower generation latency, and 10x fewer GPU training hours compared to the strongest baseline. We confirm its practical utility by showing that reinforcement learning planning agents are more challenged in Scenario Dreamer environments than traditional non-generative simulation environments, especially on long and adversarial driving environments.

2025-06-12

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept)

doi.org

arxiv.org

Spectral State Space Model for Rotation-Invariant Visual Representation Learning

Sahar Dastani

Ali Bahri

Moslem Yazdanpanah

Mehrdad Noori

David Osowiechi

Gustavo Adolfo Vargas Hakim

Farzad Beizaee

Milad Cheraghalikhani

Arnab Kumar Mondal

Hervé Lombaert

Christian Desrosiers

State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling glob… (see more)al relationships with linear complexity. SSMs are specifically designed to capture spatially proximate relationships of image patches. However, they fail to identify relationships between conceptually related yet not adjacent patches. This limitation arises from the non-causal nature of image data, which lacks inherent directional relationships. Additionally, current vision-based SSMs are highly sensitive to transformations such as rotation. Their predefined scanning directions depend on the original image orientation, which can cause the model to produce inconsistent patch-processing sequences after rotation. To address these limitations, we introduce Spectral VMamba, a novel approach that effectively captures the global structure within an image by leveraging spectral information derived from the graph Laplacian of image patches. Through spectral decomposition, our approach encodes patch relationships independently of image orientation, achieving rotation invariance with the aid of our Rotational Feature Normalizer (RFN) module. Our experiments on classification tasks show that Spectral VMamba outperforms the leading SSM models in vision, such as VMamba, while maintaining invariance to rotations and a providing a similar runtime efficiency.

2025-06-12

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept)

doi.org

arxiv.org

StarVector: Generating Scalable Vector Graphics Code from Images and Text

Juan A. Rodriguez

Abhay Puri

Shubham Agarwal

Issam H. Laradji

Sai Rajeswar

David Vázquez

Christopher Pal

Marco Pedersoli

Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation metho… (see more)ds have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond path curves. To address these issues, we introduce StarVector, a multimodal large language model for SVG generation. It performs image vectorization by understanding image semantics and using SVG primitives for compact, precise outputs. Unlike traditional methods, StarVector works directly in the SVG code space, leveraging visual understanding to apply accurate SVG primitives. To train StarVector, we create SVG-Stack, a diverse dataset of 2M samples that enables generalization across vectorization tasks and precise use of primitives like ellipses, polygons, and text. We address challenges in SVG evaluation, showing that pixel-based metrics like MSE fail to capture the unique qualities of vector graphics. We introduce SVG-Bench, a benchmark across 10 datasets, and 3 tasks: Image-to-SVG, Text-to-SVG generation, and diagram generation. Using this setup, StarVector achieves state-of-the-art performance, producing more compact and semantically rich SVGs.

2025-06-12

IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept)

doi.org

arxiv.org

Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search

Tabitha Edith Lee

Nan Rosemary Ke

Sarvesh Patil

Annya Dahmani

Eunice Yiu

Esra'a Saleh

Alison Gopnik