Publications

Improved Ising Model Formulation for Polar Codes
Ryan Seah
Warren J. Gross
This paper presents an improved Ising model framework for polar codes, termed POLARIS, which reduces the number of binary variables by incor… (voir plus)porating rate-1 node structures and embedding elements of successive-cancellation decoding into the Ising formulation. The decoder scales efficiently to block lengths up to N = 64, doubling prior Ising-based limits. POLARIS achieves near-successive-cancellation list performance within 0.4 dB while reducing QUBO dimensionality from 192 to 126 variables. These advancements bring Ising-based polar decoding closer to practical realization, offering improved efficiency for implementation on both quantum and hybrid CMOS-classical annealing hardware.
2026-05-18
2026 IEEE 56th International Symposium on Multiple-Valued Logic (ISMVL) (publié)
doi.org
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
Alexis Roger
Prateek Humane
Zhenghan Tai
Gwen Legate
Andrei Mircea
Vasilii Feofanov
Irina Rish
Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer ari… (voir plus)ses because language pretraining preconditions time series training with a reusable manifold. A linear probe on frozen LLM states decodes realistic time-series trajectories without paired supervision, and retrieval in this projected space yields competitive forecasts, showing that structure and dynamics exist before finetuning. Pretrained initialization also improves optimization, producing coherent gradients and a highly anisotropic loss landscape unlike random initialization. Finetuning then acts as low-dimensional alignment, reusing existing directions rather than learning temporal primitives from scratch, as evidenced by low-rank updates, subspace alignment, and shared features for periodicity, trend, and repetition. Together, these results support a geometric account of LLM-to-time-series transfer: language pretraining builds the manifold, and finetuning projects numerical dynamics onto task-relevant directions.
2026-05-18
arXiv (prépublication)
doi.org
arxiv.org
Matérn Noise for Triangulation-Agnostic Flow Matching on Meshes
Tianshu Kuai
Arman Maesumi
Daniel Ritchie
Noam Aigerman
This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained mod… (voir plus)el can be applied to different meshes and triangulations effectively. Practically, the paper adapts the flow matching (FM) paradigm to a mesh-based, triangulation-agnostic setting. Theoretically, it proposes a specific noise distribution which is triangulation agnostic, to be used inside the FM model's denoising process. While noise distributions are usually trivial to devise for, e.g., images, devising a triangulation-agnostic distribution proves to be a much more difficult task. We formulate a mathematical definition of triangulation agnosticism of distributions, via their spectrum. We then show that a discretization of a specific Gaussian random field called a Matérn process holds these desired properties, and provides a simple and efficient sampling algorithm. We use it as our noise model, and adapt FM to the triangulation-agnostic setting by using a state-of-the-art approach for learning signals on meshes in the gradient domain -- PoissonNet -- as the denoiser. We conduct experiments on elaborate tasks such as sampling elastic rest states, and generating poses of humanoids. Our method is shown to be capable of producing highly realistic results for meshes of over one million triangles, significantly exceeding the state-of-the-art in quality and diversity.
2026-05-18
arXiv (prépublication)
doi.org
arxiv.org
A Measure-Theoretic Analysis of Reasoning: Structural Generalization and Approximation Limits
Yuyang Zhang
Yifu Zhang
Xuehai Zhou
Xiaoyin Chen
While empirical scaling laws for LLM reasoning are well-documented, the theoretical mechanisms governing out-of-distribution (OOD) generaliz… (voir plus)ation remain elusive. We formalize reasoning via optimal transport, projecting discrete trajectories into a continuous metric space to quantify domain shifts using the Wasserstein-1 distance. Invoking Kantorovich duality, we bound OOD generalization via architectural Lipschitz continuity and functional approximation limits. This exposes two primary constraints. First, position-dependent attention (e.g., Absolute Positional Encoding) fails to preserve shift invariance, yielding an
2026-05-18
arXiv (prépublication)
doi.org
arxiv.org
RFGWRK: a hybrid downscaling framework for high-resolution precipitation mapping in geohazard-prone mountainous regions
Simin Zhang
Zeshuang Zheng
Jun Ding
Shengbing Yang
Yuan Zeng
2026-05-18
International Conference on Remote Sensing, Surveying, and Mapping (publié)
doi.org
A Universal Source-Free Class Unlearning Framework via Synthetic Embeddings
Zahra Dehghani Tafti
Pablo Piantanida
Mohammadhadi Shateri
Class unlearning in neural classifiers refers to selectively removing the model’s ability to recognize a target (forget) class by reshapin… (voir plus)g the decision boundaries. This is essential when taxonomies change, labels are corrected, or legal or ethical requirements mandate class removal. The objective is to preserve performance on the remaining (retain) classes while avoiding costly full retraining. Existing methods generally require access to the source, i.e., forget/retain data or a relevant surrogate dataset. This dependency limits their applicability in scenarios where access to source data is restricted or unavailable. Even the recent source-free class unlearning methods rely on generating samples in the data space, which is computationally expensive and not even essential for doing class unlearning. In this work, we propose a novel source-free class unlearning framework that enables existing unlearning methods to operate using only the deployed model. We show that, under assumptions on the forget loss with respect to logits, class unlearning can be performed source-free for any given neural classifier by utilizing randomly generated samples within the classifier’s intermediate space. Specifically, randomly generated embeddings pseudo-labeled by the model as belonging to the forget or retain classes can support effective source-free unlearning. Our analysis further shows that, under conditions on the forget loss and synthetic forget embeddings, minimizing the forget loss induces expected logit shifts consistent with class unlearning, without requiring a specific parametric form of the embedding distribution. We validate our framework on four backbone architectures, ResNet-18, ResNet-50, ViT-B/16, and Swin-T, across three benchmark datasets, CIFAR-10, CIFAR-100, and TinyImageNet. Our experimental results show that existing class unlearning methods can operate within our source-free framework, with minimal impact on their forgetting efficacy and retain class accuracy. The code is available at https://github.com/Yasaman-dt/Source_Free_Class_Unlearning.
2026-05-18
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero
Parham Saremi
Emily Kaczmarek
Brennan Nichyporuk
Tal Arbel
2026-05-17
Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press
Factorized and Vectorized Execution: Optimizing Analytical and Semantic Queries over Relations
Sunny Yasser
Anas Dorbani
Amine Mhedhbi
Many-to-many joins are central to analytical and semantic workloads such as fraud detection, network analysis, and recommendation, where ins… (voir plus)ights arise from relationships between entities. These workloads often suffer from an explosion of intermediate results, sometimes orders of magnitude larger than the inputs. Factorized representations address this problem by exploiting conditional independence among attributes to encode intermediates more compactly. In some cases, they can reduce the output size asymptotically below the worst-case output size. However, adopting factorization in modern vectorized query processors remains challenging: factorized representations are hierarchical, whereas vectorized execution is built around flat, block-oriented processing. Prior approaches either rely on full materialization or support only restricted factorization layouts, sacrificing much of the benefits of both factorization and vectorization. We present FFX, a novel engine for F ast F actorized e X ecution. FFX is the first pipelined engine to support arbitrary factorization schemes while preserving full vectorization. The engine introduces packed factorized vectors and operators that maintain cache-friendly, contiguous layouts. Beyond analytics, FFX also co-optimizes semantic operators by serializing factorized intermediates into compact prompts for large language models (LLMs), substantially reducing token usage and inference cost while maintaining output quality and, in some cases, improving it. Together, these contributions enable efficient execution of join-heavy analytical queries, including queries augmented with semantic operators.
2026-05-17
Proceedings of the ACM on Management of Data (publié)
doi.org
Presupposition and Reasoning in Conditionals: A Theory-Based Study of Humans and LLMs
Tara Azin
Yongan Yu
Raj Singh
Olessia Jouravlev
Presupposition projection in conditionals is central to theories of meaning and pragmatics, yet it remains largely unevaluated in large lang… (voir plus)uage models. We address this gap through a parallel behavioral study comparing human judgments and LLM predictions on a normed dataset of conditional sentences that controls the relation between the antecedent and the projected presupposition. We collect likelihood ratings from 120 participants and four LLMs under matched contextual conditions. Results show that humans integrate probabilistic and pragmatic cues in their judgment, whereas LLMs show variable alignment with human patterns. Using a linguistically motivated checklist within an LLM-as-a-Judge framework, we further evaluate model reasoning. We observe models that best match human ratings often lack coherent pragmatic reasoning, while models with stronger reasoning produce less human-like judgments. These findings suggest that LLMs' performance on such tasks may result from surface pattern matching rather than pragmatic competence. Our findings highlight the importance of benchmarks grounded in linguistic theory for comparing humans and models.
2026-05-17
arXiv (prépublication)
doi.org
arxiv.org
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Tal Arbel
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (voir plus)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
2026-05-17
Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press
Scalable Environments Drive Generalizable Agents
Jiayi Zhang
Fanqi Kong
Guibin Zhang
Maojia Song
Zhaoyang Yu
Jianhao Ruan
Jinyu Xiang
Bang Liu
Chenglin Wu
Yuyu Luo
Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues th… (voir plus)at such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather than only increasing trajectories or tasks within fixed benchmarks. Current scaling practices largely focus on collecting more experience or broader task sets under fixed interaction rules, leaving agents brittle when underlying interfaces, dynamics, observations, or feedback signals change. The core challenge is therefore a world-level distribution shift: agents need systematic exposure to environments with meaningfully different executable rule-sets. To clarify this challenge, we propose a unified taxonomy that separates trajectory scaling, task scaling, and environment scaling by their primary deliverables and by what changes in the executable rule-set. Building on this taxonomy, we synthesize construction paradigms for scalable environments, contrasting programmatic generators that prioritize controllability and verifiability with generative world models that offer broader coverage and open-endedness. We further outline how environment scaling can be coupled with stateful learning mechanisms, emphasizing learned update rules for cross-environment adaptation. We conclude by discussing alternative perspectives and argue that scalable environments provide the essential substrate for measurable and controllable progress toward robust general agents.
2026-05-17
arXiv (prépublication)
doi.org
arxiv.org
Adaptive PK/PD model optimization: a comparative analysis of bounded optimization methods for individual BIS prediction
Zheyan Tu
Sean Jeffries
Eric Pelletier
Oliver Cafferty
Joshua Morse
Avinash Sinha
Thomas Hemmerling
2026-05-15
Journal of Pharmacokinetics and Pharmacodynamics (publié)
doi.org