Publications

Adaptive Inference-Time Scaling via Cyclic Diffusion Search

Gyubin Lee

Truong Nhat Nguyen Bao

Jaesik Yoon

Dongwoo Lee

Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. Ho… (voir plus)wever, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference-and propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework. ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination. It comprises three components: Cyclic Diffusion Search, Automatic Exploration-Exploitation Balancing, and Adaptive Thinking Time. Experiments show that ABCD improves performance across diverse tasks while maintaining computational efficiency.

2025-05-20

ArXiv (prépublication)

arxiv.org

Adaptive Inference-Time Scaling via Cyclic Diffusion Search

Gyubin Lee

Truong Nhat Nguyen Bao

Jaesik Yoon

Dongwoo Lee

Minsu Kim

Yoshua Bengio

Sungjin Ahn

Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. Ho… (voir plus)wever, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference-and propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework. ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination. It comprises three components: Cyclic Diffusion Search, Automatic Exploration-Exploitation Balancing, and Adaptive Thinking Time. Experiments show that ABCD improves performance across diverse tasks while maintaining computational efficiency.

2025-05-20

ArXiv (prépublication)

arxiv.org

Adaptive Inference-Time Scaling via Cyclic Diffusion Search

Gyubin Lee

Truong Nhat Nguyen Bao

Jaesik Yoon

Dongwoo Lee

Minsu Kim

Yoshua Bengio

Sungjin Ahn

Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. Ho… (voir plus)wever, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference-and propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework. ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination. It comprises three components: Cyclic Diffusion Search, Automatic Exploration-Exploitation Balancing, and Adaptive Thinking Time. Experiments show that ABCD improves performance across diverse tasks while maintaining computational efficiency.

2025-05-20

ArXiv (prépublication)

arxiv.org

Determinants of surgical approach to pediatric appendicitis in Brazil.

Ayla Gerk

Paulo Henrique Moreira Melo

Mohsen Amoei

Shreenik Kundu

Luiza Telles

Justina O. Seyi-Olajide

Dunya Moghul

Gabriel Schnitman

Cristina Camargo

David P. Mooney

Joaquim Bustorff-Silva

Dan Poenaru

2025-05-20

Pediatric Surgery International (publié)

doi.org

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Marc Gendron-Bellemare

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

Kevin M Roccapriore

We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (voir plus)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2025-05-20

Advanced Materials Interfaces (publié)

doi.org

arxiv.org

Multi-center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study

Eva Alonso‐Ortiz

Daniel Papp

Robert L. Barry

Kyota Poëti

Alan C. Seifert

Kyle M. Gilbert

Nibardo Lopez‐Rios

Jan Paska

Falk Eippert

Nikolaus Weiskopf

Laura Beghini

Nadine Graedel

Robert Trampel

Martina F Callaghan

Christoph S. Aigner

Patrick Freund

Maryam Seif

Aurélien Destruel

Virginie Callot

Johanna Vannesjo … (voir 1 de plus)

Julien Cohen-Adad

2025-05-20

Magnetic Resonance in Medicine (publié)

doi.org

Multi‐center benchmarking of cervical spinal cord <scp>RF</scp> coils for 7 T <scp>MRI</scp>: A traveling spines study

Eva Alonso‐Ortiz

Daniel Papp

Robert L. Barry

Kyota Poëti

Alan C. Seifert

Kyle M. Gilbert

Nibardo Lopez‐Rios

Jan Paska

Falk Eippert

Nikolaus Weiskopf

Laura Beghini

Nadine Graedel

Robert Trampel

Martina F Callaghan

Christoph S. Aigner

Patrick Freund

Maryam Seif

Aurélien Destruel

Virginie Callot

Johanna Vannesjo … (voir 1 de plus)

Julien Cohen-Adad

2025-05-20

Magnetic Resonance in Medicine (publié)

doi.org

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs

Roozbeh Aghili

Xingfang Wu

Foutse Khomh

Heng Li

2025-05-20

ArXiv (prépublication)

arxiv.org

Self-Evolving Curriculum for LLM Reasoning

Nicolas Gontier

Ehsan Kamalloo

Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abili… (voir plus)ties in domains such as mathematics and code generation. A crucial factor influencing RL fine-tuning success is the training curriculum: the order in which training problems are presented. While random curricula serve as common baselines, they remain suboptimal; manually designed curricula often rely heavily on heuristics, and online filtering methods can be computationally prohibitive. To address these limitations, we propose Self-Evolving Curriculum (SEC), an automatic curriculum learning method that learns a curriculum policy concurrently with the RL fine-tuning process. Our approach formulates curriculum selection as a non-stationary Multi-Armed Bandit problem, treating each problem category (e.g., difficulty level or problem type) as an individual arm. We leverage the absolute advantage from policy gradient methods as a proxy measure for immediate learning gain. At each training step, the curriculum policy selects categories to maximize this reward signal and is updated using the TD(0) method. Across three distinct reasoning domains: planning, inductive reasoning, and mathematics, our experiments demonstrate that SEC significantly improves models'reasoning capabilities, enabling better generalization to harder, out-of-distribution test problems. Additionally, our approach achieves better skill balance when fine-tuning simultaneously on multiple reasoning domains. These findings highlight SEC as a promising strategy for RL fine-tuning of LLMs.

2025-05-20

ArXiv (prépublication)

doi.org

arxiv.org

Self-Evolving Curriculum for LLM Reasoning

Nicolas Gontier

Ehsan Kamalloo

Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abili… (voir plus)ties in domains such as mathematics and code generation. A crucial factor influencing RL fine-tuning success is the training curriculum: the order in which training problems are presented. While random curricula serve as common baselines, they remain suboptimal; manually designed curricula often rely heavily on heuristics, and online filtering methods can be computationally prohibitive. To address these limitations, we propose Self-Evolving Curriculum (SEC), an automatic curriculum learning method that learns a curriculum policy concurrently with the RL fine-tuning process. Our approach formulates curriculum selection as a non-stationary Multi-Armed Bandit problem, treating each problem category (e.g., difficulty level or problem type) as an individual arm. We leverage the absolute advantage from policy gradient methods as a proxy measure for immediate learning gain. At each training step, the curriculum policy selects categories to maximize this reward signal and is updated using the TD(0) method. Across three distinct reasoning domains: planning, inductive reasoning, and mathematics, our experiments demonstrate that SEC significantly improves models'reasoning capabilities, enabling better generalization to harder, out-of-distribution test problems. Additionally, our approach achieves better skill balance when fine-tuning simultaneously on multiple reasoning domains. These findings highlight SEC as a promising strategy for RL fine-tuning of LLMs.

2025-05-20

ArXiv (prépublication)

doi.org

arxiv.org

Self-Evolving Curriculum for LLM Reasoning

Alex Pich'e

Nicolas Gontier