Publications

FLAM: Frame-Wise Language-Audio Modeling

Yusong Wu

Christos Tsirigotis

Ke Chen

Cheng-Zhi Anna Huang

Aaron Courville

Oriol Nieto

Prem Seetharaman

Justin Salamon

Recent multi-modal audio-language models (ALMs) excel at text-audio retrieval but struggle with frame-wise audio understanding. Prior works … (see more)use temporal-aware labels or unsupervised training to improve frame-wise capabilities, but they still lack fine-grained labeling capability to pinpoint when an event occurs. While traditional sound event detection models can precisely localize events, they are limited to pre-defined categories, making them ineffective for real-world scenarios with out-of-distribution events. In this work, we introduce FLAM, an open-vocabulary contrastive audio-language model capable of localizing specific sound events. FLAM employs a memory-efficient and calibrated frame-wise objective with logit adjustment to address spurious correlations, such as event dependencies and label imbalances during training. To enable frame-wise supervision, we leverage a large-scale dataset with diverse audio events, LLM-generated captions and simulation. Experimental results and case studies demonstrate that FLAM significantly improves the open-vocabulary localization capability while maintaining strong performance in global retrieval and downstream tasks.

2025-04-30

ICML.cc/2025/Conference (poster)

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N

Tianyu Zhang

Andrew Williams

Phillip Wozny

Kai-Hendrik Cohrs

Koen Ponse

Marco Jiralerspong

Soham Phade

Sunil Srinivasa

Lu Li

Yang Zhang

Prateek Gupta

Erman Acar

Irina Rish

Yoshua Bengio

Stephan Zheng

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing se… (see more)vere inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.

2025-04-30

ICML.cc/2025/Conference (poster)

Generative AI: Hype, Hope, and Responsible Use in Science and Everyday Life

Doina Precup

2025-04-30

Biological Psychiatry (published)

Half Search Space is All You Need

Pavel Rumiantsev

Mark J. Coates

2025-04-30

arXiv (published)

HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts

Neil He

Rishabh Anand

Hiren Madhu

Ali Maatouk

Smita Krishnaswamy

Leandros Tassiulas

Menglin Yang 0001

Rex Ying

2025-04-30

arXiv (published)

Impact of through‐slice gradient optimization for dynamic slice‐wise shimming in the cervico‐thoracic spinal cord

Arnaud Breheret

Alexandre D'Astous

Yixin Ma

Jason P. Stockmann

Julien Cohen‐Adad

This study investigates the effectiveness of through‐slice gradient optimization in dynamic slice‐wise B0 shimming of the cervico‐thor… (see more)acic spinal cord to enhance signal recovery in gradient‐echo (GRE) EPI sequences commonly used in functional MRI studies. Six volunteers underwent MRI acquisitions with dynamic shim updating (DSU) using a custom‐built 15‐channel AC/DC coil at 3 T. A magnetization‐prepared rapid gradient echo was acquired to segment the spine and to provide a clear image of the anatomical region of interest in the figures. GRE B0 field maps were used to measure field homogeneity before and after shimming; the pre‐shimming field map was used for optimization. Shimmed fields were dynamically applied to GRE–echo planar imaging acquisitions simulating functional MRI acquisitions under two shimming conditions: DSU with and without through‐slice gradient consideration. DSU with through‐slice gradient optimization increased the temporal signal‐to‐noise ratio at the T2 vertebral level by 201% compared with volume‐wise shim and by 28% compared with DSU without through‐slice. The residual geometric distortions were similar between DSU with and without through‐slice gradient optimization. A high signal loss penalty parameter was effective in simulations for reducing through‐slice gradient‐induced signal loss but led to instability and reduced image quality in actual acquisitions due to excessive in‐plane B0 inhomogeneities. Introducing a carefully balanced through‐slice gradient parameter in slice‐wise shimming substantially improves signal recovery in axial GRE images of the spinal cord, without compromising in‐plane homogeneity. This effective approach can advance spinal cord functional MRI applications at high field strengths.

2025-04-30

Magnetic Resonance in Medicine (published)

Improving Multilingual Math Reasoning for African Languages

Odunayo Ogundepo

Akintunde Oladipo

Kelechi Ogueji

Esther Adenuga

David Ifeoluwa Adelani

Jimmy Lin

Researchers working on low-resource languages face persistent challenges due to limited data availability and restricted access to computati… (see more)onal resources. Although most large language models (LLMs) are predominantly trained in high-resource languages, adapting them to low-resource contexts, particularly African languages, requires specialized techniques. Several strategies have emerged for adapting models to low-resource languages in todays LLM landscape, defined by multi-stage pre-training and post-training paradigms. However, the most effective approaches remain uncertain. This work systematically investigates which adaptation strategies yield the best performance when extending existing LLMs to African languages. We conduct extensive experiments and ablation studies to evaluate different combinations of data types (translated versus synthetically generated), training stages (pre-training versus post-training), and other model adaptation configurations. Our experiments focuses on mathematical reasoning tasks, using the Llama 3.1 model family as our base model.

2025-04-30

arXiv (published)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reyhane Askari-Hemmat

Mohammad Pezeshki

Elvis Dohmatob

Florian Bordes

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Michal Drozdzal

Adriana Romero-Soriano

Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a nov… (see more)el framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling, enabling models to focus on the most informative synthetic samples. Rather than generating a large dataset and pruning it afterward, DP efficiently approximates the direct generation of informative samples. We theoretically show how training on challenging, informative examples improves scaling laws and empirically validate that DP achieves better scaling performance with significantly fewer training samples and iterations. On ImageNet-100, DP generates 3.4x fewer samples and requires six times fewer iterations, while on ImageNet-1k, it generates 8x fewer samples with a 30 percent reduction in iterations, all while achieving superior performance compared to prior work.

2025-04-30

ICML.cc/2025/Conference (oral)

Latent Veracity Inference for Identifying Errors in Stepwise Reasoning

Minsu Kim

Jean-Pierre R. Falet

Oliver E. Richardson

Xiaoyin Chen

Moksh J. Jain

Sungjin Ahn

Sungsoo Ahn

Yoshua Bengio

Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (see more)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.

2025-04-30

arXiv (published)

Learning Penalty for Optimal Partitioning via Automatic Feature Extraction

Tung L. Nguyen

Toby Dylan Hocking

Changepoint detection identifies significant shifts in data sequences, making it important in areas like finance, genetics, and healthcare. … (see more)The Optimal Partitioning algorithms efficiently detect these changes, using a penalty parameter to limit the changepoints number. Determining the appropriate value for this penalty can be challenging. Traditionally, this process involved manually extracting statistical features, such as sequence length or variance to make the prediction. This study proposes a novel approach that uses recurrent neural networks to learn this penalty directly from raw sequences by automatically extracting features. Experiments conducted on 20 benchmark genomic datasets show that this novel method surpasses traditional methods in partitioning accuracy in most cases.

2025-04-30

arXiv (published)

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Sergio Arnaud

Paul McVay

Ada Martin

Arjun Majumdar

Krishna Murthy

Phillip Thomas

Ruslan Partsey

Daniel Dugas

Abha Gejji

Alexander Sax

Vincent-Pierre Berges

Mikael Henaff

Ayush Jain

Ang Cao

Ishita Prasad

Mrinal Kalakrishnan

Michael G. Rabbat

Nicolas Ballas

Mahmoud Assran

Oleksandr Maksymets … (see 2 more)

Aravind Rajeswaran

Franziska Meier

2025-04-30

ICML.cc/2025/Conference (poster)