Publications

Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
Mehran Shakerinava
Siamak Ravanbakhsh
Adam Oberman
Recent work has formalized the reward hypothesis through the lens of expected utility theory, by interpreting reward as utility. Hausner's f… (voir plus)oundational work showed that dropping the continuity axiom leads to a generalization of expected utility theory where utilities are lexicographically ordered vectors of arbitrary dimension. In this paper, we extend this result by identifying a simple and practical condition under which preferences cannot be represented by scalar rewards, necessitating a 2-dimensional reward function. We provide a full characterization of such reward functions, as well as the general d-dimensional case, in Markov Decision Processes (MDPs) under a memorylessness assumption on preferences. Furthermore, we show that optimal policies in this setting retain many desirable properties of their scalar-reward counterparts, while in the Constrained MDP (CMDP) setting -- another common multiobjective setting -- they do not.
2025-12-02
Conference on Neural Information Processing Systems (Accept (spotlight))
doi.org
openreview.net
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng
Arthur Ouaknine
Étienne Laliberté
Yoshua Bengio
David Rolnick
Hugo Larochelle
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Caption This, Reason That: VLMs Caught in the Middle
Zihan Weng
Lucas Gomez
Taylor Whittington Webb
Pouya Bashivan
Vision-Language Models (VLMs) have shown remarkable progress in visual understanding in recent years. Yet, they still lag behind human capab… (voir plus)ilities in specific visual tasks such as counting or relational reasoning. To understand the underlying limitations, we adopt methodologies from cognitive science, analyzing VLM performance along core cognitive axes: Perception, Attention, and Memory. Using a suite of tasks targeting these abilities, we evaluate state-of-the-art VLMs, including GPT-4o. Our analysis reveals distinct cognitive profiles: while advanced models approach ceiling performance on some tasks (e.g. category identification), a significant gap persists, particularly in tasks requiring spatial understanding or selective attention. Investigating the source of these failures and potential methods for improvement, we employ a vision-text decoupling analysis, finding that models struggling with direct visual reasoning show marked improvement when reasoning over their own generated text captions. These experiments reveal a strong need for improved VLM Chain-of-Thought (CoT) abilities, even in models that consistently exceed human performance. Furthermore, we demonstrate the potential of targeted fine-tuning on composite visual reasoning tasks and show that fine-tuning smaller VLMs substantially improves core cognitive abilities. While this improvement does not translate to large enhancements on challenging, out-of-distribution benchmarks, we show broadly that VLM performance on our datasets strongly correlates with performance on these other benchmarks. Our work provides a detailed analysis of VLM cognitive strengths and weaknesses and identifies key bottlenecks in simultaneous perception and reasoning while also providing an effective and simple solution.
2025-12-02
Neural Information Processing Systems (Accept (spotlight))
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Causal Climate Emulation with Bayesian Filtering
Sebastian Hickman
Ilija Trajković
Julia Kaltenborn
Francis Pelletier
Alex Archibald
Yaniv Gurwicz
Peer Nowack
David Rolnick
Julien Boussard
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These … (voir plus)simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physically-based causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a novel approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
Navita Goyal
Hal Daumé III
Alexandre Drouin
Dhanya Sridhar
2025-12-02
Conference on Neural Information Processing Systems (Accept (spotlight))
openreview.net
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Cognitive cartography of mammalian brains using meta-analysis of AI experts
Andrea I. Luppi
Hana Ali
Zhen-Qi Liu
Filip Milisav
Alessandro Gozzi
Danilo Bzdok
Bratislav Misic
The complexity of the brain is increasingly mirrored by the complexity of the neuroscientific literature, yet no individual mind can fully g… (voir plus)rasp the diversity of scales, methodologies and model organisms. Where human experts flag, the latest AI models excel: large language models can seamlessly integrate knowledge across scientific domains. Here we show how large language models can systematically and quantitatively synthesise literature-wide neuroscientific knowledge about the cognitive operations and dysfunctions associated with each brain region. Meta-analysis of AI experts reveals structure-function mappings to which existing meta-analytic frameworks are blind, demonstrated by lesions and direct intracranial stimulation. It also unlocks the possibility of extending quantitative literature meta-analysis and decoding of brain maps to other model organisms beyond human. As proof of concept, we integrate LLM meta-analysis with species-specific transcriptomics in human, macaque, and mouse, to discover an evolutionarily conserved molecular circuit for cognition. Altogether, meta-analysis of AI experts can fundamentally catalyze neuroscientific discovery by overcoming the barrier of data aggregation from heterogeneous studies, finally bringing together a scattered literature to identify emergent patterns and latent insights across disparate subfields, modalities, and species.
2025-12-02
bioRxiv (prépublication)
doi.org
Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models
Samuel Lavoie
Michael Noukhovitch
Aaron Courville
We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their input conditioning. This … (voir plus)paper investigates the representation used to condition diffusion models from the perspective that ideal representations should improve sample fidelity, be easy to generate, and be compositional to allow out-of-training samples generation. We introduce Discrete Latent Code (DLC), an image representation derived from Simplicial Embeddings trained with a self-supervised learning objective. DLCs are sequences of discrete tokens, as opposed to the standard continuous image embeddings. They are easy to generate and their compositionality enables sampling of novel images beyond the training distribution. Diffusion models trained with DLCs have improved generation fidelity, establishing a new state-of-the-art for unconditional image generation on ImageNet. Additionally, we show that composing DLCs allows the image generator to produce out-of-distribution samples that coherently combine the semantics of images in diverse ways. Finally, we showcase how DLCs can enable text-to-image generation by leveraging large-scale pretrained language models. We efficiently finetune a text diffusion language model to generate DLCs that produce novel samples outside of the image generator training distribution.
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
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Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
Yash Jhaveri
Harley Wiltzer
Patrick Shafto
Bellemare Marc-Emmanuel
David Meger
In the pursuit of finding an optimal policy, reinforcement learning (RL) methods generally ignore the properties of learned policies apart f… (voir plus)rom their expected return. Thus, even when successful, it is difficult to characterize which policies will be learned and what they will do. In this work, we present a theoretical framework for policy optimization that guarantees convergence to a particular optimal policy, via vanishing entropy regularization and a temperature decoupling gambit. Our approach realizes an interpretable, diversity-preserving optimal policy as the regularization temperature vanishes and ensures the convergence of policy derived objects--value functions and return distributions. In a particular instance of our method, for example, the realized policy samples all optimal actions uniformly. Leveraging our temperature decoupling gambit, we present an algorithm that estimates, to arbitrary accuracy, the return distribution associated to its interpretable, diversity-preserving optimal policy.
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
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openreview.net
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrović
Lazar Atanackovic
Viggo Moro
Kacper Kapuśniak
İsmail İlkan Ceylan
Michael Bronstein
Avishek Joey Bose
Alexander Tong
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Suc… (voir plus)h models rely on key assumptions about the underlying process in order to enable faithful learning of governing dynamics that mimic the actual system behavior. The de facto assumption in current approaches relies on the principle of least action that results in gradient field dynamics and leads to trajectories minimizing an energy functional between two probability measures. However, many real-world systems, such as cell cycles in single-cell RNA, are known to exhibit non-gradient, periodic behavior, which fundamentally cannot be captured by current state-of-the-art methods such as flow and bridge matching. In this paper, we introduce Curly Flow Matching (Curly-FM), a novel approach that is capable of learning non-gradient field dynamics by designing and solving a Schrödinger bridge problem with a non-zero drift reference process---in stark contrast to typical zero-drift reference processes---which is constructed using inferred velocities in addition to population snapshot data. We showcase Curly-FM by solving the trajectory inference problems for single cells, computational fluid dynamics, and ocean currents with approximate velocities. We demonstrate that Curly-FM can learn trajectories that better match both the reference process and population marginals. Curly-FM expands flow matching models beyond the modeling of populations and towards the modeling of known periodic behavior in physical systems. Our code repository is accessibleat: https://github.com/kpetrovicc/curly-flow-matching.git
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
openreview.net
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Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
Vineet Jain
Kusha Sareen
Mohammad Pedramfar
Siamak Ravanbakhsh
Adapting a pretrained diffusion model to new objectives at inference time remains an open problem in generative modeling. Existing steering … (voir plus)methods suffer from inaccurate value estimation, especially at high noise levels, which biases guidance. Moreover, information from past runs is not reused to improve sample quality, resulting in inefficient use of compute. Inspired by the success of Monte Carlo Tree Search, we address these limitations by casting inference-time alignment as a search problem that reuses past computations. We introduce a tree-based approach that samples from the reward-aligned target density by propagating terminal rewards back through the diffusion chain and iteratively refining value estimates with each additional generation. Our proposed method, Diffusion Tree Sampling (DTS), produces asymptotically exact samples from the target distribution in the limit of infinite rollouts, and its greedy variant, Diffusion Tree Search (DTS
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by dat… (voir plus)a complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.
2025-12-02
Conference on Neural Information Processing Systems (Accept (spotlight))
doi.org
openreview.net
Discovering Data Structures: Nearest Neighbor Search and Beyond
Omar Salemohamed
Laurent Charlin
Shivam Garg
Vatsal Sharan
Gregory Valiant
We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and prov… (voir plus)ides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or seeding with candidate data structures/algorithms. We first apply this framework to the problem of nearest neighbor search. In several settings, we are able to reverse-engineer the learned data structures and query algorithms. For 1D nearest neighbor search, the model discovers optimal distribution (in)dependent algorithms such as binary search and variants of interpolation search. In higher dimensions, the model learns solutions that resemble k-d trees in some regimes, while in others, they have elements of locality-sensitive hashing. The model can also learn useful representations of high-dimensional data and exploit them to design effective data structures. We also adapt our framework to the problem of estimating frequencies over a data stream, and believe it could also be a powerful discovery tool for new problems.
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net