Publications

Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion
Somjit Nath
Jackson J Cone
Derek Nowrouzezahrai
Samira Ebrahimi Kahou
Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynam… (voir plus)ically fusing multiple sources of information through adaptive gating mechanisms. Inspired by these principles, we propose a novel reinforcement learning (RL) framework that encourages the disentanglement of dynamics-specific and reward-specific features, drawing direct parallels to how neural circuits separate and integrate information for efficient decision-making. Our approach leverages locally linear embeddings (LLEs) to capture the intrinsic, locally linear structure inherent in many environments—mirroring the local smoothness observed in neural population activity—while concurrently deriving reward-specific features through the standard RL objective. An attention mechanism, analogous to cortical gating, adaptively fuses these complementary representations on a per-state basis. Experimental results on benchmark tasks demonstrate that our method, grounded in neuroscientific principles, improves learning efficiency and overall performance compared to conventional RL approaches, highlighting the benefits of explicitly modeling local state structures and adaptive feature selection as observed in biological systems.
2026-05-24
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
Training Fair Tabular Foundation Models
Patrik Kenfack
Jesse C. Cresswell
Anthony L. Caterini
Samira Ebrahimi Kahou
Ulrich Aivodji
Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on … (voir plus)new data without task-specific training. Despite the increased use of TFMs in high-stakes decision-making, their fairness properties remain largely unexplored. In this work, we incorporate fairness constraints directly into TFM training, enabling fair predictions in a single forward pass. Our approach addresses two key challenges: limited access to sensitive attributes in training data, and the incompatibility of existing fairness techniques with the in-context learning paradigm. We propose \ftfm{}, a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer, which encourages the model to learn representations invariant to sensitive attributes. Experiments on 120 fairness tasks show consistent improvements in fairness while maintaining competitive accuracy.
2026-05-24
FMSD @ International Conference on Machine Learning (présentation orale)
openreview.net
openreview.net
Unified Neural Scaling Laws
Ethan Caballero
Priyank Jaini
David Krueger
Irina Rish
We present a functional form (that we refer to as a Unified Neural Scaling Law (UNSL)) that accurately models and extrapolates the scaling b… (voir plus)ehaviors of deep neural networks as multiple dimensions all vary simultaneously (i.e. how the evaluation metric of interest varies as one simultaneously varies the number of model parameters, training dataset size, number of training steps, number of inference steps, amount of compute, and various hyperparameters) for various architectures and for each of various tasks within a varied set of upstream and downstream tasks. This set includes large-scale vision, language, math, and reinforcement learning. When compared to other functional forms for neural scaling, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set.
2026-05-24
arXiv (prépublication)
doi.org
arxiv.org
From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition
Dimitrios Kollias
Panagiotis Tzirakis
Alan Cowen
Stefanos Zafeiriou
Irene Kotsia
Eric Granger
Marco Pedersoli
Simon Bacon
Jens Madsen
Soufiane Belharbi
Muhammad Haseeb Aslam
Chunchang Shao
Guanyu Hu
The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on … (voir plus)modelling, analysis, understanding of human affect and behavior in real-world, unconstrained environments. The workshop maintains its dual structure, comprising both a competition and a paper track. The ABAW Competition introduces a diverse set of challenges targeting key aspects of affective and behavioral understanding, including continuous affect (valence-arousal) estimation, discrete affect (expression and action unit) recognition, as well as more complex behavior analysis tasks, such as emotional mimicry intensity estimation, ambivalence/hesitancy recognition and fine-grained violence detection. These challenges are built upon large-scale in-the-wild datasets, providing comprehensive benchmarks for state-of-the-art approaches. In parallel, the paper track presents a wide range of contributions spanning pose, motion & behavior estimation, affect modelling & multimodal learning, benchmarks, datasets & evaluation protocols, fairness, robustness & deployment. Overall, the 10th ABAW Workshop and Competition continues to serve as a key platform for benchmarking, collaboration and innovation, shaping the development of next-generation multimodal, human-centered AI systems.
2026-05-23
arXiv (prépublication)
doi.org
Meta-Merging by Checkpoint Nowcasting
Albert Manuel Orozco Camacho
Boris Knyazev
Eugene Belilovsky
Guy Wolf
Model merging---the direct combination of parameters from independently fine-tuned networks---offers a way to compose task-specific capabili… (voir plus)ties without retraining or ensemble inference. Existing merge methods are often built from hand-crafted arithmetic or sparsification heuristics, leaving open whether general learned weight-space operators can be repurposed for merging directly. We study this question with NiNo, a pre-trained checkpoint-nowcasting meta-network originally designed to predict near-future training states from short checkpoint histories. We show that pre-trained NiNo can be reused as a data-free pairwise meta-merge operator for independently fine-tuned models. On an 8-task CLIP ViT-B/16 benchmark, NiNo is competitive with strong arithmetic baselines and consistently lands in the same functional region as weight averaging, Task Arithmetic, and TIES. Moreover, NiNo is best on HumanEval in a Qwen3 language extension among the compared merge methods, while extending meta-merge beyond pairs remains an open challenge. These results position learned checkpoint nowcasting as a practical starting point for data-free model merging and motivate future weight-space learners trained for merging explicitly.
2026-05-23
WSS @ International Conference on Machine Learning (poster)
openreview.net
openreview.net
AI Agent Safety is a Reinforcement Learning Problem
Reginald McLean
Tabitha Edith Lee
Montaser Mohammedalamen
Kevin Roice
Glen Berseth
Patrick M. Pilarski
Marlos C. Machado
Alyssa Lefaivre Škopac
Benjamin Rosman
With the rapid advancement and deployment of Agentic AI, our scientific understanding of capabilities and limitations has not kept pace, lea… (voir plus)ding to cases where AI agents cause harm. We argue that many of these safety limitations are not novel problems. Instead, the safety challenges currently facing AI agents can be seen as instances of problems the reinforcement learning (RL) community has studied rigorously for decades. The core of this argument concerns the problem formulation of AI agents. AI agents are designed to solve sequential decision-making problems: problems with long-term objectives in which actions have delayed consequences. To model these types of problem, the problem is set up the problem such that the agent receives observations, feedback on its progress, and then takes actions. This is precisely the formulation of the RL problem. In this paper, we formalize the problem equivalence, which we then leverage to argue that \textbf{AI Agent safety is a reinforcement learning problem: the failure modes currently observed in deployed AI agents are structural instances of problems RL has formalized for decades, and the RL safety literature provides principled tools to diagnose and address them.}. We conclude with a call for deliberate collaboration between the RL and AI agent research communities: AI agent researchers gain access to principled frameworks, while RL researchers gain a class of real-world problems that could expose fundamental gaps in current RL benchmarks and theory.
2026-05-22
AIWILD @ International Conference on Machine Learning (publié)
openreview.net
openreview.net
Consistent Identification of Top-$K$ Nodes in Noisy Networks
Hui Shen
Eric D. Kolaczyk
Identifying the most influential nodes in a network, typically using centrality measures, is a central task in applied network analysis. How… (voir plus)ever, real-world networks are often constructed from noisy or incomplete data, which can distort rankings and lead to errors in identifying the true top-
2026-05-22
arXiv (prépublication)
doi.org
arxiv.org
ReCode: Unify Plan and Action for Universal Granularity Control
Zhaoyang Yu
Jiayi Zhang
Huixue Su
Yufan Zhao
Yifan Wu
Mingyi Deng
Jinyu Xiang
Yizhang Lin
Fanqi Kong
Lingxiao Tang
Yuyu Luo
Bang Liu
Chenglin Wu
Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where… (voir plus) planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitation stems from existing paradigms that enforce a rigid separation between high-level planning and low-level action, which impairs dynamic adaptability and limits generalization. We propose **ReCode** (**Re**cursive **Code** Generation), a novel paradigm that addresses this limitation by unifying planning and action within a single code representation. In this representation, ReCode treats high-level plans as abstract placeholder functions, which the agent then recursively decomposes into finer-grained sub-functions until reaching primitive actions. This recursive approach dissolves the rigid boundary between plan and action, enabling the agent to dynamically control its decision granularity. Furthermore, the recursive structure inherently generates rich, multi-granularity training data, enabling models to learn hierarchical decision-making processes. Extensive experiments show ReCode significantly surpasses advanced baselines in inference performance and demonstrates exceptional data efficiency in training, validating our core insight that unifying planning and action through recursive code generation is a powerful and effective approach to achieving universal granularity control.
2026-05-22
AIWILD @ International Conference on Machine Learning (publié)
doi.org
openreview.net
Rotation-Preserving Supervised Fine-Tuning
Hangzhan Jin
Tianwei Ni
Lu Li
Pierre-Luc Bacon
Mohammad Hamdaqa
Doina Precup
Supervised fine-tuning (SFT) improves in-domain performance but can degrade out-of-domain (OOD) generalization. Prior work suggests that thi… (voir plus)s degradation is related to changes in dominant singular subspaces of pretrained weight matrices. However, directly identifying loss-sensitive directions with Hessian or Fisher information is computationally expensive at LLM scale. In this work, we propose preserving projected rotations in pretrained singular subspaces as an efficient proxy for Fisher-sensitive directions, which we call Rotation-Preserving Supervised Fine-Tuning (RPSFT). RPSFT penalizes changes in the projected top-
2026-05-22
CATS @ International Conference on Machine Learning (poster)
doi.org
openreview.net
WebArena-Pro: A Heterogeneous, Multimodal, Reproducible Benchmark for Web Agents
Imene Kerboua
Fatemeh Pesaran zadeh
Xing Han Lu
Weijian Qi
Alexander Miller
Junyi Song
Yunjia Tian
Dongjin Kang
Seyeon Choi
Marzia Nouri
Ewen Gueguen
Matteo Boglioni
Fengyuan Liu
Zeyi Liao
Mengqi Yuan
Yue Li
Alexandre Lacoste
Alexandre Drouin
Spandana Gella
Huan Sun … (voir 2 de plus)
Gunhee Kim
Siva Reddy
Web agents powered by large language and vision-language models are increasingly applied to realistic browser work that spans heterogeneous … (voir plus)applications, multimodal content, and stateful workflows. However, existing reproducible web-agent benchmarks cover only a small number of web applications drawn from a few software categories, and restrict modality to text and vision. Live benchmarks broaden site coverage but sacrifice reproducibility, since pages and data drift between runs. Moreover, existing benchmarks do not meaningfully evaluate whether agents can understand and use audio and video content embedded within web tasks. To address these gaps, we introduce WebArena-Pro, a benchmark comprising 300 tasks across 20 self-hosted web applications in six domain categories, spanning distinct interface conventions, workflows, and data models. Across the evaluated agents, the best performance is achieved by Gemini 3.1 Pro, which attains 37.0 % success under a 50-step budget, while open-source models' performance does not exceed 27.7% success. Among reproducible, human-curated web agent benchmarks, WebArena-Pro provides the broadest application coverage and the most comprehensive multimodal support to date. The benchmark treats audio and video as core observations alongside text and vision, with dedicated actions for extracting information from each. WebArena-Pro runs each task in isolation and supports reproducible, parallel evaluation. Tasks are authored through a dedicated annotator interface, filtered by LLM-assisted triage, and finally validated by humans before release.
2026-05-22
AIWILD @ International Conference on Machine Learning (publié)
openreview.net
openreview.net
A Pragmatic Approach to Learned Indexing in RocksDB: Targeted Optimizations with Minimal System Modification
Shubham Vashisth
Olivier Michaud
Bettina Kemme
Oana Balmau
Learned indexes have emerged as a promising alternative to traditional index structures, offering higher throughput and lower memory usage b… (voir plus)y approximating the cumulative key distribution function with lightweight models. Despite these benefits, adoption in production systems remains limited, partly because learned indexes that support concurrency and persistence as effectively as, e.g., the B+-Tree, do not yet exist, while many research prototypes introduce substantial complexity. In this paper, we investigate whether off-the-shelf learned indexes can be integrated into a production database with minimal storage-engine redesign. Using RocksDB as a case study, we exploit its separation between in-memory Memtables and immutable on-disk files to deploy specialized indexes at each level. We show that directly applying existing learned indexes is insufficient under write-heavy workloads because frequent Memtable replacement prevents models from fully adapting. To address this, we introduce a reuse mechanism that preserves structural knowledge across Memtable instances. At the storage level, we replace RocksDB's disk index with a learned index without modifying the storage layer or read path. We further adapt a read-only learned index to be block-aware, enabling worst-case single-I/O lookups. We implement these techniques in MountDB, an extension of RocksDB. Experiments on large-scale workloads with diverse data distributions and access patterns show up to 1.5X higher write throughput and 2.1X higher read throughput than state-of-the-art systems, demonstrating that established learned indexes can be integrated into production systems with minimal overhead and substantial performance benefits.
2026-05-21
arXiv (prépublication)
doi.org
arxiv.org
SeedER: Seed-and-Expand Retrieval from Knowledge Graphs
Hamed Shirzad
Frederik Wenkel
Dominique Beaini
Danica J. Sutherland
Emmanuel Noutahi
Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-… (voir plus)graph expansion grows rapidly, and dense embedding methods struggle with multi-hop compositional queries. Existing agent-based graph exploration approaches, while expressive, are often too expensive for large-scale retrieval. We introduce SeedER (Seed-and-Expand Retrieval), a retrieval framework that explicitly leverages KG structure through iterative, low-cost expansion. SeedER first seeds a compact set of core nodes using lightweight dense and entity-based retrieval, then selectively expands this set via a learned graph-aware policy trained with reinforcement learning. This design decomposes global reasoning into reusable local decisions, enabling efficient discovery of query-relevant nodes while tightly controlling expansion cost. We show theoretical limitations of dense retrieval on compositional graph queries, and establish advantages of SeedER from both compositional generalization and graph-constrained submodular optimization perspectives. Empirically, SeedER substantially improves recall with compact candidate sets over strong dense and graph-augmented baselines, making it an effective first-stage retriever for knowledge-intensive reasoning systems.
2026-05-21
arXiv (prépublication)
doi.org
arxiv.org