Publications

NAVIX: Scaling Minigrid Environments with JAX
Eduardo Pignatelli
Jarek Liesen
Robert Tjarko Lange
Chris Lu
Pablo Samuel Castro
Laura Toni
As Deep Reinforcement Learning (Deep RL) research moves towards solving large-scale worlds, efficient environment simulations become crucial… (voir plus) for rapid experimentation. However, most existing environments struggle to scale to high throughput, setting back meaningful progress. Interactions are typically computed on the CPU, limiting training speed and throughput, due to slower computation and communication overhead when distributing the task across multiple machines. Ultimately, Deep RL training is CPU-bound, and developing batched, fast, and scalable environments has become a frontier for progress. Among the most used Reinforcement Learning (RL) environments, MiniGrid is at the foundation of several studies on exploration, curriculum learning, representation learning, diversity, meta-learning, credit assignment, and language-conditioned RL, and still suffers from the limitations described above. In this work, we introduce NAVIX, a re-implementation of MiniGrid in JAX. NAVIX achieves over
2025-09-17
NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (poster)
openreview.net
openreview.net
Object-centric Binding in Contrastive Language-Image Pretraining
Rim Assouel
Pietro Astolfi
Florian Bordes
Michal Drozdzal
Adriana Romero-Soriano
Recent advances in vision language models (VLM) have been driven by contrastive models such as CLIP, which learn to associate visual informa… (voir plus)tion with their corresponding text descriptions. However, these models have limitations in understanding complex compositional scenes involving multiple objects and their spatial relationships. To address these challenges, we propose a novel approach that diverges from commonly used strategies, which rely on the design of hard-negative augmentations. Instead, our work focuses on integrating inductive biases into pre-trained CLIP-like models to improve their compositional understanding without using any additional hard-negatives. To that end, we introduce a binding module that connects a scene graph, derived from a text description, with a slot-structured image representation, facilitating a structured similarity assessment between the two modalities. We also leverage relationships as text-conditioned visual constraints, thereby capturing the intricate interactions between objects and their contextual relationships more effectively. Our resulting model not only enhances the performance of CLIP-based models in multi-object compositional understanding but also paves the way towards more accurate and sample-efficient image-text matching of complex scenes.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
Yuyan Chen
Nico Lang
B. Christian Schmidt
Aditya Jain
Yves Basset
Sara Beery
Maxim Larrivée
David Rolnick
Global biodiversity is declining at an unprecedented rate, yet little information is known about most species and how their populations are … (voir plus)changing. Indeed, some 90% of Earth's species are estimated to be completely unknown. Machine learning has recently emerged as a promising tool to facilitate long-term, large-scale biodiversity monitoring, including algorithms for fine-grained classification of species from images. However, such algorithms typically are not designed to detect examples from categories unseen during training -- the problem of open-set recognition (OSR) -- limiting their applicability for highly diverse, poorly studied taxa such as insects. To address this gap, we introduce Open-Insect, a large-scale, fine-grained dataset to evaluate unknown species detection across different geographic regions with varying difficulty. We benchmark 38 OSR algorithms across three categories: post-hoc, training-time regularization, and training with auxiliary data, finding that simple post-hoc approaches remain a strong baseline. We also demonstrate how to leverage auxiliary data to improve species discovery in regions with limited data. Our results provide insights to guide the development of computer vision methods for biodiversity monitoring and species discovery.
2025-09-17
NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (spotlight)
doi.org
openreview.net
OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
Christina Kassab
Sacha Morin
Martin Büchner
Matias Mattamala
Kumaraditya Gupta
Abhinav Valada
Liam Paull
Maurice Fallon
3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language. However, at pre… (voir plus)sent the evaluation of these representations is limited to datasets with closed-set semantics that do not capture the richness of language. This work presents OpenLex3D, a dedicated benchmark for evaluating 3D open-vocabulary scene representations. OpenLex3D provides entirely new label annotations for scenes from Replica, ScanNet++, and HM3D, which capture real-world linguistic variability by introducing synonymical object categories and additional nuanced descriptions. Our label sets provide 13 times more labels per scene than the original datasets. By introducing an open-set 3D semantic segmentation task and an object retrieval task, we evaluate various existing 3D open-vocabulary methods on OpenLex3D, showcasing failure cases, and avenues for improvement. Our experiments provide insights on feature precision, segmentation, and downstream capabilities. The benchmark is publicly available at: https://openlex3d.github.io/.
2025-09-17
NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (poster)
openreview.net
openreview.net
Overcoming Long-Context Limitations of State-Space Models via Context-Dependent Sparse Attention
Zhihao Zhan
Jianan Zhao
Zhaocheng Zhu
Jian Tang
Efficient long-context modeling remains a critical challenge for natural language processing (NLP), as the time complexity of the predominan… (voir plus)t Transformer architecture scales quadratically with the sequence length. While state-space models (SSMs) offer alternative sub-quadratic solutions, they struggle to capture long-range dependencies effectively. In this work, we focus on analyzing and improving the long-context modeling capabilities of SSMs. We show that the widely used synthetic task, associative recall, which requires a model to recall a value associated with a single key without context, insufficiently represents the complexities of real-world long-context modeling. To address this limitation, we extend the associative recall to a novel synthetic task, \emph{joint recall}, which requires a model to recall the value associated with a key given in a specified context. Theoretically, we prove that SSMs do not have the expressiveness to solve multi-query joint recall in sub-quadratic time complexity. To resolve this issue, we propose a solution based on integrating SSMs with Context-Dependent Sparse Attention (CDSA), which has the expressiveness to solve multi-query joint recall with sub-quadratic computation. To bridge the gap between theoretical analysis and real-world applications, we propose locality-sensitive Hashing Attention with sparse Key Selection (HAX), which instantiates the theoretical solution and is further tailored to natural language domains. Extensive experiments on both synthetic and real-world long-context benchmarks show that HAX consistently outperforms SSM baselines and SSMs integrated with context-independent sparse attention (CISA).
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Planning and Learning in Average Risk-aware MDPs
Weikai Wang
Erick Delage
For continuing tasks, average cost Markov decision processes have well-documented value and can be solved using efficient algorithms. Howeve… (voir plus)r, it explicitly assumes that the agent is risk-neutral. In this work, we extend risk-neutral algorithms to accommodate the more general class of dynamic risk measures. Specifically, we propose a relative value iteration (RVI) algorithm for planning and design two model-free Q-learning algorithms, namely a generic algorithm based on the multi-level Monte Carlo (MLMC) method, and an off-policy algorithm dedicated to utility-based shortfall risk measures. Both the RVI and MLMC-based Q-learning algorithms are proven to converge to optimality. Numerical experiments validate our analysis, confirm empirically the convergence of the off-policy algorithm, and demonstrate that our approach enables the identification of policies that are finely tuned to the intricate risk-awareness of the agent that they serve.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Plasticity as the Mirror of Empowerment
David Abel
Michael Bowling
Andre Barreto
Will Dabney
Shi Dong
Steven Hansen
Anna Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agents are minimally entities that are influenced by their past observations and act to influence future observations. This latter capacity … (voir plus)is captured by empowerment, which has served as a vital framing concept across artificial intelligence and cognitive science. This former capacity, however, is equally foundational: In what ways, and to what extent, can an agent be influenced by what it observes? In this paper, we ground this concept in a universal agent-centric measure that we refer to as plasticity, and reveal a fundamental connection to empowerment. Following a set of desiderata on a suitable definition, we define plasticity using a new information-theoretic quantity we call the generalized directed information. We show that this new quantity strictly generalizes the directed information introduced by Massey (1990) while preserving all of its desirable properties. Under this definition, we find that plasticity is well thought of as the mirror of empowerment: The two concepts are defined using the same measure, with only the direction of influence reversed. Our main result establishes a tension between the plasticity and empowerment of an agent, suggesting that agent design needs to be mindful of both characteristics. We explore the implications of these findings, and suggest that plasticity, empowerment, and their relationship are essential to understanding agency
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
doi.org
openreview.net
POCO: Scalable Neural Forecasting through Population Conditioning
Yu Duan
Hamza Tahir Chaudhry
Misha B. Ahrens
Christopher D. Harvey
Matthew G. Perich
Karl Deisseroth
Kanaka Rajan
Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to… (voir plus) closed-loop neurotechnology. While recent models of population activity emphasize interpretability and behavioral decoding, neural forecasting-particularly across multi-session, spontaneous recordings-remains underexplored. We introduce POCO, a unified forecasting model that combines a lightweight univariate forecaster with a population-level encoder to capture both neuron-specific and brain-wide dynamics. Trained across five calcium imaging datasets spanning zebrafish, mice, and C. elegans, POCO achieves state-of-the-art accuracy at cellular resolution in spontaneous behaviors. After pre-training, POCO rapidly adapts to new recordings with minimal fine-tuning. Notably, POCO's learned unit embeddings recover biologically meaningful structure-such as brain region clustering-without any anatomical labels. Our comprehensive analysis reveals several key factors influencing performance, including context length, session diversity, and preprocessing. Together, these results position POCO as a scalable and adaptable approach for cross-session neural forecasting and offer actionable insights for future model design. By enabling accurate, generalizable forecasting models of neural dynamics across individuals and species, POCO lays the groundwork for adaptive neurotechnologies and large-scale efforts for neural foundation models. Code is available at https://github.com/yuvenduan/POCO.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
Tara Akhound-Sadegh
Jungyoon Lee
Avishek Joey Bose
Valentin De Bortoli
Arnaud Doucet
Michael M. Bronstein
Dominique Beaini
Siamak Ravanbakhsh
Kirill Neklyudov
Alexander Tong
Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact sc… (voir plus)ientific applications. A promising approach towards this challenge is the design of amortized samplers that borrow key ideas, such as probability path design, from state-of-the-art generative diffusion models. However, all existing diffusion-based samplers remain unable to draw samples from distributions at the scale of even simple molecular systems. In this paper, we propose Progressive Inference-Time Annealing (PITA), a novel framework to learn diffusion-based samplers that combines two complementary interpolation techniques: I.) Annealing of the Boltzmann distribution and II.) Diffusion smoothing. PITA trains a sequence of diffusion models from high to low temperatures by sequentially training each model at progressively higher temperatures, leveraging engineered easy access to samples of the temperature-annealed target density. In the subsequent step, PITA enables simulating the trained diffusion model to procure training samples at a lower temperature for the next diffusion model through inference-time annealing using a novel Feynman-Kac PDE combined with Sequential Monte Carlo. Empirically, PITA enables, for the first time, equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates with dramatically lower energy function evaluations. Code available at: https://github.com/taraak/pita
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
doi.org
openreview.net
Rendering-Aware Reinforcement Learning for Vector Graphics Generation
Juan A. Rodriguez
Haotian Zhang
Abhay Puri
Aarash Feizi
Rishav Pramanik
Pascal Wichmann
Arnab Mondal
Mohammad Reza Samsami
Rabiul Awal
Perouz Taslakian
Spandana Gella
Sai Rajeswar
David Vázquez
Christopher Pal
Marco Pedersoli
Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (voir plus)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF (Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Retro SynFlow: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis
Robin Yadav
Qi Yan
Guy Wolf
Avishek Joey Bose
Renjie Liao
A fundamental problem in organic chemistry is identifying and predicting the series of reactions that synthesize a desired target product mo… (voir plus)lecule. Due to the combinatorial nature of the chemical search space, single-step reactant prediction -- i.e. single-step retrosynthesis -- remains challenging even for existing state-of-the-art template-free generative approaches to produce an accurate yet diverse set of feasible reactions. In this paper, we model single-step retrosynthesis planning and introduce RETRO SYNFLOW (RSF) a discrete flow-matching framework that builds a Markov bridge between the prescribed target product molecule and the reactant molecule. In contrast to past approaches, RSF employs a reaction center identification step to produce intermediate structures known as synthons as a more informative source distribution for the discrete flow. To further enhance diversity and feasibility of generated samples, we employ Feynman-Kac steering with Sequential Monte Carlo based resampling to steer promising generations at inference using a new reward oracle that relies on a forward-synthesis model. Empirically, we demonstrate \nameshort achieves
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents
Wanxin Tian
Shijie Zhang
Kevin Zhang
Xiaowei Chi
Chun-Kai Fan
Junyu Lu
Yulin Luo
Qiang Zhou
Yiming Zhao
Ning Liu
Siyu Lin
Zhiyuan Qin
Xiaozhu Ju
Shanghang Zhang
Jian Tang
2025-09-17
NeurIPS.cc/2025/Conference (poster)
openreview.net
openreview.net