GenAI4ELab/papercli-papers huggingface dataset:
A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations
Authors: Kyra L. Kadhim; Jonas Beck; Ziwei Huang; Jakob H. Macke; Fred Rieke; Thomas Euler; Michael Deistler; Philipp Berens
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Visual processing starts in the outer retina where photoreceptors transform light into electrochemical signals. These signals are modulated by inhibition from horizontal cells and sent to the inner retina via excitatory bipolar cells. The outer retina is thought to play an important role in contrast invariant coding of visual information, but how the different cell types implement this computation together remains incompletely understood. To understand the role of each cell type, we developed a fully-differentiable biophysical model of a circular patch of mouse outer retina. The model includes 200 cone photoreceptors with a realistic phototransduction cascade and ribbon synapses as well as horizontal and bipolar cells, all with cell-type specific ion channels. Going beyond decades of work constraining biophysical models of neurons only by experimental data, we used a dual approach, constraining some parameters of the model with available measurements and others by a visual task: (1) We fit the parameters of the cone models to whole cell patch-clamp measurements of photocurrents and two-photon glutamate imaging measurements of synaptic release. (2) We then trained the spatiotemporal outer retina model with photoreceptors and the other cell types to perform a visual classification task with varying contrast and luminance levels. We found that our outer retina model could learn to solve the classification task despite contrast and luminance variance in the stimuli. Testing different cell type compositions and connectivity patterns, we found that feedback from horizontal cells did not further improve task performance beyond that of excitatory photoreceptors and bipolar cells. This is surprising given that horizontal cells are positioned to mediate communication across cones and that they add to the model's number of trainable parameters. Finally, we found that our model generalized better to out of distribution contrast levels than a linear classifier. Our work shows how the nonlinearities found in the outer retina can accomplish contrast invariant classification and teases apart the contributions of different cell types.
pLSTM: parallelizable Linear Source Transition Mark networks
Authors: Korbinian Pöppel; Richard Freinschlag; Thomas Schmied; Wei Lin; Sepp Hochreiter
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. 
  However, their structure constrains their applicability to sequences only or requires processing multi-dimensional data structures, such as images or molecular graphs, in a pre-defined sequential order. 
  In contrast, Multi-Dimensional RNNs (MDRNNs) are well suited for data with a higher level structure, like 2D grids, trees, and directed acyclic graphs (DAGs). 
  In this work, we extend the notion of multi-dimensionality to linear RNNs.
  We introduce parallelizable Linear Source Transition Mark networks (pLSTMs) using Source, Transition, and Mark gates that act on the linegraph of a general DAG.
  This enables parallelization in analogy to parallel associative scans and the chunkwise-recurrent form of sequential linear RNNs, but for DAGs.
  For regular grids (1D and 2D), like images, this scheme can be efficiently implemented using einsum operations, concatenations, and padding in logarithmic time.
  pLSTMs tackle the vanishing/exploding activation/gradient problem for long distances in DAGs via two distinct modes: a directed propagation mode (P-mode) and a diffusive distribution mode (D-mode). 
  To showcase the long-range capabilities of pLSTM, we introduce arrow-pointing extrapolation as a synthetic computer vision task that contains long-distance directional information.
  We demonstrate that pLSTMs generalize well to larger image sizes, whereas Transformers struggle to extrapolate.
  On established molecular graph and computer vision benchmarks, pLSTMs also show strong performance. The complete code is available at https://github.com/ml-jku/plstm_experiments.
Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization
Authors: Amin Heyrani Nobari; Lyle Regenwetter; Cyril Picard; Ligong Han; Faez Ahmed
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing general deployment. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64 × 64 to 256 x 256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization and provide a large-scale dataset to spur further research in generative modeling for inverse design. Code & data can be found at https://github.com/ahnobari/OptimizeAnyTopology.
RespoDiff: Dual-Module Bottleneck Transformation for Responsible & Faithful T2I Generation
Authors: Silpa Vadakkeeveetil Sreelatha; Sauradip Nag; Muhammad Awais; Serge Belongie; Anjan Dutta
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The rapid advancement of diffusion models has enabled high-fidelity and semantically rich text-to-image generation; however, ensuring fairness and safety remains an open challenge. Existing methods typically improve fairness and safety at the expense of semantic fidelity and image quality. In this work, we propose RespoDiff, a novel framework for responsible text-to-image generation that incorporates a dual-module transformation on the intermediate bottleneck representations of diffusion models. Our approach introduces two distinct learnable modules: one focused on capturing and enforcing responsible concepts, such as fairness and safety, and the other dedicated to maintaining semantic alignment with neutral prompts. To facilitate the dual learning process, we introduce a novel score-matching objective that enables effective coordination between the modules. Our method outperforms state-of-the-art methods in responsible generation by ensuring semantic alignment while optimizing both objectives without compromising image fidelity. Our approach improves responsible and semantically coherent generation by \textasciitilde20\% across diverse, unseen prompts. Moreover, it integrates seamlessly into large-scale models like SDXL, enhancing fairness and safety. The project page is available at https://vssilpa.github.io/respodiff_project_page.
Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models
Authors: Jipeng Li; Yanning Shen
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Explicit noise-level conditioning is widely regarded as essential for the effective operation of Graph Diffusion Models (GDMs). In this work, we challenge this assumption by investigating whether denoisers can implicitly infer noise levels directly from corrupted graph structures, potentially eliminating the need for explicit noise conditioning.  To this end, we develop a theoretical framework centered on Bernoulli edge-flip corruptions and extend it to encompass more complex scenarios involving coupled structure-attribute noise. Extensive empirical evaluations on both synthetic and real-world graph datasets, using models such as GDSS and DiGress, provide strong support for our theoretical findings. Notably, unconditional GDMs achieve performance comparable or superior to their conditioned counterparts, while also offering reductions in parameters (4-6%) and computation time (8-10%). Our results suggest that the high-dimensional nature of graph data itself often encodes sufficient information for the denoising process, opening avenues for simpler, more efficient GDM architectures.
SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
Authors: Yarden As; Chengrui Qu; Benjamin Unger; Dongho Kang; Max van der Hart; Laixi Shi; Stelian Coros; Adam Wierman; Andreas Krause
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable *sim-to-real gap*. Robust safe RL techniques are provably safe, however difficult to scale, while domain randomization is more practical yet prone to unsafe behaviors. We address this gap by proposing SPiDR, short for Sim-to-real via Pessimistic Domain Randomization—a scalable algorithm with provable guarantees for safe sim-to-real transfer. SPiDR uses domain randomization to incorporate the uncertainty about the sim-to-real gap into the safety constraints, making it versatile and highly compatible with existing training pipelines. Through extensive experiments on sim-to-sim benchmarks and two distinct real-world robotic platforms, we demonstrate that SPiDR effectively ensures safety despite the sim-to-real gap while maintaining strong performance.
Remasking Discrete Diffusion Models with Inference-Time Scaling
Authors: Guanghan Wang; Yair Schiff; Subham Sekhar Sahoo; Volodymyr Kuleshov
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Part of the success of diffusion models stems from their ability to perform iterative refinement, i.e., repeatedly correcting outputs during generation. However, modern masked discrete diffusion lacks this capability: when a token is generated, it cannot be updated again, even when it introduces an error. Here, we address this limitation by introducing the remasking diffusion model (ReMDM) sampler, a method that can be applied to pretrained masked diffusion models in a principled way and that is derived from a discrete diffusion model with a custom remasking backward process. Most interestingly, ReMDM endows discrete diffusion with a form of inference-time compute scaling. By increasing the number of sampling steps, ReMDM generates natural language outputs that approach the quality of autoregressive models, whereas when the computation budget is limited, ReMDM better maintains quality. ReMDM also improves sample quality of masked diffusion models for discretized images, and in scientific domains such as molecule design, ReMDM facilitates diffusion guidance and pushes the Pareto frontier of controllability relative to classical masking and uniform noise diffusion. When applied to large pretrained diffusion language models, ReMDM boosts the model’s performance on downstream tasks requiring factual knowledge grasp and reasoning ability.
Let a Neural Network be Your Invariant
Authors: Mirco Giacobbe; Daniel Kroening; Abhinandan Pal; Michael Tautschnig
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Safety verification ensures that a system avoids undesired behaviour. 
Liveness complements safety, ensuring that the system also achieves its
desired objectives.  A complete specification of functional correctness must
combine both safety and liveness.  Proving with mathematical
certainty that a system satisfies a safety property demands presenting an
appropriate inductive invariant of the system, whereas proving liveness
requires showing a measure of progress witnessed by a ranking function. 
Neural model checking has recently introduced a data-driven approach to the
formal verification of reactive systems, albeit focusing on ranking
functions and thus addressing liveness properties only.  In this paper, we
extend and generalise neural model checking to additionally encompass
inductive invariants and thus safety properties as well.  Given a system and
a linear temporal logic specification of safety and liveness, our approach
alternates a learning and a checking component towards the construction of a
provably sound neural certificate.  Our new method introduces a neural
certificate architecture that jointly represents inductive invariants as
proofs of safety, and ranking functions as proofs of liveness.  Moreover,
our new architecture is amenable to training using constraint solvers,
accelerating prior neural model checking work otherwise based on gradient
descent. We experimentally demonstrate that our method is orders of
magnitude faster than the state-of-the-art model checkers on pure liveness
and combined safety and liveness verification tasks written in
SystemVerilog, while enabling the verification of richer properties than was
previously possible for neural model checking.
Understanding the Gain from Data Filtering in Multimodal Contrastive Learning
Authors: Divyansh Pareek; Sewoong Oh; Simon Shaolei Du
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The success of modern multimodal representation learning relies on internet-scale datasets. Due to the low quality of a large fraction of raw web data, data curation has become a critical step in the training pipeline. Filtering using a trained model (i.e., teacher-based filtering) has emerged as a successful solution, leveraging a pre-trained model to compute quality scores. 
To explain the empirical success of teacher-based filtering, we characterize the performance of filtered contrastive learning under the standard bimodal data generation model. Denoting $\eta\in(0,1]$ as the fraction of data with correctly matched modalities among $n$ paired samples, we utilize a linear contrastive learning setup to show a provable benefit of data filtering: $(i)$ the error without filtering is upper and lower bounded by $\frac{1}{\eta \sqrt{n}}$, and $(ii)$ the error with teacher-based filtering is upper bounded by $\frac{1}{\sqrt{\eta n}}$ in the large $\eta$ regime, and by $\frac{1}{\sqrt{n}}$ in the small $\eta$ regime.
An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination
Authors: Sukanya Patra; Souhaib Ben Taieb
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Unsupervised anomaly detection (AD) methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies, leading to significant performance degradation. Existing solutions require access to the training pipelines, data or prior knowledge of the proportions of anomalies in the data, limiting their real-world applicability. To address this challenge, we propose EPHAD, a simple yet effective test-time adaptation framework that updates the outputs of AD models trained on contaminated datasets using evidence gathered at test time. Our approach integrates the prior knowledge captured by the AD model trained on contaminated datasets with evidence derived from multimodal foundation models like Contrastive Language-Image Pre-training (CLIP), classical AD methods like the Local Outlier Factor or domain-specific knowledge. We illustrate the intuition behind EPHAD using a synthetic toy example and validate its effectiveness through comprehensive experiments across eight visual AD datasets, twenty-six tabular AD datasets, and a real-world industrial AD dataset. Additionally, we conduct an ablation study to analyse hyperparameter influence and robustness to varying contamination levels, demonstrating the versatility and robustness of EPHAD across diverse AD models and evidence pairs. To ensure reproducibility, our code is publicly available at https://github.com/sukanyapatra1997/EPHAD.
Online Optimization for Offline Safe Reinforcement Learning
Authors: Yassine Chemingui; Aryan Deshwal; Alan Fern; Thanh Nguyen-Tang; Jana Doppa
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We study the problem of Offline Safe Reinforcement Learning (OSRL), where the goal is to learn a reward-maximizing policy from fixed data under a cumulative cost constraint. We propose a novel OSRL approach that frames the problem as a minimax objective and solves it by combining offline RL with online optimization algorithms. We prove the approximate optimality of this approach when integrated with an approximate offline RL oracle and no-regret online optimization. We also present a practical approximation that can be combined with any offline RL algorithm, eliminating the need for offline policy evaluation. Empirical results on the DSRL benchmark demonstrate that our method reliably enforces safety constraints under stringent cost budgets, while achieving high rewards. The code is available at https://github.com/yassineCh/O3SRL.
Global Minimizers of $\ell^p$-Regularized Objectives Yield the Sparsest ReLU Neural Networks
Authors: Julia B Nakhleh; Robert D Nowak
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Overparameterized neural networks can interpolate a given dataset in many different ways, prompting the fundamental question: which among these solutions should we prefer, and what explicit regularization strategies will provably yield these solutions? This paper addresses the challenge of finding the sparsest interpolating ReLU network—i.e., the network with the fewest nonzero parameters or neurons—a goal with wide-ranging implications for efficiency, generalization, interpretability, theory, and model compression. Unlike post hoc pruning approaches, we propose a continuous, almost-everywhere differentiable training objective whose global minima are guaranteed to correspond to the sparsest single-hidden-layer ReLU networks that fit the data. This result marks a conceptual advance: it recasts the combinatorial problem of sparse interpolation as a smooth optimization task, potentially enabling the use of gradient-based training methods. Our objective is based on minimizing $\ell^p$ quasinorms of the weights for $0 < p < 1$, a classical sparsity-promoting strategy in finite-dimensional settings. However, applying these ideas to neural networks presents new challenges: the function class is infinite-dimensional, and the weights are learned using a highly nonconvex objective. We prove that, under our formulation, global minimizers correspond exactly to sparsest solutions.  Our work lays a foundation for understanding when and how continuous sparsity-inducing objectives can be leveraged to recover sparse networks through training.
How to Train Your LLM Web Agent: A Statistical Diagnosis
Authors: Dheeraj Vattikonda; Santhoshi Ravichandran; Emiliano Penaloza; Hadi Nekoei; Thibault Le Sellier de Chezelles; Megh Thakkar; Nicolas Gontier; Miguel Muñoz-Mármol; Sahar Omidi Shayegan; Stefania Raimondo; Xue Liu; Alexandre Drouin; Alexandre Piché; Alexandre Lacoste; Massimo Caccia
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large language model (LLM) agents for web interfaces have advanced rapidly, yet open-source systems still lag behind proprietary agents. Bridging this gap is key to enabling customizable, efficient, and privacy-preserving agents. Two challenges hinder progress: the reproducibility issues in RL and LLM agent training, where results often depend on sensitive factors like seeds and decoding parameters, and the focus of prior work on single-step tasks, overlooking the complexities of web-based, multi-step decision-making.

We address these gaps by providing a statistically driven study of training LLM agents for web tasks. Our two-stage pipeline combines imitation learning from a Llama 3.3 70B teacher with on-policy fine-tuning via Group Relative Policy Optimization (GRPO) on a Llama 3.1 8B student. Through 240 configuration sweeps and rigorous bootstrapping, we chart the first compute allocation curve for open-source LLM web agents. Our findings show that dedicating one-third of compute to teacher traces and the rest to RL improves MiniWoB++ success by 6 points and closes 60\% of the gap to GPT-4o on WorkArena, while cutting GPU costs by 45\%. We introduce a principled hyperparameter sensitivity analysis, offering actionable guidelines for robust and cost-effective agent training.
Activation-Informed Merging of Large Language Models
Authors: Amin Heyrani Nobari; Kaveh Alim; Ali ArjomandBigdeli; Akash Srivastava; Faez Ahmed; Navid Azizan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance across various tasks while maintaining computational efficiency. This paper introduces Activation-Informed Merging (AIM), a technique that integrates the information from the activation space of LLMs into the merging process to improve performance and robustness. AIM is designed as a flexible, complementary solution that is applicable to any existing merging method. It aims to preserve critical weights from the base model, drawing on principles from continual learning (CL) and model compression. Utilizing a task-agnostic calibration set, AIM selectively prioritizes essential weights during merging. We empirically demonstrate that AIM significantly enhances the performance of merged models across multiple benchmarks. Our findings suggest that considering the activation-space information can provide substantial advancements in the model merging strategies for LLMs with up to 40% increase in benchmark performance. Our code is publicly available at https://github.com/ahnobari/ActivationInformedMerging
Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
Authors: Remco F. Leijenaar; Hamidreza Kasaei
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Learning semantically meaningful representations from unstructured 3D point clouds remains a central challenge in computer vision, especially in the absence of large-scale labeled datasets. While masked point modeling (MPM) is widely used in self-supervised 3D learning, its reconstruction-based objective can limit its ability to capture high-level semantics. We propose AsymDSD, an Asymmetric Dual Self-Distillation framework that unifies masked modeling and invariance learning through prediction in the latent space rather than the input space. AsymDSD builds on a joint embedding architecture and introduces several key design choices: an efficient asymmetric setup, disabling attention between masked queries to prevent shape leakage, multi-mask sampling, and a point cloud adaptation of multi-crop. AsymDSD achieves state-of-the-art results on ScanObjectNN (90.53\%) and further improves to 93.72\% when pretrained on 930k shapes, surpassing prior methods.