Weekly TMLR digest for Nov 16, 2025

[TMLR]

New certifications
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J2C Certification: Learning Deformable Body Interactions With Adaptive Spatial Tokenization

Hao Wang, Yu Liu, Daniel Biggs, Haoru Wang, Jiandong Yu, Ping Huang

https://openreview.net/forum?id=qBOEHzkr1P

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Accepted papers
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Title: RIZE: Adaptive Regularization for Imitation Learning

Authors: Adib Karimi, Mohammad Mehdi Ebadzadeh

Abstract: We propose a novel Inverse Reinforcement Learning (IRL) method that mitigates the rigidity of fixed reward structures and the limited flexibility of implicit reward regularization. Building on the Maximum Entropy IRL framework, our approach incorporates a squared temporal-difference (TD) regularizer with adaptive targets that evolve dynamically during training, thereby imposing adaptive bounds on recovered rewards and promoting robust decision-making. To capture richer return information, we integrate distributional RL into the learning process. Empirically, our method achieves expert-level performance on complex MuJoCo and Adroit environments, surpassing baseline methods on the Humanoid-v2 task with limited expert demonstrations. Extensive experiments and ablation studies further validate the effectiveness of the approach and provide insights into reward dynamics in imitation learning. Our source code is available at https://github.com/adibka/RIZE.

URL: https://openreview.net/forum?id=a6DWqXJZCZ

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Title: Self-Supervised Learning on Molecular Graphs: A Systematic Investigation of Masking Design

Authors: Jiannan Yang, Veronika Thost, Tengfei Ma

Abstract: Self-supervised learning (SSL) plays a central role in molecular representation learning. Yet, many recent innovations in masking-based pretraining are introduced as heuristics and lack principled evaluation, obscuring which design choices are genuinely effective. This work cast the entire pretrain–finetune workflow into a unified probabilistic framework, enabling a transparent comparison and deeper understanding of masking strategies. Building on this formalism, we conduct a controlled study of three core design dimensions: masking distribution, prediction target, and encoder architecture, under rigorously controlled settings. We further employ information-theoretic measures to assess the informativeness of pretraining signals and connect them to empirically benchmarked downstream performance. Our findings reveal a surprising insight: sophisticated masking distributions offer no consistent benefit over uniform sampling for common node-level prediction tasks. Instead, the choice of prediction target and its synergy with the encoder architecture are far more critical. Specifically, shifting to semantically richer targets yields substantial downstream improvements, particularly when paired with expressive Graph Transformer encoders. These insights offer practical guidance for developing more effective SSL methods for molecular graphs.

URL: https://openreview.net/forum?id=TE4vcYWRcc

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Title: ComFe: An Interpretable Head for Vision Transformers

Authors: Evelyn Mannix, Liam Hodgkinson, Howard Bondell

Abstract: Interpretable computer vision models explain their classifications through comparing the distances between the local embeddings of an image and a set of prototypes that represent the training data. However, these approaches introduce additional hyper-parameters that need to be tuned to apply to new datasets, scale poorly, and are more computationally intensive to train in comparison to black-box approaches. In this work, we introduce Component Features (ComFe), a highly scalable interpretable-by-design image classification head for pretrained Vision Transformers (ViTs) that can obtain competitive performance in comparison to comparable non-interpretable methods. To our knowledge, ComFe is the first interpretable head and unlike other interpretable approaches can be readily applied to large-scale datasets such as ImageNet-1K. Additionally, ComFe provides improved robustness and outperforms previous interpretable approaches on key benchmark datasets while using a consistent set of hyperparameters and without finetuning the pretrained ViT backbone. With only global image labels and no segmentation or part annotations, ComFe can identify consistent component features within an image and determine which of these features are informative in making a prediction. Code is available at github.com/emannix/comfe-component-features.

URL: https://openreview.net/forum?id=cI4wrDYFqE

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Title: Preserving Angles Improves Feature Distillation

Authors: Evelyn Mannix, Liam Hodgkinson, Howard Bondell

Abstract: Knowledge distillation methods compress models by training a student network using the classification outputs of a high quality teacher model, but can fail to effectively transfer the properties of computer vision foundation models from the teacher to the student. While it has been recently shown that feature distillation—where a teacher model's output features are replicated instead—can reproduce performance for foundation models across numerous downstream tasks, they fall short in matching critical properties such as robustness and out-of-distribution (OOD) detection performance. This paper overcomes this shortcoming by introducing Cosine-similarity Preserving Compression (CosPress), a feature distillation technique that learns a mapping to compress the latent space of the teacher model into the smaller latent space of the student, by preserving the cosine similarities between image embeddings. This enables direct optimisation of the student network and produces a more faithful reproduction of the teacher's properties. It is shown that distillation with CosPress on a variety of datasets, including ImageNet, produces more accurate models with greater performance on generalisability, robustness and OOD detection benchmarks, and that this technique provides a competitive pathway for training highly performant lightweight models on small datasets. Code is available at github.com/emannix/cospress.

URL: https://openreview.net/forum?id=ZEhgODZkWU

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Title: MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation

Authors: Sayantan Adak, Somnath Banerjee, Rajarshi Mandal, Avik Halder, Sayan Layek, Rima Hazra, Animesh Mukherjee

Abstract: Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems “especially those relying on explicit text” frequently fail to recognize such subtle or implicit harm. We introduce MemeSense, an adaptive framework designed to generate socially grounded interventions for harmful memes by combining visual and textual understanding with curated, semantically aligned examples enriched with commonsense cues. This enables the model to detect nuanced complexed threats like misogyny, stereotyping, or vulgarity “even in memes lacking overt language”. Across multiple benchmark datasets, MemeSense outperforms state-of-the-art methods, achieving up to 35% higher semantic similarity
and 9% improvement in BERTScore for non-textual memes, and notable gains for text-rich memes as well. These results highlight MemeSense as a promising step toward safer, more context-aware AI systems for real-world content moderation. The code and data are available at: https://github.com/sayantan11995/MemeSense

URL: https://openreview.net/forum?id=ahRqI3NBiq

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Title: Gradient GA: Gradient Genetic Algorithm For Drug Molecular Design

Authors: Debadyuti Mukherjee, Chris Zhuang, Yingzhou Lu, Tianfan Fu, Ruqi Zhang

Abstract: Molecular discovery has brought great benefit to the chemical industry. Various molecu-
lar design techniques have been developed to identify molecules with desirable properties.
Traditional optimization methods, such as genetic algorithms, continue to achieve state-of-
the-art results across various molecular design benchmarks. However, these techniques rely
solely on undirected random exploration, which hinders both the quality of the final solution
and the convergence speed. To address this limitation, we propose a novel approach called
Gradient Genetic Algorithm (Gradient GA), which incorporates gradient information from
the objective function into genetic algorithms. Instead of random exploration, each proposed
sample iteratively progresses toward an optimal solution by following the gradient direction.
We achieve this by designing a differentiable objective function parameterized by a neural
network and utilizing the Discrete Langevin Proposal to enable gradient guidance in discrete
molecular spaces. Experimental results demonstrate that our method significantly improves
both convergence speed and solution quality, outperforming cutting-edge techniques. The
proposed method has shown up to a 25% improvement in the Top 10 score over the vanilla ge-
netic algorithm. The code is available at https://github.com/debadyuti23/GradientGA.

URL: https://openreview.net/forum?id=kFKcktAeEG

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Title: Schauder Bases for $C[0, 1]$ Using ReLU, Softplus and Two Sigmoidal Functions

Authors: Anand Ganesh, Babhrubahan Bose, Anand Rajagopalan

Abstract: We construct four Schauder bases for the space $C[0,1]$, one using ReLU functions, another using Softplus functions, and two more using sigmoidal versions of the ReLU and Softplus functions. This establishes the existence of a basis using these functions for the first time, and improves on the universal approximation property associated with them. We also show an $O(\frac{1}{n})$ approximation bound based on our ReLU basis, and a negative result on constructing multivariate functions using finite combinations of ReLU functions.

URL: https://openreview.net/forum?id=YT79Qu1bOi

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Title: UMP-Net: Uncertainty-Aware Mixture of Prompts Network for Efficient Instruction Tuning

Authors: Fatemeh Daneshfar, Abdulhady abas, Moloud Abdar, Pietro Lio

Abstract: Instruction tuning has greatly improved how large language models (LLMs) respond to human-like instructions. However, fully fine-tuning these models is still computationally demanding, and many existing parameter-efficient methods fall short, particularly when it comes to uncertainty estimation and working effectively across different modalities. To address this, we introduce UMP-Net (Uncertainty-Aware Mixture of Prompts Network), a new approach designed to enhance the ability of LLaMA to follow instructions.
UMP-Net combines a novel mixture of prompts (MoPs) technique with Latent Noise Prompting, KNN-based Heterogeneous Clustering, and Conformal Predictions to select the most reliable prompts dynamically while accounting for uncertainty. In addition, it features a CLIP-based multi-modal architecture to streamline vision-language integration. We evaluated UMP-Net on a range of benchmarks including ScienceQA, COCO Caption, and various zero-shot multi-modal tasks. The results show a strong performance: an average accuracy of 88.41% on ScienceQA and a CIDEr score of 158.3 on COCO Caption, surpassing models such as LLaVA, LLaMA-Adapter, and LLaMA-Excitor. These findings suggest that UMP-Net offers both improved multi-modal capability and computational efficiency. Further ablations demonstrate UMP-Net’s conformal prediction module provides robust uncertainty estimates under noise and domain shifts, outperforming Bayesian alternatives in coverage guarantees with minimal overhead.

URL: https://openreview.net/forum?id=EehtvgNXAl

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Title: Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning

Authors: Zheng Zhang

Abstract: Large Language Models (LLMs) display striking surface fluency yet systematically fail at tasks requiring symbolic reasoning, arithmetic accuracy, and logical consistency. This paper offers a structural diagnosis of such failures, revealing a persistent gap between \textit{comprehension} and \textit{competence}. Through controlled experiments and architectural analysis, we demonstrate that LLMs often articulate correct principles without reliably applying them—a failure rooted not in knowledge access, but in computational execution. We term this phenomenon the computational \textit{split-brain syndrome}, where instruction and action pathways are geometrically and functionally dissociated. This core limitation recurs across domains, from mathematical operations to relational inferences, and explains why model behavior remains brittle even under idealized prompting. We argue that LLMs function as powerful pattern completion engines, but lack the architectural scaffolding for principled, compositional reasoning. Our findings delineate the boundary of current LLM capabilities and motivate future models with metacognitive control, principle lifting, and structurally grounded execution. This diagnosis also clarifies why mechanistic interpretability findings may reflect training-specific pattern coordination rather than universal computational principles, and why the geometric separation between instruction and execution pathways suggests limitations in neural introspection and mechanistic analysis.

URL: https://openreview.net/forum?id=Gz5HMiJLqv

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Title: Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data

Authors: Keyan Chen, Yile Li, Da Long, Zhitong Xu, WEI W. XING, Jacob Hochhalter, Shandian Zhe

Abstract: Neural operators have shown great potential in surrogate modeling. However, training a well-performing neural operator typically requires a substantial amount of data, which can pose a major challenge in complex applications. In such scenarios, detailed physical knowledge can be unavailable or difficult to obtain, and collecting extensive data is often prohibitively expensive. To mitigate this challenge, we propose the Pseudo Physics-Informed Neural Operator (PPI-NO) framework. PPI-NO constructs a surrogate physics system for the target system using partial differential equations (PDEs) derived from simple, rudimentary physics principles, such as basic differential operators.
This surrogate system is coupled with a neural operator model, using an alternating update and learning process to iteratively enhance the model's predictive power.
While the physics derived via PPI-NO may not mirror the ground-truth underlying physical laws --- hence the term ``pseudo physics'' --- this approach significantly improves the accuracy of standard operator learning models in data-scarce scenarios, which is evidenced by extensive evaluations across five benchmark tasks and a fatigue modeling application.

URL: https://openreview.net/forum?id=5N1V25Rf7D

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Title: Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition

Authors: Barproda Halder, Faisal Hamman, Pasan Dissanayake, Qiuyi Zhang, Ilia Sucholutsky, Sanghamitra Dutta

Abstract: Spuriousness arises when there is an association between two or more variables in a dataset that are not causally related. In this work, we propose an explainability framework to preemptively disentangle the nature of such spurious associations in a dataset before model training. We leverage a body of work in information theory called Partial Information Decomposition (PID) to decompose the total information about the target into four non-negative quantities, namely unique information (in core and spurious features, respectively), redundant information, and synergistic information. Our framework helps anticipate when the core or spurious feature is indispensable, when either suffices, and when both are jointly needed for an optimal classifier trained on the dataset. Next, we leverage this decomposition to propose a novel measure of the spuriousness of a dataset. We arrive at this measure systematically by examining several candidate measures, and demonstrating what they capture and miss through intuitive canonical examples and counterexamples. Our framework Spurious Disentangler consists of segmentation, dimensionality reduction, and estimation modules, with capabilities to specifically handle high-dimensional image data efficiently. Finally, we also perform empirical evaluation to demonstrate the trends of unique, redundant, and synergistic information, as well as our proposed spuriousness measure across $6$ benchmark datasets under various experimental settings. We observe an agreement between our preemptive measure of dataset spuriousness and post-training model generalization metrics such as worst-group accuracy, further supporting our proposition. The code is available at https://github.com/Barproda/spuriousness-disentangler.

URL: https://openreview.net/forum?id=zw6UAPYmyx

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Title: Learning Deformable Body Interactions With Adaptive Spatial Tokenization

Authors: Hao Wang, Yu Liu, Daniel Biggs, Haoru Wang, Jiandong Yu, Ping Huang

Abstract: Simulating interactions between deformable bodies is vital in fields like material science, mechanical design, and robotics. While learning-based methods with Graph Neural Networks (GNNs) are effective at solving complex physical systems, they encounter scalability issues when modeling deformable body interactions. To model interactions between objects, pairwise global edges have to be created dynamically, which is computationally intensive and impractical for large-scale meshes. To overcome these challenges, drawing on insights from geometric representations, we propose an Adaptive Spatial Tokenization (AST) method for efficient representation of physical states. By dividing the simulation space into a grid of cells and mapping unstructured meshes onto this structured grid, our approach naturally groups adjacent mesh nodes. We then apply a cross-attention module to map the sparse cells into a compact, fixed-length embedding, serving as tokens for the entire physical state. Self-attention modules are employed to predict the next state over these tokens in latent space. This framework leverages the efficiency of tokenization and the expressive power of attention mechanisms to achieve accurate and scalable simulation results. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches in modeling deformable body interactions. Notably, it remains effective on large-scale simulations with meshes exceeding 100,000 nodes, where existing methods are hindered by computational limitations. Additionally, we contribute a novel large-scale dataset encompassing a wide range of deformable body interactions to support future research in this area.

URL: https://openreview.net/forum?id=qBOEHzkr1P

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Title: Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers

Authors: Dylan Bouchard, Mohit Singh Chauhan

Abstract: Hallucinations are a persistent problem with Large Language Models (LLMs). As these models become increasingly used in high-stakes domains, such as healthcare and finance, the need for effective hallucination detection is crucial. To this end, we outline a versatile framework for closed-book hallucination detection that practitioners can apply to real-world use cases. To achieve this, we adapt a variety of existing uncertainty quantification (UQ) techniques, including black-box UQ, white-box UQ, and LLM-as-a-Judge, transforming them as necessary into standardized response-level confidence scores ranging from 0 to 1. To enhance flexibility, we propose a tunable ensemble approach that incorporates any combination of the individual confidence scores. This approach enables practitioners to optimize the ensemble for a specific use case for improved performance. To streamline implementation, the full suite of scorers is offered in this paper's companion Python toolkit, \texttt{uqlm}. To evaluate the performance of the various scorers, we conduct an extensive set of experiments using several LLM question-answering benchmarks. We find that our tunable ensemble typically surpasses its individual components and outperforms existing hallucination detection methods. Our results demonstrate the benefits of customized hallucination detection strategies for improving the accuracy and reliability of LLMs.

URL: https://openreview.net/forum?id=WOFspd4lq5

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Title: The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance

Authors: Anwesha Mohanty, Venkatesh Balavadhani Parthasarathy, Arsalan Shahid

Abstract: Multimodal Large Language Models (MLLMs) are set to transform how machines process and generate human-like responses by integrating diverse modalities such as text, images, and code. In this study, we specifically focus on text–image multimodal reasoning
and understanding, evaluating their performance across diverse task categories. Yet, effectively harnessing their capabilities hinges on optimal prompt engineering. We present a comprehensive experimental evaluation of seven prompt engineering methods applied to 13 open-source MLLMs over 24 tasks spanning Reasoning and Compositionality, Multimodal Understanding and Alignment, Complex Code Generation and Execution, and Knowledge Retrieval and Integration. Our approach stratifies models by parameter count into Small
(< 4B), Medium (4B–10B), and Large (> 10B) categories and compares prompting techniques including Zero-Shot, One-Shot, Few-Shot, Chain-of-Thought, Analogical, Generated Knowledge, and Tree-of-Thought. Our experiments reveal that while Large MLLMs excel in structured tasks such as code generation and execution, achieving accuracies as high as 96.88% under Few-Shot prompting. In multimodal understanding and alignment (with relevance scores reaching 100% using Zero-Shot prompting), all models struggle with complex reasoning and abstract model understanding, often yielding accuracies below 60% and high hallucination rates. Notably, structured reasoning prompts (Chain-of-Thought, Analogical, Generated Knowledge and Tree-of-Thought) frequently increased hallucination up to 75% in small models and led to longer response times (exceeding 20 seconds in Large MLLMs), while simpler prompting methods (One-Shot and Few-Shot) provided more concise and efficient outputs. Our findings underscore that no single prompting method uniformly optimizes all task types. Instead, adaptive prompting strategies that combine the strengths of example-based guidance with selective structured reasoning are essential to enhance robustness, efficiency, and factual accuracy in MLLMs. Our work provides critical insights and actionable recommendations for optimizing prompt engineering in text–image multimodal contexts, paving the way for more reliable deployment of MLLMs in real-world applications ranging from AI-assisted coding and knowledge retrieval to visual–textual content understanding.

URL: https://openreview.net/forum?id=B1L8HrjoA1

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Title: An Unconditional Representation of the Conditional Score in Infinite Dimensional Linear Inverse Problems

Authors: Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop, Tapio Helin

Abstract: Score-based diffusion models (SDMs) have emerged as a powerful tool for sampling from the posterior distribution in Bayesian inverse problems. However, existing methods often require multiple evaluations of the forward mapping to generate a single sample, resulting in significant computational costs for large-scale inverse problems. To address this, we propose an unconditional representation of the conditional score-function (UCoS) tailored to linear inverse problems, which avoids forward model evaluations during sampling by shifting computational effort to an offline training phase. In this phase, a task-dependent score function is learned based on the linear forward operator. Crucially, we show that the conditional score can be derived exactly from a trained (unconditional) score using affine transformations, eliminating the need for conditional score approximations. Our approach is formulated in infinite-dimensional function spaces, making it inherently discretization-invariant. We support this formulation with a rigorous convergence analysis that justifies UCoS beyond any specific discretization. Finally we validate UCoS through high-dimensional computed tomography (CT) and image deblurring experiments, demonstrating both scalability and accuracy.

URL: https://openreview.net/forum?id=rO8erhXHPo

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Title: Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction

Authors: Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong

Abstract: We consider a distributed time series forecasting problem where multiple distributed nodes each observing a local time series (of potentially different modality) collaborate to make both local and global forecasts. This problem is particularly challenging because each node only observes time series generated from a subset of sources, making it challenging to utilize correlations among different streams for accurate forecasting; and the data streams observed at each node may represent different modalities, leading to heterogeneous computational requirements among nodes. To tackle these challenges, we propose a hierarchical learning framework, consisting of multiple local models and a global model, and provide a suite of efficient training algorithms to achieve high local and global forecasting accuracy. We theoretically establish the convergence of the proposed framework and demonstrate the effectiveness of the proposed approach using several time series forecasting tasks, with the (somewhat surprising) observation that the proposed distributed models can match, or even outperform centralized ones.

URL: https://openreview.net/forum?id=fFKWs9HslJ

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Title: Learning Is a Kan Extension

Authors: Matthew Pugh, Nick Harris, Corina Cirstea, Jo Grundy

Abstract: Previous work has demonstrated that efficient algorithms exist for computing Kan extensions and that some Kan extensions have interesting similarities to various machine learning algorithms. This paper closes the gap by proving that all error minimisation algorithms may be presented as a Kan extension. This result provides a foundation for future work to investigate the optimisation of machine learning algorithms through their presentation as Kan extensions. A corollary of this representation of error-minimising algorithms is a presentation of error from the perspective of lossy and lossless transformations of data.

URL: https://openreview.net/forum?id=xWKtKdeefL

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Title: Monotone Missing Data: A Blessing and a Curse

Authors: Santtu Tikka, Juha Karvanen

Abstract: Monotone missingness is commonly encountered in practice when a missing measurement compels another measurement to be missing. Because of the simpler missing data pattern, monotone missing data is often viewed as beneficial from the perspective of practical data analysis. However, in graphical missing data models, monotonicity has implications for the identifiability of the full law, i.e., the joint distribution of actual variables and response indicators. In the general nonmonotone case, the full law is known to be nonparametrically identifiable if and only if specific graphical structures are not present. We show that while monotonicity may enable the identification of the full law despite some of these structures, it also prevents the identification in certain cases that are identifiable without monotonicity. The results emphasize the importance of proper treatment of monotone missingness in the analysis of incomplete data.

URL: https://openreview.net/forum?id=kVthdlAVks

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Title: RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation

Authors: Matteo El-Hariry, Antoine Richard, Ricard Marsal, Luis Felipe Wolf Batista, Matthieu Geist, Cédric Pradalier, Miguel Olivares-Mendez

Abstract: Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing frameworks and benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments. Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real- world experiments with multiple robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle; (3) the release of the first open-source API for deploying IsaacLab-trained policies to real robots, enabling lightweight inference and rapid field validation; and (4) uniform tasks and metrics for cross-medium evaluation, through a unified evaluation testbed to assess performance of navigation tasks in diverse operational conditions (aquatic, terrestrial and space). By ensuring consistency between simulation and real-world deployment, RoboRAN lowers the barrier to developing adaptable RL-based navigation strategies. Its modular design enables straightforward integration of new robots and tasks through predefined templates, fostering reproducibility and extension to diverse domains. To support the community, we release RoboRAN as open-source.

URL: https://openreview.net/forum?id=0wDbhLeMj9

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Title: Phase-driven Generalizable Representation Learning for Nonstationary Time Series Classification

Authors: Payal Mohapatra, Lixu Wang, Qi Zhu

Abstract: Pattern recognition is a fundamental task in continuous sensing applications, but real-world scenarios often experience distribution shifts that necessitate learning generalizable representations for such tasks. This challenge is exacerbated with time-series data, which also exhibit inherent nonstationarity—variations in statistical and spectral properties over time. In this work, we offer a fresh perspective on learning generalizable representations for time-series classification by considering the phase information of a signal as an approximate proxy for nonstationarity and propose a phase-driven generalizable representation learning framework for time-series classification, PhASER. It consists of three key elements: 1) Hilbert transform-based augmentation, which diversifies nonstationarity while preserving task-specific discriminatory semantics, 2) separate magnitude-phase encoding, viewing time-varying magnitude and phase as independent modalities, and 3) phase-residual feature broadcasting, integrating 2D phase features with a residual connection to the 1D signal representation, providing inherent regularization to improve distribution-invariant learning. Extensive evaluations on five datasets from sleep-stage classification, human activity recognition, and gesture recognition against 13 state-of-the-art baseline methods demonstrate that PhASER consistently outperforms the best baselines by an average of 5% and up to 11% in some cases. Additionally, the principles of PhASER can be broadly applied to enhance the generalizability of existing time-series representation learning models.

URL: https://openreview.net/forum?id=cb3nwoqLdd

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Title: Dataset Condensation with Color Compensation

Authors: Huyu Wu, Duo Su, Junjie Hou, Guang Li

Abstract: Dataset condensation always faces a constitutive trade-off: balancing performance and fidelity under extreme compression. Existing methods struggle with two bottlenecks: image-level selection methods (Coreset Selection, Dataset Quantization) suffer from inefficiency in condensation, while pixel-level optimization (Dataset Distillation) introduces semantic distortion due to over-parameterization. With empirical observations, we find that a critical problem in dataset condensation is the oversight of color's dual role as an information carrier and a basic semantic representation unit. We argue that improving the colorfulness of condensed images is beneficial for representation learning. Motivated by this, we propose DC3: a Dataset Condensation framework with Color Compensation. After a calibrated selection strategy, DC3 utilizes the latent diffusion model to enhance the color diversity of an image rather than creating a brand-new one. Extensive experiments demonstrate the superior performance and generalization of DC3 that outperforms SOTA methods across multiple benchmarks. To the best of our knowledge, besides focusing on downstream tasks, DC3 is the first research to fine-tune pre-trained diffusion models with condensed datasets. The Frechet Inception Distance (FID) and Inception Score (IS) results prove that training networks with our high-quality datasets is feasible without model collapse or other degradation issues.

URL: https://openreview.net/forum?id=hIdwvIOiJt

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Title: Adaptive Group Robust Ensemble Knowledge Distillation

Authors: Patrik Kenfack, Ulrich Aïvodji, Samira Ebrahimi Kahou

Abstract: Neural networks can learn spurious correlations in the data, often leading to performance degradation for underrepresented subgroups. Studies have demonstrated that the disparity is amplified when knowledge is distilled from a complex teacher model to a relatively ``simple'' student model. Prior work has shown that ensemble deep learning methods can improve the performance of the worst-case subgroups; however, it is unclear if this advantage carries over when distilling knowledge from an ensemble of teachers, especially when the teacher models are debiased. This study demonstrates that traditional ensemble knowledge distillation can significantly drop the performance of the worst-case subgroups in the distilled student model even when the teacher models are debiased. To overcome this, we propose Adaptive Group Robust Ensemble Knowledge Distillation (\AGREKD), a simple ensembling strategy to ensure that the student model receives knowledge beneficial for unknown underrepresented subgroups. Leveraging an additional biased model, our method selectively chooses teachers whose knowledge would better improve the worst-performing subgroups by upweighting the teachers with gradient directions deviating from the biased model. Our experiments on several datasets demonstrate the superiority of the proposed ensemble distillation technique and show that it can even outperform classic model ensembles based on majority voting. Our source code is available at https://github.com/patrikken/AGRE-KD.

URL: https://openreview.net/forum?id=G2BEBaKd8Y

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Title: Two-Step Offline Preference-Based Reinforcement Learning on Explicitly Constrained Policies

Authors: Yinglun Xu, Tarun Suresh, Rohan Gumaste, David Zhu, Ruirui Li, Zhengyang Wang, Haoming Jiang, Xianfeng Tang, Qingyu Yin, Monica Xiao Cheng, Qi Zeng, Chao Zhang, Gagandeep Singh

Abstract: Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework that learns a reward model from an offline dataset first and then optimizes a policy over the learned reward model through online reinforcement learning has been widely adopted. However, such a method faces challenges from the risk of reward hacking and the complexity of reinforcement learning. To overcome the challenge, our insight is that both challenges come from the state-actions not supported in the dataset. Such state actions are unreliable and increase the complexity of the reinforcement learning problem. Based on the insight, we develop a novel two-step learning method called PRC: preference-based reinforcement learning on explicitly constrained policies. The high-level idea is to limit the reinforcement learning agent to optimize over policies supported on an explicitly constrained action space that excludes the out-of-distribution state-actions. We empirically verify that our method has high learning efficiency on various datasets in robotic control environments.

URL: https://openreview.net/forum?id=LxPg5GJuY3

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Title: An Evolutionary Algorithm for Black-Box Adversarial Attack Against Explainable Methods

Authors: Phoenix Neale Williams, Jessica Schrouff, Lea Goetz

Abstract: The explainability of deep neural networks (DNNs) remains a major challenge in developing trustworthy AI, particularly in high-stakes domains such as medical imaging. Although explainable AI (XAI) techniques have advanced, they remain vulnerable to adversarial perturbations, underscoring the need for more robust evaluation frameworks. Existing adversarial attacks often focus on specific explanation strategies, while recent research has introduced black-box attacks capable of targeting multiple XAI methods. However, these approaches typically craft pixel-level perturbations that require a large number of queries and struggle to effectively attack less granular XAI methods such as Grad-CAM and LIME. To overcome these limitations, we propose a novel attack that generates perturbations using semi-transparent, RGB-valued circles optimized via an evolutionary strategy. This design reduces the number of tunable parameters, improves attack efficiency, and is adaptable to XAI methods with varying levels of granularity. Extensive experiments on medical and natural image datasets demonstrate that our method outperforms state-of-the-art techniques, exposing critical vulnerabilities in current XAI systems and highlighting the need for more robust interpretability frameworks.

URL: https://openreview.net/forum?id=MlUP5Euj6S

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Title: Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data

Authors: Mouad El Bouchattaoui, Myriam Tami, BENOIT LEPETIT, Paul-Henry Cournède

Abstract: Accurately estimating treatment effects over time is crucial in fields such as precision medicine, epidemiology, economics, and marketing. Many current methods for estimating treatment effects over time assume that all confounders are observed or attempt to infer unobserved ones. In contrast, our approach focuses on unobserved adjustment variables—variables that specifically have a causal effect on the outcome sequence. Under the assumption of unconfoundedness, we address estimating Conditional Average Treatment Effects (CATEs) while accounting for unobserved heterogeneity in response to treatment due to these unobserved adjustment variables. Our proposed Causal Dynamic Variational Autoencoder (CDVAE) is grounded in theoretical guarantees concerning the validity of latent adjustment variables and generalization bounds on CATEs estimation error. Extensive evaluations on synthetic and real-world datasets show that CDVAE outperforms existing baselines. Moreover, we demonstrate that state-of-the-art models significantly improve their CATE estimates when augmented with the latent substitutes learned by CDVAE—approaching oracle-level performance without direct access to the true adjustment variables.

URL: https://openreview.net/forum?id=atf9q49DeF

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New submissions
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Title: BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions

Abstract: Efficiently solving real-world problems with LLMs increasingly hinges on their ability to interact with dynamic web environments and autonomously acquire external information. While recent research like Search-R1 and WebDancer demonstrates strong performance in solving web tasks, they heavily rely on additional tools to convert the interactive web environment into static text content. This is in contrast to human browsing behaviors, which involve diverse interactions with the browser, such as scrolling, clicking, and typing. In this paper, we propose BrowserAgent, a more interactive agent that solves complex tasks through human-inspired browser actions. BrowserAgent operates directly on raw web pages via Playwright through a set of predefined browser actions. We adopt a two-stage training (Supervised Fine-Tuning (SFT) and Rejection Fine-Tuning (RFT)) to improve the model's generalization abilities. Despite using significantly less training data than Search-R1, BrowserAgent achieves more competitive results across different Open-QA tasks. Additionally, we introduce an explicit memory mechanism to store key conclusions across steps, further enhancing the model's reasoning capabilities for long-horizon tasks. Notably, BrowserAgent-7B can achieve around 20\% improvement over Search-R1 on multi-hop QA tasks like HotpotQA, 2Wiki, and Bamboogle. These results indicate that BrowserAgent can serve as a more advanced framework for more interactive and scalable web agents.

URL: https://openreview.net/forum?id=X4CfZPSEHE

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Title: A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Abstract: Reinforcement learning (RL) methods such as Group Relative Policy Optimization (GRPO) have recently emerged as a leading approach for enhancing the reasoning ability of large language models (LLMs). Yet, the precise sources of their effectiveness remain unclear. In this work, we systematically decompose GRPO by benchmarking it against simpler REINFORCE-style baselines to identify its core components. Our analysis reveals a clear hierarchy: (i) iterative, online data collection is the dominant driver of performance, enabling even simple positive-only fine-tuning (e.g., RAFT) to be surprisingly strong; (ii) negative signals primarily sustain exploration by preventing rapid entropy collapse; and (iii) GRPO’s main benefit stems not from reward normalization itself, but from the implicit data filtering effect it induces by discarding prompts with uniform rewards (all-correct or all-incorrect). Guided by this insight, we propose REINFORCE-Rej, a minimal variant that makes filtering explicit. REINFORCE-Rej matches GRPO’s performance while being simpler and more KL-efficient. These findings suggest that principled data filtering, rather than algorithmic complexity, is the key to robust RL for LLMs.

URL: https://openreview.net/forum?id=eK3yDPtwIK

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Title: Robust Object Detection with Pseudo Labels from VLMs using Per-Object Co-teaching

Abstract: Foundation models, especially vision-language models (VLMs), offer compelling zero-shot object detection for applications like autonomous driving, a domain where manual labelling is prohibitively expensive. However, their detection latency and tendency to hallucinate predictions render them unsuitable for direct deployment. This work introduces a novel pipeline that addresses this challenge by leveraging VLMs to automatically generate pseudo-labels for training efficient, real-time object detectors. Our key innovation is a per-object co-teaching-based training strategy that mitigates the inherent noise in VLM-generated labels. The proposed per-object coteaching approach filters noisy bounding boxes from training instead of filtering the entire image. Specifically, two YOLO models learn collaboratively, filtering out unreliable boxes from each mini-batch based on their peers' per-object loss values. Overall, our pipeline provides an efficient, robust, and scalable approach to train high-performance object detectors for autonomous driving, significantly reducing reliance on costly human annotation. Experimental results on the KITTI dataset demonstrate that our method outperforms a baseline YOLOv5m model, achieving a significant mAP@0.5 boost ($31.12\%$ to $46.61\%$) while maintaining real-time detection latency. Furthermore, we show that supplementing our pseudo-labelled data with a small fraction of ground truth labels ($10\%$) leads to further performance gains, reaching $57.97\%$ mAP@0.5 on the KITTI dataset. We observe similar performance improvements for the ACDC and BDD100k datasets.

URL: https://openreview.net/forum?id=WqiUgx90nO

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Title: PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors

Abstract: Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increases, this bias drives generation toward overly smooth, homogeneous samples, reducing intra-class diversity and limiting generalization. We present PRISM (PRIors from diverse Source Models), a framework that disentangles architectural priors during synthesis. PRISM decouples the logit-matching and regularization objectives, supervising them with different teacher architectures: a primary model for logits and a stochastic subset for batch-normalization (BN) alignment. On ImageNet-1K, PRISM consistently and reproducibly outperforms single-teacher methods (e.g., SRe2L) and recent multi-teacher variants (e.g., G-VBSM) at low- and mid-IPC regimes. The generated data also show significantly richer intra-class diversity, as reflected by a notable drop in cosine similarity between features. We further analyze teacher selection strategies (pre- vs. intra-distillation) and introduce a scalable cross-class batch formation scheme for fast parallel synthesis. Code will be released after the review period.

URL: https://openreview.net/forum?id=xN58FtB1Gq

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Title: Genomic Next-Token Predictors are In-Context Learners

Abstract: In-context learning (ICL) -- the capacity of a model to infer and apply abstract patterns from examples provided within its input -- has been extensively studied in large language models trained for next-token prediction on human text. In fact, prior work often attributes this emergent behavior to distinctive statistical properties in *human* language. This raises a fundamental question: can ICL arise *organically* in other sequence domains purely through large-scale predictive training?

To explore this, we turn to genomic sequences, an alternative symbolic domain rich in statistical structure. Specifically, we study the Evo2 genomic model, trained predominantly on next-nucleotide (A/T/C/G) prediction, at a scale comparable to mid-sized LLMs. We develop a controlled experimental framework comprising symbolic reasoning tasks instantiated in both linguistic and genomic forms, enabling direct comparison of ICL across genomic and linguistic models. Our results show that genomic models, like their linguistic counterparts, exhibit log-linear gains in pattern induction as the number of in-context demonstrations increases. To the best of our knowledge, this is the first evidence of organically emergent ICL in genomic sequences, supporting the hypothesis that ICL arises as a consequence of large-scale predictive modeling over rich data.

URL: https://openreview.net/forum?id=KmNFx8DmaZ

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Title: AdaCubic: An Adaptive Cubic Regularization Optimizer for Deep Learning

Abstract: A novel regularization technique called AdaCubic is proposed that adapts the weight of
the cubic term. The heart of AdaCubic is an auxiliary optimization problem with cubic
constraints that dynamically adjusts the weight of the cubic term in Newton’s cubic regular-
ized method. We utilize Hutchinson’s method to approximate the Hessian matrix, thereby
reducing computation costs. We demonstrate that AdaCubic inherits the cubically regular-
ized Newton method’s local convergence guarantees. Our experiments in Computer Vision,
Natural Language Processing, and Signal Processing tasks demonstrate that AdaCubic out-
performs or competes with several widely used optimizers. Unlike other adaptive algorithms
that require fine-tuning of hyperparameters, AdaCubic is evaluated with a pre-fixed set of
hyperparameters, making it a highly attractive optimizer in situations where fine-tuning is
not feasible. This makes AdaCubic an attractive option for researchers and practitioners
alike. To our knowledge, AdaCubic is the first optimizer to leverage the power of cubic
regularization for large-scale applications. The code of AdaCubic will be publicly released
upon paper acceptance.

URL: https://openreview.net/forum?id=pZBQ7J37lk

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Title: Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios

Abstract: Multiple instance learning (MIL) is a framework for weakly supervised classification, where labels are assigned to sets of instances, i.e., bags, rather than to individual data points. This paradigm has proven effective in tasks where fine-grained annotations are unavailable or costly to obtain. However, the effectiveness of MIL drops sharply when training data are scarce, such as for rare disease classification. To address this challenge, we propose incorporating topological inductive biases into the data representation space within the MIL framework. This bias introduces a topology-preserving constraint that encourages the instance encoder to maintain the topological structure of the instance distribution within each bag when mapping them to MIL latent space. As a result, our Topology Guided MIL (TG-MIL) method enhances the performance and generalizability of MIL classifiers across different aggregation functions, especially under scarce-data regimes. Our evaluations show average performance improvement of 15.3% for synthetic MIL datasets, 2.8% for MIL benchmarks, and 5.5% for rare anemia datasets compared to current state-of-the-art MIL models, where only 17–120 samples per class are available. We make our code publicly available at https://anonymous.4open.science/r/TGMIL-59B6.

URL: https://openreview.net/forum?id=1hZy9ZjjCc

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Title: Toward bilipshiz geometric models

Abstract: Many neural networks for point clouds are, by design, invariant to the symmetries of this datatype: permutations and rigid motions. The purpose of this paper is to examine whether such networks preserve natural symmetry aware distances on the point cloud spaces, through the notion of bi-Lipschitz equivalence. This inquiry is motivated by recent work in the Equivariant learning literature which highlights the advantages of bi-Lipschitz models in other scenarios.

We consider two symmetry aware metrics on point clouds: (a) The Procrustes Matching (PM) metric and (b) the Hard Gromov Wasserstien distances. We show that these two distances themselves are not bi-Lipschitz equivalent, and as a corollary deduce that popular invariant networks for point clouds are not bi-Lipschitz with respect to the PM metric. We then show how these networks can be modified so that they do obtain bi-Lipschitz guarantees. Finally, we provide initial experiments showing the advantage of the proposed bi-Lipschitz model over standard invariant models, for the tasks of finding correspondences between 3D point clouds.

URL: https://openreview.net/forum?id=UeLoPZPjBu

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Title: Diversity Sampling Regularization for Multi-Domain Generalization

Abstract: Domain Generalization (DG) seeks to create models that can successfully generalize to new,
unseen target domains without the need for target domain data during training. Traditional
approaches often rely on data augmentation or feature mixing techniques, such as MixUp;
however, these methods may fall short in capturing the essential diversity within the feature
space, resulting in limited robustness against domain shifts. In this research, we revisit the
importance of diversity in DG tasks and propose a simple yet effective method to improve DG
performance through diversity-sampling regularization. Specifically, we calculate entropy
values for input data to assess their prediction uncertainty, and use these values to guide
sampling through Determinantal Point Process (DPP), which prioritizes selecting data sub-
sets with high diversity. By incorporating DPP-based diversity sampling as a regularization
strategy, our framework enhances the standard Empirical Risk Minimization (ERM) objec-
tive, promoting the learning of domain-agnostic features without relying on explicit data aug-
mentation. We empirically validate the effectiveness of our method on standard DG bench-
marks, including PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet, and through
extensive experiments show that it consistently improves generalization to unseen domains
and outperforms widely used baselines and S.O.T.A without relying on any task-specific
heuristics.

URL: https://openreview.net/forum?id=nXqMt7X2RX

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Title: MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence

Abstract: Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning framework, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder, and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact, while allowing flexible, data efficient adaptation to system heterogeneities. We benchmark MetaSym with highly varied and realistic datasets, such as a high-dimensional spring-mesh system Otness et al. (2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Crucially, we fine-tune and deploy MetaSym on real-world quadrotor data, demonstrating robustness to sensor noise and real-world uncertainty. Across all tasks, MetaSym achieves superior few-shot adaptation and outperforms larger State-of-The-Art (SoTA) models.

URL: https://openreview.net/forum?id=MV1wfMe647

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Title: Semi-Supervised Cross-Domain Imitation Learning

Abstract: Cross-domain imitation learning (CDIL) accelerates policy learning by transferring expert knowledge across domains, which is valuable in applications where collection of expert data is costly. Existing methods are either supervised, relying on proxy tasks and explicit alignment, or unsupervised, aligning distributions without paired data but often unstable. We introduce the Semi-Supervised CDIL (SS-CDIL) setting and propose the first algorithm for SS-CDIL with theoretical justification. Our method uses only offline data, including a small number of target expert demonstrations and some unlabeled imperfect trajectories. To handle domain discrepancy, we propose a novel cross-domain loss function for learning inter-domain state-action mappings and design an adaptive weight function to balance the source and target knowledge. Experiments on MuJoCo and Robosuite show consistent gains over the baselines, demonstrating that our approach achieves stable and data-efficient policy learning with minimal supervision.

URL: https://openreview.net/forum?id=WARXnbJawZ

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Title: Dynamics‑Aligned Diffusion Planning for Offline RL: A Unified Framework with Forward and Inverse Guidance

Abstract: Diffusion-based planning has emerged as a powerful paradigm for offline reinforcement learning (RL). However, existing approaches often overlook the physical constraints imposed by real-world dynamics, resulting in dynamics inconsistency—a mismatch between diffusion-generated trajectories and those feasible under true environment transitions. To address this issue, we propose Dynamics-Aligned Diffusion Planning (DADP), a unified framework that explicitly enforces dynamics consistency during the diffusion denoising process. DADP offers two complementary variants: DADP-F (Forward), which employs a forward dynamics model to ensure state-level feasibility, and DADP-I (Inverse), which leverages an inverse dynamics model to enhance action-level executability. Both variants share a unified guidance formulation that integrates task return optimization and dynamics alignment through gradient-based updates. Experiments on D4RL Maze2D and MuJoCo benchmarks demonstrate that DADP-F and DADP-I outperform state-of-the-art offline RL baselines, effectively reducing dynamics inconsistency and improving long-horizon robustness. This unifies diffusion-based planning with physically grounded dynamics modeling.

URL: https://openreview.net/forum?id=h3hG6EuqU2

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Title: AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective

Abstract: As machine learning (ML) systems expand in both scale and functionality, the security landscape has become increasingly complex, with a proliferation of attacks and defenses. However, existing studies largely treat these threats in isolation, lacking a coherent framework to expose their shared principles and interdependencies. This fragmented view hinders systematic understanding and limits the design of comprehensive defenses.
Crucially, the two foundational assets of ML—\textbf{data} and \textbf{models}—are no longer independent; vulnerabilities in one directly compromise the other. The absence of a holistic framework leaves open questions about how these bidirectional risks propagate across the ML pipeline.
To address this critical gap, we propose a \emph{unified closed-loop threat taxonomy} that explicitly frames model–data interactions along four directional axes. Our framework offers a principled lens for analyzing and defending foundation models.
The resulting four classes of security threats represent distinct but interrelated categories of attacks: (1) Data$\rightarrow$Data (D$\rightarrow$D): including \emph{data decryption attacks, watermark removal attacks, and jailbreak attacks}.
(2) Data$\rightarrow$Model (D$\rightarrow$M): including \emph{poisoning and harmful fine-tuning attacks};
(3) Model$\rightarrow$Data (M$\rightarrow$D): including \emph{model inversion, membership inference attacks, and training data extraction attacks};
(4) Model$\rightarrow$Model (M$\rightarrow$M): including \emph{model extraction attacks}.
We conduct a systematic review that analyzes the mathematical formulations, attack and defense strategies, and applications across the vision, language, audio, and graph domains.
Our unified framework elucidates the underlying connections among these security threats and establishes a foundation for developing scalable, transferable, and cross-modal security strategies—particularly within the landscape of foundation models.

URL: https://openreview.net/forum?id=1g7pKgClZs

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Title: Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning

Abstract: Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world systems. Attackers can implant backdoors into these models, causing them to behave normally under typical conditions but execute malicious behaviors when specific triggers are activated. In this work, we propose Plan2Cleanse, a test-time detection and mitigation framework that adapts Monte Carlo Tree Search to efficiently identify and neutralize RL backdoor attacks without requiring model retraining. Our approach recasts backdoor detection as a planning problem, enabling systematic exploration of temporally extended trigger sequences while maintaining black-box access to the target policy. By leveraging the detection results, Plan2Cleanse can further achieve efficient mitigation through tree-search preventive replanning. We evaluate our method across competitive MuJoCo environments, simulated O-RAN wireless networks, and Atari games. Plan2Cleanse achieves substantial improvements, increasing trigger detection success rates by over 61.4 percentage points in stealthy O-RAN scenarios and improving win rates from 35\% to 53\% in competitive Humanoid environments. These results demonstrate the effectiveness of our test-time defense approach and highlight the importance of proactive defenses against backdoor threats in RL deployments.

URL: https://openreview.net/forum?id=ZKhKxqwuPu

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Title: Mechanism-Aware Prediction of Tissue-Specific Drug Activity via Multi-Modal Biological Graphs

Abstract: Predicting how small molecules behave across human tissues is essential for targeted therapy development. While some existing models incorporate tissue identity, they treat it as a label—ignoring the underlying biological mechanisms that differentiate tissues. We present Expresso, a multi-modal architecture that predicts tissue-specific molecular activity by modeling how compounds interact with transcriptomic and pathway-level tissue context. Expresso constructs heterogeneous graphs from GTEx data, linking samples, genes, and pathways to reflect expression profiles and curated biological relationships. These graphs are encoded using a hierarchical GNN and fused with frozen molecular embeddings to produce context-aware predictions. A multi-task pretraining strategy—spanning gene recovery, tissue classification, and pathway-level contrastive learning—guides the model to learn mechanistically grounded representations. On nine tissues, Expresso improves mean AUC by up to 27.9 points over molecule-only baselines. Our results demonstrate that incorporating biological structure—not just tissue labels, yields more accurate and interpretable models for tissue-specific drug behavior.

URL: https://openreview.net/forum?id=UDW8m9iQeC

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Title: Wikipedia in the Era of LLMs: Evolution and Risks

Abstract: In this paper, we present a comprehensive analysis and monitoring framework for the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through existing data and using simulations to explore potential risks. We begin by analyzing article content and page views to study the recent changes in Wikipedia and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect various Natural Language Processing (NLP) tasks related to Wikipedia, including machine translation and retrieval-augmented generation (RAG). Our findings and simulation results reveal that Wikipedia articles have been affected by LLMs, with an impact of approximately 1%-2% in certain categories. If the machine translation benchmark based on Wikipedia is influenced by LLMs, the scores of the models may become inflated, and the comparative results among models could shift. Moreover, the effectiveness of RAG might decrease if the knowledge has been contaminated by LLMs. While LLMs have not yet fully changed Wikipedia's language and knowledge structures, we believe that our empirical findings signal the need for careful consideration of potential future risks in NLP research.

URL: https://openreview.net/forum?id=ahVmnYkVLt

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Title: GenAI vs. Human Creators: Procurement Mechanism Design in Two-/Three-Layer Markets

Abstract: With the rapid advancement of generative AI (GenAI), mechanism design adapted to its unique characteristics poses new theoretical and practical challenges. Unlike traditional goods, content from one domain can enhance the training and performance of GenAI models in other domains. For example, OpenAI’s video generation model Sora (Liu et al., 2024b) relies heavily on image data to improve video generation quality. In this work, we study nonlinear procurement mechanism design under data transferability, where online platforms employ both human creators and GenAI to satisfy cross-domain content demand. We propose optimal mechanisms that maximize either platform revenue or social welfare and identify the specific properties of GenAI that make such high-dimensional design problems tractable. Our analysis further reveals which domains face stronger competitive pressure and which tend to experience overproduction. Moreover, the growing role of data intermediaries, including labeling companies such as Scale AI and creator organizations such as The Wall Street Journal, introduces a third layer into the traditional platform–creator structure. We show that this three-layer market can result in a lose-lose outcome, reducing both platform revenue and social welfare, as large pre-signed contracts distort creators’ incentives and lead to inefficiencies in the data market. These findings suggest a need for government regulation of the GenAI data ecosystem, and our theoretical insights are further supported by numerical simulations.

URL: https://openreview.net/forum?id=Eukf4TBHS7

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Title: Enhancing Deep Consistent Graph Metric with Affinity and Alignment for Incremental Social Event Detection using Cross-Layer Attention

Abstract: Existing methods of event detection from social media (i.e., X), for instance, KPGNN, FinEvent, and CLKD, use triplet loss for feature separation. Triplet loss suffers from two notable discrepancies in the latent space: (i) inconsistency in intra-event and inter-event distances, and (ii) an inability to ensure the closeness of messages from the same event across different mini-batches. The present paper proposes two novel loss functions to improve consistency in the latent space. The first loss function guarantees consistent intra-event and inter-event distances by increasing the affinity between intra-event points. On the other hand, the alignment loss enhances the cosine similarity between the feature space and label space, thereby aligning features of the same event class across diverse mini-batches. We provide theoretical justification that the proposed loss ensures discriminative features in the latent space, like CGML, without its costly pairwise or specialised batching. Adding to our loss function, we introduce a new attention module designed to effectively address heterogeneous relations without necessitating a separate optimisation objective. Through comprehensive experimentation on two publicly available datasets, we have shown an average improvement of $26.59\%$, $30.49\%$ and $142.38\%$ in NMI, AMI and ARI, respectively, over supervised SOTA event detection methods. Our method also shows improvements over SOTA unsupervised event detection methods across both datasets. These are supported by statistical significance tests.

URL: https://openreview.net/forum?id=vNJ7mCgDbq

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Title: A Bayesian Approach to Segmentation with Noisy Labels via Spatially Correlated Distributions

Abstract: In semantic segmentation, the accuracy of models heavily depends on the high-quality annotations. However, in many practical scenarios, such as medical imaging and remote sensing, obtaining true annotations is not straightforward and usually requires significant human labor.
Relying on human labor often introduces annotation errors, including mislabeling, omissions, and inconsistency between annotators.
In the case of remote sensing, differences in procurement time can lead to misaligned ground-truth annotations.
These label errors are not independently distributed, and instead usually appear in spatially connected regions where adjacent pixels are more likely to share the same errors.
To address these issues, we propose an approximate Bayesian estimation based on a probabilistic model that assumes training data include label errors, incorporating the tendency for these errors to occur with spatial correlations between adjacent pixels.
However, Bayesian inference for such spatially correlated discrete variables is notoriously intractable. To overcome this fundamental challenge, we introduce a novel class of probabilistic models, which we term the \textbf{ELBO-Computable Correlated Discrete Distribution (ECCD)}. By representing the discrete dependencies through a continuous latent Gaussian field with a Kac-Murdock-Szeg\"{o} (KMS) structured covariance, our framework enables scalable and efficient variational inference for problems previously considered computationally prohibitive.
Through experiments on multiple segmentation tasks, we confirm that leveraging the spatial correlation of label errors significantly improves performance.
Notably, in specific tasks such as lung segmentation, the proposed method achieves performance comparable to training with clean labels under moderate noise levels. Code is included in the supplementary materials.

URL: https://openreview.net/forum?id=oMgfr8Kk2x

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Title: AI Influence: Mechanisms, Amplifiers, and Consequences

Abstract: AI influence refers to AI's impact on the knowledge and values of individuals by acting as producers, mediators, and receivers of information. As a result, it impacts our collective processes of creating and spreading knowledge, forming beliefs, and reaching consensus. We argue that there are mechanisms of inconspicuous influence in AI development and deployment pipelines, which, when amplified by societal dynamics, could lead to dangerous outcomes that we may reverse by early interventions. We detail those mechanisms, amplifiers, and potential long-term consequences.

URL: https://openreview.net/forum?id=7MKCuXjJMW

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Title: BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D Synthesis

Abstract: Text-to-3D synthesis has recently seen intriguing advances by combining the text-to-image priors with 3D representation methods, e.g., 3D Gaussian Splatting (3D GS), via Score Distillation Sampling (SDS). However, a hurdle of existing methods is the low efficiency, per-prompt optimization for a single 3D object. Therefore, it is imperative for a paradigm shift from per-prompt optimization to feed-forward generation for any unseen text prompts, which yet remains challenging. An obstacle is how to directly generate a set of millions of 3D Gaussians to represent a 3D object. This paper presents BrightDreamer, an end-to-end feed-forward approach that can achieve generalizable and fast (77 ms) text-to-3D generation. Our key idea is to formulate the generation process as estimating the 3D deformation from an anchor shape with predefined positions. For this, we first propose a Text-guided Shape Deformation (TSD) network to predict the deformed shape and its new positions, used as the centers (one attribute) of 3D Gaussians. To estimate the other four attributes (i.e., scaling, rotation, opacity, and SH), we then design a novel Text-guided Triplane Generator (TTG) to generate a triplane representation for a 3D object. The center of each Gaussian enables us to transform the spatial feature into the four attributes. The generated 3D Gaussians can be finally rendered at 705 frames per second. Extensive experiments demonstrate the superiority of our method over existing methods. Also, BrightDreamer possesses a strong semantic understanding capability even for complex text prompts. The project code is available in supplementary materials.

URL: https://openreview.net/forum?id=Rb19CQCwbi

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Title: When Does Causal Regularization Help? A Systematic Study of Boundary Conditions in Spurious Correlation Learning

Abstract: We challenge the conventional wisdom that explicit causal regularization is necessary for out-of-distribution generalization. Through systematic investigation on ColoredMNIST, we discover that reconstructive architectures like autoencoders provide a powerful implicit causal bias that largely obviates the need for explicit methods like IRM or HSIC. Autoen-coder baselines achieve 82-86% accuracy with 99% spurious correlation, with explicit causal losses adding only marginal (0-4pp) gains.
Using the Atlasing Pattern Space (APS) framework—a modular toolkit combining topology preservation (T), causal invariance (C), and energy shaping (E)—we establish clear bound-ary conditions for when explicit regularization helps. Our experiments across multiple do-mains reveal that: (1) explicit causal methods become critical only when architectural bias is absent or spurious correlations are pathologically strong; (2) topology preservation im-proves kNN fidelity in high-dimensional vision tasks but fails completely in low-dimensional synthetic settings; and (3) energy-based regularization effectively prevents overfitting while maintaining OOD accuracy.
Through controlled experiments including a systematic study of component domain-specificity, we demonstrate that regularization components are not universally beneficial but rather require careful domain-specific validation. Our results reframe causal learning as a hierarchical process: architectural choice is primary, with explicit regularizers serving as targeted, domain-specific corrections when architectural bias proves insufficient.

URL: https://openreview.net/forum?id=IiIhq5JeDJ

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Title: Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection

Abstract: Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-consistency-based techniques have been proposed for hallucination detection. We empirically show that these methods perform nearly as well as a supervised (black-box) oracle, leaving limited room for further gains within this paradigm. To address this limitation, we explore cross-model consistency checking between the target model and an additional verifier LLM. With this extra information, we observe improved oracle performance compared to purely self-consistency-based methods. We then propose a budget-friendly, two-stage detection algorithm that calls the verifier model only for a subset of cases. It dynamically switches between self-consistency and cross-consistency based on an uncertainty interval of the self-consistency classifier. We provide a geometric interpretation of consistency-based hallucination detection methods through the lens of kernel mean embeddings, offering deeper theoretical insights. Extensive experiments show that this approach maintains high detection performance while significantly reducing computational cost.

URL: https://openreview.net/forum?id=6tlLISSgiu

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Title: Lifelong Open-Ended Probability Predictors

Abstract: We advance probabilistic multiclass prediction on lifelong streams of
items. A (learner) predictor must provide item probabilities, adapting
to significant non-stationarity, including new item appearances and
frequency changes. The predictor is not given the set of items that it
needs to predict before hand, and moreover the set can grow unbounded:
the space-limited predictor need only track the currently salient
items and their probabilities.
We develop Sparse Moving Average techniques (SMAs), including
adaptations of sparse EMA as well as novel queue-based methods with
dynamic per-item histories. For performance evaluation, to handle new
items, we develop a bounded version of log-loss. Our findings, on a
range of synthetic and real data streams, show that dynamic
predictand-specific (per connection) parameters, such as learning
rates, enhance both adaptation speed and stability. Code is provided.

URL: https://openreview.net/forum?id=rojnGCcMaK

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Title: Scalable physical source-to-field inference with hypernetworks

Abstract: We present a generative model that amortises computation for the field and potential around e.g.~gravitational or electromagnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources $M$ and evaluation points $N$, or requires a fixed evaluation grid to exploit fast Fourier transforms. Using an architecture where a hypernetwork produces an implicit representation of the field or potential around a source collection, our model instead performs as $\mathcal{O}(M + N)$, achieves relative error of $\sim\!4\%-6\%$, and allows evaluation at arbitrary locations for arbitrary numbers of sources, greatly increasing the speed of e.g.~physics simulations. We compare with existing models and develop two-dimensional examples, including cases where sources overlap or have more complex geometries, to demonstrate its application.

URL: https://openreview.net/forum?id=EvfwGpo135

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Title: HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data

Abstract: Ensembling is commonly used in machine learning on tabular data to boost predictive performance and robustness, but larger ensembles often lead to increased hardware demand. We introduce HAPEns, a post-hoc ensembling method that explicitly balances accuracy against hardware efficiency. Inspired by multi-objective and quality diversity optimization, HAPEns constructs a diverse set of ensembles along the Pareto front of predictive performance and resource usage. Experiments on 83 tabular classification datasets show that HAPEns significantly outperforms baselines, achieving superior accuracy–efficiency trade-offs. Ablation studies further reveal that memory usage is a particularly effective objective metric. Further, we show that even a greedy ensembling algorithm can be significantly improved in this task with a static multi-objective weighting scheme.

URL: https://openreview.net/forum?id=FbuhDKWyx9

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Title: A New Kind of Network? Review and Reference Implementation of Neural Cellular Automata

Abstract: Stephen Wolfram proclaimed in his 2003 seminal work ``A New Kind Of Science'' that simple recursive programs in the form of Cellular Automata (CA) are a promising approach to replace currently used mathematical formalizations, e.g. differential equations, to improve the modeling of complex systems.
Over two decades later, while Cellular Automata have still been waiting for a substantial breakthrough in scientific applications, recent research showed new and promising approaches which combine Wolfram's ideas with learnable Artificial Neural Networks: So-called Neural Cellular Automata (NCA) are able to learn the complex update rules of CA from data samples, allowing them to model complex, self-organizing generative systems.
The aim of this paper is to review the existing work on NCA and provide a unified theory, as well as a reference implementation in the open-source library NCAtorch.

URL: https://openreview.net/forum?id=NRwjj0ZLq0

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Title: Incorporating New Knowledge into Federated Learning: Advances, Insights, and Future Directions

Abstract: Federated Learning (FL) is a distributed learning approach that allows participants to collaboratively train machine learning models without sharing the raw data. It is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid development trend, along with the continuous emergence of new demands for FL in the real world, that prompts us to focus on a very important problem: How to Incorporate New Knowledge into Federated Learning? The primary challenge here is to effectively and timely incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, extend their lifespan, and facilitate sustainable development. In the meantime, established FL systems should preserve existing functionalities during the incorporation of new knowledge. In this paper, we systematically define the main sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we thoroughly analyze and discuss the technical approaches for incorporating new knowledge into existing FL systems and examine the impact of the form and timing of new knowledge arrival on the incorporation process. Furthermore, we comprehensively discuss the potential future directions for FL, incorporating new knowledge and considering a variety of factors, including scenario setups, security and privacy threats, and incentives.

URL: https://openreview.net/forum?id=BWBfK3B3b7

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Title: Astrocyte-Inspired Hierarchical Routing for Enhanced Expert Specialization in Mixture-of-Experts Models

Abstract: The Mixture-of-Experts (MoE) architecture is a leading paradigm for scaling, but cultivating genuine expert specialization is a persistent challenge, often hindered by load balancing. This paper introduces Astrocyte-Hierarchical Routing (AHR), a novel, bio-inspired mechanism that addresses this challenge. Drawing inspiration from astrocytes, AHR conditions local, token-level routing decisions on a global context signal. In our encoder-based implementation, this signal, derived from the [CLS] token, additively biases local routing decisions, promoting a developmental trajectory for expert functionality. We conduct experiments on a multi-class text classification task, comparing AHR against strong baselines. The results demonstrate that AHR achieves a statistically significant and substantial increase in final-layer expert specialization without incurring a discernible loss in task performance. Qualitative analysis further confirms that AHR fosters a transition from generalist experts in early layers to highly specialized experts in later layers. This work presents a new principle for MoE router design: a contextual, two-level approach. This successful validation in an encoder model serves as a proof-of-concept, opening the way for future work on scaling AHR and adapting its principle to other architectures.

URL: https://openreview.net/forum?id=4pHo47SXaA

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Title: Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning

Abstract: Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These outages halt industrial operations, impact communities, damage critical infrastructure, profoundly disrupt economies, and have far-reaching effects across various sectors. To mitigate these effects, the University of Connecticut and Eversource Energy Center have developed an outage prediction modeling (OPM) system to provide pre-emptive forecasts for electric distribution networks before such weather events occur. However, existing predictive models in the system do not incorporate the spatial effect of extreme weather events. To this end, we develop Spatially Aware Hybrid Graph Neural Networks (SA-HGNN) with contrastive learning to enhance the OPM predictions for extreme weather-induced power outages. Specifically, we first encode spatial relationships of both static features (e.g., land cover, infrastructure) and event-specific dynamic features (e.g., wind speed, precipitation) via Spatially Aware Hybrid Graph Neural Networks (SA-HGNN). Next, we leverage contrastive learning to handle the imbalance problem associated with different types of extreme weather events and generate location-specific embeddings by minimizing intra-event distances between similar locations while maximizing inter-event distances across all locations. Thorough empirical studies in four utility service territories, i.e., Connecticut, Western Massachusetts, Eastern Massachusetts, and New Hampshire, demonstrate that SA-HGNN can achieve state-of-the-art performance for power outage prediction.

URL: https://openreview.net/forum?id=Vf5FDYrOiU

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Title: Topology-Guided Graph Pre-training and Prompt Learning on Directed Graphs

Abstract: In recent years, graph neural networks (GNNs) have been the dominant approach for graph representation learning, leading to new state-of-the-art
results on many classification and prediction tasks. However, they are limited by the fact that they cannot effectively learn expressive node representations without the guide of labels, thus suffering from the labeled data scarcity problem. To address the challenges of labeling costs and improve robustness in few-shot scenarios, pre-training on self-supervised tasks has garnered significant attention. Additionally, numerous prompting methods have been proposed as effective ways to bridge the gap between pretext tasks and downstream applications. Although graph pre-training and prompt tuning methods have explored various downstream tasks on undirected graphs, directed graphs have been largely under-explored and these models suffer limitations in capture directional and topological information in directed graphs. In this paper, we propose a novel topology-guided directed graph pre-training and prompt tuning model, named TopoDIG, that can effectively capture intrinsic directional structural and local topological features in directed graphs. These features play essential roles in transferring knowledge from a pre-trained model to downstream tasks. For model architecture, TopoDIG consists of an encoder in the form of a magnetic Laplacian matrix, a topological encoder, and a graph prompt learning function. Experimental results on both real-world and synthetic directed graphs demonstrate the superior performance of TopoDIG compared to prominent baseline methods.

URL: https://openreview.net/forum?id=kMIdkLTys8

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Title: Watermarking Degrades Alignment in Language Models: Analysis and Mitigation

Abstract: Watermarking is emerging as a practical mechanism for provenance in language models, but it modifies token probabilities at inference time, the very same locus targeted by alignment training. This overlap raises a basic question relevant for deployment: how do watermark-induced shifts interact with the procedures intended to make models safe and useful? We conduct a systematic study across several contemporary models and two representative watermarking schemes. We find that watermarking induces a nontrivial, patterned yet model-specific shift in alignment. Two regimes recur: guard attenuation, where models become more helpful but less safe, and guard amplification, where refusals become overly conservative. Crucially, these effects persist even after controlling for perplexity degradation, indicating alignment-specific distortions beyond generalized quality loss. To mitigate these effects, we introduce Alignment Resampling (AR), a procedure that samples multiple watermarked outputs and selects the most aligned response according to an external reward model. Drawing on established results for the expected maximum of Gaussian random variables, we derive a theoretical lower bound showing that alignment gains grow sublogarithmically with sample size, providing principled guidance on minimal sampling requirements. Interestingly, we observe that sampling as few as two to four candidates largely restores unwatermarked alignment performance in truthfulness, safety, and helpfulness, while leaving watermark detectability essentially unchanged. This study offers the first systematic audit of watermarking-alignment interactions, quantifies the trade-off between watermark strength and alignment, and proposes a simple, inference-time mitigation procedure suitable for deployment.

URL: https://openreview.net/forum?id=w2ATKQcfWx

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Title: E$^2$M: Double Bounded $\alpha$-Divergence Optimization for Tensor-based Discrete Density Estimation

Abstract: Tensor-based discrete density estimation requires flexible modeling and proper divergence criteria to enable effective learning; however, traditional approaches using α-divergence face analytical challenges due to the α-power terms in the objective function, which hinder the
derivation of closed-form update rules. We present a generalization of the expectation-maximization (EM) algorithm, called the E2M algorithm. It circumvents this issue by first relaxing the optimization into the minimization of a surrogate objective based on the Kullback–Leibler (KL) divergence, which is tractable via the standard EM algorithm, and subsequently applying a tensor many-body approximation in the M-step to enable simultaneous closed-form updates of all parameters. Our approach offers flexible modeling for not only a variety of low-rank structures, including the CP, Tucker, and Tensor Train formats, but also their mixtures, thus allowing us to leverage the strengths of different low-rank structures. We evaluate the effectiveness of our approach on synthetic and real datasets, highlighting its superior convergence to gradient-based procedures, robustness to outliers, and favorable density estimation performance compared to prominent existing tensor-based methods.

URL: https://openreview.net/forum?id=954CjhXSXL

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Title: BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching

Abstract: Generating independent samples from a Boltzmann distribution is a highly relevant problem in scientific research, \textit{e.g.} in molecular dynamics, where one has initial access to the underlying energy function but not to samples from the Boltzmann distribution. We address this problem by learning the energies of the convolution of the Boltzmann distribution with Gaussian noise. These energies are then used to generate independent samples through a denoising diffusion approach. The resulting method, \textsc{Noised Energy Matching} (NEM), has lower variance and only slightly higher cost than previous related works. We also improve NEM through a novel bootstrapping technique called \textsc{Bootstrap NEM} (BNEM) that further reduces variance while only slightly increasing bias. Experiments on a collection of problems demonstrate that NEM can outperform previous methods while being more robust and that BNEM further improves on NEM.

URL: https://openreview.net/forum?id=ZZktU0U6Pu

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Title: Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges

Abstract: The emergence of multi-modal foundation models has markedly transformed the technology for autonomous driving, shifting away from conventional and mostly hand-crafted design choices towards unified, foundation-model-based approaches, capable of directly inferring motion trajectories from raw sensory inputs. This new class of methods can also incorporate natural language as an additional modality, with Vision-Language-Action (VLA) models serving as a representative example. In this review, we provide a comprehensive examination of such methods through a unifying taxonomy to critically evaluate their architectural design choices, methodological strengths, and their inherent capabilities and limitations. Our survey covers 37 recently proposed approaches that span the landscape of trajectory planning with foundation models. Furthermore, we assess these approaches with respect to the openness of their source code and datasets, offering valuable information to practitioners and researchers. We provide an accompanying webpage that catalogues the methods based on our taxonomy, which will be released publicly upon acceptance.

URL: https://openreview.net/forum?id=E2L5J2O2Bk

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Title: Retrospective Feature Estimation for Continual Learning

Abstract: The intrinsic capability to continuously learn a changing data stream is a desideratum of deep neural networks (DNNs). However, current DNNs suffer from catastrophic forgetting, which interferes with remembering past knowledge. To mitigate this issue, existing Continual Learning (CL) approaches often retain exemplars for replay, regularize learning, or allocate dedicated capacity for new tasks. This paper investigates an unexplored direction for CL called Retrospective Feature Estimation (RFE). RFE learns to reverse feature changes by aligning the features from the current trained DNN backward to the feature space of the old task, where performing predictions is easier. This retrospective process utilizes a chain of small feature mapping networks called retrospector modules. Empirical experiments on several CL benchmarks, including CIFAR10, CIFAR100, and Tiny ImageNet, demonstrate the effectiveness and potential of this novel CL direction compared to existing representative CL methods.

URL: https://openreview.net/forum?id=9NnhVME4Q6

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Title: STEALTH: Secure Transformer for Encrypted Alignment of Latent Text Embeddings via Semantic Isomorphism Enforcement (SIE) Loss Function

Abstract: The pervasive use of large language models (LLMs) on sensitive data presents a critical privacy challenge, as traditional encryption renders data unusable for inference. We introduce STEALTH, a 120M secure transformer framework designed to process encrypted text while preserving its semantic utility under an authorized-key threat model (no decryption or side-channel access). The core innovation of STEALTH is the Semantic Isomorphism Enforcement (SIE) loss function, a loss that trains the model to learn a topology-preserving mapping between encrypted text embeddings and their original plaintext latent space. This encourages preservation of semantic relationships and topological structure in the encrypted domain. Using retrieval-based reconstruction from a domain-aligned plaintext corpus, STEALTH achieves near-perfect semantic retrieval (BLEU score of 1.0 under full-corpus coverage in our experiments) and enables accurate privacy-preserving clustering on encrypted embeddings. We evaluate STEALTH across 44 datasets spanning general language understanding, healthcare, finance, legal, e-commerce, programming, content analysis, reading comprehension, and corporate communication domains with 16 encryption schemes (704 experimental conditions), establishing a comprehensive benchmark for
privacy-preserving NLP on encrypted text. Performance depends on domain alignment between encrypted inputs and the indexed plaintext corpus. Our results demonstrate that, with well-aligned domain indexes and retrieval support, models can perform effective NLP on encrypted data without direct decryption.

URL: https://openreview.net/forum?id=73PV17dVCM

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Title: SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition

Abstract: Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning have become promising in this field. They learn meaningful representations by pulling positive pairs (two augmented versions of the same instance) closer and pushing negative pairs (different from the positive pairs) apart. In SLR, only certain parts of the sign videos provide information that is truly useful for their recognition. Applying contrastive methods to SLR raises two issues: (i) contrastive learning methods treat all parts of a video in the same way, without taking into account the relevance of certain parts over others; (ii) shared movements between different signs make negative pairs highly similar, complicating sign discrimination. These issues lead to learning non-discriminative features for sign recognition and poor results in downstream tasks. In response, this paper proposes a self-supervised learning framework designed to learn meaningful representations for SLR. This framework consists of two key components designed to work together: (i) a new self-supervised approach with free-negative pairs; (ii) a new data augmentation technique. This approach shows a considerable gain in accuracy compared to several contrastive and self-supervised methods, across linear evaluation, semi-supervised learning, and transferability between sign languages.

URL: https://openreview.net/forum?id=buTZkTXijy

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Title: Qini Curve Estimation under Clustered Network Interference

Abstract: Qini curves are a widely used tool for assessing treatment policies under allocation constraints as they visualize the incremental gain of a new treatment policy versus the cost of its implementation. Standard Qini curve estimation assumes no interference between units: that is, that treating one unit does not influence the outcome of any other unit. In many real-life applications such as public policy or marketing, however, the presence of interference is common. Ignoring interference in these scenarios can lead to systematically biased Qini curves that over- or under-estimate a treatment policy's cost-effectiveness. In this paper, we address the problem of Qini curve estimation under clustered network interference, where interfering units form independent clusters. We propose a formal description of the problem setting with an experimental study design under which we can account for clustered network interference. Within this framework, we describe three estimation strategies, each suited to different conditions, and provide guidance for selecting the most appropriate approach by highlighting the inherent bias-variance trade-offs. To complement our theoretical analysis, we introduce a marketplace simulator that replicates clustered network interference in a typical e-commerce environment, allowing us to evaluate and compare the proposed strategies in practice.

URL: https://openreview.net/forum?id=iYsLwAuCY5

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Title: Probabilistic Shapley Value Modeling and Inference

Abstract: We propose probabilistic Shapley inference (PSI), a novel probabilistic framework to model and infer sufficient statistics of feature attributions in flexible predictive models, via latent random variables whose mean recovers Shapley values. PSI enables efficient, scalable inference over input-to-output attributions and their uncertainty, via a variational objective that jointly trains a predictive (regression or classification) model and its attribution distributions. To address the challenge of marginalizing over variable-length input feature subsets for Shapley value calculation, we introduce a masking-based neural network architecture, with a modular training and inference procedure. We evaluate PSI on synthetic and real-world datasets, showing that it achieves competitive predictive performance compared to strong baselines, while learning feature attribution distributions —centered at Shapley values— that reveal meaningful attribution uncertainty across data modalities.

URL: https://openreview.net/forum?id=Au7e02c4C7

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Title: Hierarchical Multi-Level 3D Geometry Generation with Stress-Aware Learning

Abstract: Current approaches for Lego 3d structural assembly are usually learned to maximize IOU between generated output and target construction. We propose a new approach which is able to build stable structures based on physics-aware reward. Our method employs a two-level agent architecture in which a high-level PPO-based planner proposes a scheme, while a low-level Wave Function Collapse (WFC) agent handles precise brick placement with constraint satisfaction. Experimental results demonstrate that our hierarchical method consistently constructs structurally sound buildings while reducing material usage. We also show that replacing the computationally expensive FEM solver with fast FNO achieves comparable performance, confirming the approach's scalability for large-scale problems.

URL: https://openreview.net/forum?id=kyoXKiyoA3

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Title: Token-Based Detection of Spurious Correlations in Vision Transformers

Abstract: Due to their powerful feature association capabilities, neural network-based computer vision models have the ability to detect and exploit unintended patterns within the data, potentially leading to correct predictions based on incorrect or unintended but statistically relevant signals. These clues may vary from simple color aberrations to small texts within the image. In situations where these unintended signals align with the predictive task, models can mistakenly link these features with the task and rely on them for making predictions. This phenomenon is referred to as spurious correlations, where patterns appear to be associated with the task but are actually coincidental. As a result, detection and mitigation of spurious correlations have become crucial tasks for building trustworthy, reliable, and generalizable machine learning models. In this work, we present a novel token-based method to detect spurious correlations in vision transformers, a type of neural network architecture that gained significant popularity in recent years. Using both supervised and self-supervised trained models, we present large-scale experiments on the ImageNet dataset demonstrating the ability of the proposed method to identify spurious correlations. We also find that, even if the same architecture is used, the training methodology has a significant impact on the model's reliance on spurious correlations. Furthermore, we show that certain classes in the ImageNet dataset contain spurious signals that are easily detected by the models and discuss the underlying reasons for those spurious signals. In light of our findings, we provide an exhaustive list of the aforementioned images and call for caution in their use in future research efforts. Lastly, we present a case study investigating spurious signals in invasive breast mass classification, grounding our work in a real-world scenario.

URL: https://openreview.net/forum?id=GlPXPhwOzI

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Title: HyperAdapt: Simple High-Rank Adaptation

Abstract: Foundation models excel across diverse tasks, but adapting them to specialized applications often requires fine-tuning, an approach that is memory and compute-intensive. Parameter-efficient fine-tuning (PEFT) methods mitigate this by updating only a small subset of weights. In this paper, we introduce HyperAdapt, a parameter-efficient fine-tuning method that significantly reduces the number of trainable parameters compared to state-of-the-art methods like LoRA. Specifically, HyperAdapt adapts a pre-trained weight matrix by applying row- and column-wise scaling through diagonal matrices, thereby inducing a high-rank update while requiring only $n+m$ trainable parameters for an $n \times m$ matrix. Theoretically, we establish an upper bound on the rank of HyperAdapt's updates, and empirically, we confirm that it consistently induces high-rank transformations across model layers. Experiments on GLUE, arithmetic reasoning, and commonsense reasoning benchmarks with models up to 14B parameters demonstrate that HyperAdapt matches or nearly matches the performance of full fine-tuning and state-of-the-art PEFT methods while using orders of magnitude fewer trainable parameters.

URL: https://openreview.net/forum?id=uhk13aXVxC

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Title: A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning

Abstract: Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal policy optimization (PPO) is the most popular choice of method for policy optimization. While effective in terms of performance, PPO is highly sensitive to hyper-parameters and involves substantial computational overhead. REINFORCE, on the other hand, mitigates some computational complexities such as high memory overhead and sensitive hyper-parameter tuning, but has suboptimal performance due to high-variance and sample inefficiency. While the variance of the REINFORCE can be reduced by sampling multiple actions per input prompt and using a baseline correction term, it still suffers from sample inefficiency. To address these challenges, we systematically analyze the efficiency-effectiveness trade-off between REINFORCE and PPO, and propose leave-one-out PPO (LOOP), a novel RL for diffusion fine-tuning method. LOOP combines variance reduction techniques from REINFORCE, such as sampling multiple actions per input prompt and a baseline correction term, with the robustness and sample efficiency of PPO via clipping and importance sampling. Our results demonstrate that LOOP effectively improves diffusion models on various black-box objectives, and achieves a better balance between computational efficiency and performance.

URL: https://openreview.net/forum?id=i8WJhKn455

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Title: Towards A More Transparent Understanding of Weight-Averaged Model Merging

Abstract: Model merging, particularly through weight averaging, has shown surprising effectiveness in saving computations and improving model performance without any additional training. However, the interpretability of why and how this technique works remains unclear. In this work, we reinterpret weight-averaged model merging through the lens of interpretability and provide empirical insights into the underlying mechanisms that govern its behavior. We approach the problem from three perspectives: (1) we analyze the learned weight structures and demonstrate that model weights encode structured representations that help explain the compatibility of weight averaging; (2) we compare averaging in weight space and feature space across diverse model architectures (CNNs and ViTs) and datasets, aiming to expose under which circumstances what combination paradigm will work more effectively; (3) we study the effect of parameter scaling on prediction stability, highlighting how weight averaging acts as a form of regularization that contributes to robustness. By framing these analyses in an interpretability context, our work contributes to a more transparent and systematic understanding of model merging for stakeholders interested in the safety and reliability of untrained model combination methods. The code is available at https://anonymous.4open.science/r/Rethink-Merge-E9BE.

URL: https://openreview.net/forum?id=DF7YplmcYx

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Title: On Almost Surely Safe Alignment of Large Language Models at Inference-Time

Abstract: We introduce a novel inference-time alignment approach for LLMs that aims to generate safe responses almost surely, i.e., with probability approaching one w.r.t. a given cost model. Our approach models the generation of safe responses as a constrained Markov Decision Process (MDP) within the LLM's latent space. We augment a safety state that tracks the evolution of safety constraints and dynamically penalize unsafe generations to ensure the generation of safe responses. Consequently, we demonstrate formal safety guarantees w.r.t. the given cost model upon solving the MDP in the latent space with sufficiently large penalties. Building on this foundation, we propose $\texttt{InferenceGuard}$, a practical implementation that safely aligns LLMs without modifying the model weights. Empirically, we demonstrate that $\texttt{InferenceGuard}$ effectively balances safety and task performance, outperforming existing inference-time alignment methods in generating safe and aligned responses. Our findings contribute to the advancement of safer LLM deployment through alignment at inference-time, thus presenting a promising alternative to resource-intensive, overfitting-prone alignment techniques like RLHF.

URL: https://openreview.net/forum?id=FlnokjaSEu

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Title: α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction

Abstract: Comprehending 3D scenes is paramount for tasks such as planning and mapping for autonomous vehicles and robotics. Camera-based 3D Semantic Occupancy Prediction (OCC) aims to infer scene geometry and semantics from limited observations. While it has gained popularity due to affordability and rich visual cues, existing methods often neglect the inherent uncertainty in models. To address this, we propose an uncertainty-aware OCC method (α-OCC). We first introduce Depth-UP, an uncertainty propagation framework that improves geometry completion by up to 11.58% and semantic segmentation by up to 12.95% across various OCC models. For uncertainty quantification (UQ), we propose the hierarchical conformal prediction (HCP) method, effectively handling the high-level class imbalance in OCC datasets. On the geometry level, the novel KL-based score function significantly improves the occupied recall (45%) of safety-critical classes with minimal performance overhead (3.4% reduction). On UQ, our HCP achieves smaller prediction set sizes while maintaining the defined coverage guarantee. Compared with baselines, it reduces up to 90% set size, with 18% further reduction when integrated with Depth-UP. Our contributions advance OCC accuracy and robustness, marking a noteworthy step forward in autonomous perception systems.

URL: https://openreview.net/forum?id=bUv25gBLlV

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Title: LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization

Abstract: We introduce LLM-Lasso, a novel framework that leverages large language models (LLMs) to guide feature selection in Lasso $\ell_1$ regression. Unlike traditional methods that rely solely on numerical data, LLM-Lasso incorporates domain-specific knowledge extracted from natural language, optionally enhanced through a retrieval-augmented generation (RAG) pipeline, to seamlessly integrate data-driven modeling with contextual insights. Specifically, the LLM generates penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. This is, to our knowledge, the first embedded LLM-driven feature selector. By design, LLM-Lasso addresses the key robustness challenges of LLM-driven feature selection: the risk of LLM hallucinations or low-quality responses. An internal cross-validation step is crucial to LLM-Lasso’s robustness, determining how heavily the prediction pipeline relies on the LLM’s outputs. Consequently, irrespective of the LLM’s generation quality, LLM-Lasso is guaranteed never to perform worse than standard Lasso.
In various biomedical case studies, LLM-Lasso outperforms standard Lasso and existing feature selection baselines.

URL: https://openreview.net/forum?id=AJPl6rwus3

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Title: LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection

Abstract: The proliferation of misinformation in the digital age has led to significant societal challenges. Existing approaches often struggle with capturing long-range dependencies, complex semantic relations, and the social dynamics influencing news dissemination. Furthermore, these methods require extensive labelled datasets, making their deployment resource-intensive. In this study, we propose a novel self-supervised misinformation detection framework that integrates both complex semantic relations using Abstract Meaning Representation (AMR) and news propagation dynamics. We introduce an LLM-based graph contrastive loss (LGCL) that utilizes negative anchor points generated by a Large Language Model (LLM) to enhance feature separability in a zero-shot manner. To incorporate social context, we employ a multi view graph masked autoencoder, which learns news propagation features from social context graph. By combining these semantic and propagation-based features, our approach effectively differentiates between fake and real news in a self-supervised manner. Extensive experiments demonstrate that our self-supervised framework achieves superior performance compared to other state-of-the-art methodologies, even with limited labelled datasets while improving generalizability.

URL: https://openreview.net/forum?id=puFYjgDXz6

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Title: When Does LoRA Reuse Work? Theoretical Limits and Mechanisms for Recycling LoRAs Without Data Access

Abstract: Reusing low-rank adapters (LoRAs) by merging or routing is a common strategy for adapting large language models to new tasks, especially when training data is unavailable but many fine-tuned LoRAs are accessible. While the availability of publicly shared LoRA weights has inspired new algorithms for composing them to solve new tasks, recent findings highlight limitations in LoRA’s ability to integrate new knowledge. This work investigates when LoRA reuse could be viable without direct access to training data. Through theoretical analysis and experiments on synthetic two-hop reasoning and math word problems, we show that data-agnostic methods, such as parameter averaging and dynamic selection, often fail to combine knowledge from logically disjoint fine-tuning datasets. This challenge is particularly pronounced when the relevant knowledge is underrepresented during pretraining. However, reuse can succeed when fine-tuning datasets share solution templates, such as reasoning patterns or reusable code, which serve as bridges among tasks. Our results suggest that LoRA reuse relies more on shallow pattern matching than on logical integration of existing knowledge. This mechanism-based perspective offers practical guidance for curating datasets and designing systems that enable LoRA reuse to overcome data-access limitations. Findings indicate that future research should focus on the mechanisms enabling effective adapter reuse rather than solely on developing new reuse algorithms.

URL: https://openreview.net/forum?id=lVqUJlsnRy

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Title: Graph Concept Bottleneck Models

Abstract: Concept Bottleneck Models (CBMs) have emerged as a prominent framework for interpretable deep learning, providing human-understandable intermediate concepts that enable transparent reasoning and direct intervention. However, existing CBMs typically assume conditional independence among concepts given the label, overlooking the intrinsic dependencies and correlations that often exist among them. In practice, concepts are rarely isolated: modifying one concept may inherently influence others. Ignoring these relationships can lead to oversimplified representations and weaken interpretability. To address this limitation, we introduce **Graph CBMs**, a novel variant of CBMs that explicitly models the relational structure among concepts through a latent concept graph. Our approach can be seamlessly integrated into existing CBMs as a lightweight, plug-and-play module, enriching their reasoning capability without sacrificing interpretability. Experimental results on multiple real-world image classification benchmarks demonstrate that Graph CBMs (1) achieve higher predictive accuracy while revealing meaningful concept structures, (2) enable more effective and robust concept-level interventions, and (3) maintain stable performance across diverse architectures and training setups.

URL: https://openreview.net/forum?id=a4azUYjRhU

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