Daily TMLR digest for Nov 24, 2025
TMLR
Accepted papers
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Title: Multi-Modal Foundation Models for Computational Pathology: A Survey
Authors: Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Zhong Chen, Peter K Sorger, Chen Zhao
Abstract: Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early developments centered on uni-modal models trained solely on visual data, recent advances have highlighted the promise of multi-modal foundation models that integrate heterogeneous data sources such as textual reports, structured domain knowledge, and molecular profiles. In this survey, we provide a comprehensive and up-to-date review of multi-modal foundation models in CPath, with a particular focus on models built upon hematoxylin and eosin (H&E) stained whole slide images (WSIs) and tile-level representations. We categorize 34 state-of-the-art multi-modal foundation models into three major paradigms: vision-language, vision-knowledge graph, and vision-gene expression. We further divide vision-language models into non-LLM-based and LLM-based approaches. Additionally, we analyze 30 available multi-modal datasets tailored for pathology, grouped into image-text pairs, instruction datasets, and image-other modality pairs. Our survey also presents a taxonomy of downstream tasks, highlights training and evaluation strategies, and identifies key challenges and future directions. We aim for this survey to serve as a valuable resource for researchers and practitioners working at the intersection of pathology and AI.
URL: https://openreview.net/forum?id=NZ7GSH92cY
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Title: AttentionBreaker: Adaptive Evolutionary Optimization for Unmasking Vulnerabilities in LLMs through Bit-Flip Attacks
Authors: Sanjay Das, Swastik Bhattacharya, Souvik Kundu, Shamik Kundu, Anand Menon, Arnab Raha, Kanad Basu
Abstract: Large language models (LLMs) have significantly advanced natural language processing (NLP) yet are still susceptible to hardware-based threats, particularly bit-flip attacks (BFAs). Traditional BFA techniques, requiring iterative gradient recalculations after each bit-flip, become computationally prohibitive and lead to memory exhaustion as model size grows, making them impractical for state-of-the-art LLMs. To overcome these limitations, we propose AttentionBreaker, a novel framework for efficient parameter space exploration, incorporating GenBFA, an evolutionary optimization method that identifies the most vulnerable bits in LLMs. Our approach demonstrates unprecedented efficacy—flipping just three bits in the LLaMA3-8B-Instruct model, quantized to 8-bit weights (W8), completely collapses performance, reducing Massive Multitask Language Understanding (MMLU) accuracy from 67.3% to 0% and increasing Wikitext perplexity by a factor of $10^5$. Furthermore, AttentionBreaker circumvents existing defenses against BFAs on transformer-based architectures, exposing a critical security risk. The framework is made open sourced at: https://github.com/TIES-Lab/attnbreaker.
URL: https://openreview.net/forum?id=2ekgTdBOZo
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New submissions
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Title: A Learning Law: Generalization via Geometric Complexity and Algebraic Capacity
Abstract: Modern machine learning systems achieve strong performance but remain data-hungry and opaque. We propose the \emph{Learning Law}, which asserts that effective learning follows the order \emph{form $\rightarrow$ law $\rightarrow$ data $\rightarrow$ understanding}. We formalize this by separating geometry discovery, law formation, and data calibration. The first stage learns a latent manifold with controlled intrinsic dimension and smoothness. The second restricts predictors to an algebraically constrained law space on this geometry. The third calibrates these laws on finite labeled data. We derive a Geometry–Algebra Generalization Bound showing that population risk depends on geometric complexity $\mathcal{C}(\phi)$ and algebraic capacity $\mathcal{A}(g)$, rather than raw parameter count, yielding intrinsic sample-efficiency advantages for geometry-first learning. A two-stage V-GIB implementation confirms these predictions on CIFAR-10 and a tabular classification task. Geometry-first pretraining lowers intrinsic dimension, improves low-label test accuracy, and outperforms data-first baselines once training stabilizes, with ablations isolating the roles of smoothness and intrinsic-dimension control.
URL: https://openreview.net/forum?id=EO1dAoql6K
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Title: Curvature-Aware Safety Restoration In LLMs Fine-Tuning
Abstract: Fine-tuning Large Language Models (LLMs) for downstream tasks often compromises safety alignment, even when using parameter-efficient methods like LoRA. In this work, we uncover a notable property: fine-tuned models preserve the geometric structure of their loss landscapes concerning harmful content, regardless of the fine-tuning method employed. This suggests that safety behaviors are not erased but shifted to less influential regions of the parameter space. Building on this insight, we propose a curvature-aware alignment restoration method that leverages influence functions and second-order optimization to selectively increase loss on harmful inputs while preserving task performance. By navigating the shared geometry between base and fine-tuned models, our method discourages unsafe outputs while preserving task-relevant performance, avoiding full reversion and enabling precise, low-impact updates. Extensive evaluations across multiple model families and adversarial settings show that our approach efficiently reduces harmful responses while maintaining or even improving utility and few-shot learning performance.
URL: https://openreview.net/forum?id=FSUehLhGyl
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Title: LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks
Abstract: Numerous real-world decisions rely on machine learning algorithms and require calibrated uncertainty estimates. However, modern methods often yield overconfident, uncalibrated predictions. The dominant approach to quantifying the uncertainty inherent in the model is to train an ensemble of separate predictors and measure their empirical variance. In an explicit implementation, the ensemble has a high computational cost and memory footprint, especially if the base model itself is already large, like modern transformers. This motivates efforts to develop implicit ensemble methods that emulate the ensemble without explicitly instantiating all its members. We introduce LoRA-Ensemble, a parameter-efficient ensembling method for self-attention networks. It is based on Low-Rank Adaptation (LoRA), originally developed for efficient LLM fine-tuning, and extends it into an implicit ensembling scheme, where all ensemble members share the same, pre-trained self-attention network, but have individual low-rank matrices for the attention projections. The resulting method not only outperforms state-of-the-art implicit techniques like BatchEnsemble, but even matches or exceeds the accuracy of an Explicit Ensemble, while at the same time achieving superior calibration.
URL: https://openreview.net/forum?id=yhXXmOMpSQ
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Title: Incentive Design for Multi-Agent Systems: A Bilevel Optimization Framework for Coordinating Independent Agents and Convergence Analysis
Abstract: Incentive design aims to guide the performance of a system towards a human's intention or preference. We study this problem in a multi-agent system with one leader and multiple followers. Each follower independently solves a \ac{mdp} to maximize its own expected total return with the same state space and action space. However, the leader’s objective depends on the collective best-response policies of all followers. To influence these policies of followers, the leader provides side payments as incentives to individual followers at a cost, aiming to align the collective behaviors of followers with its own goal while minimizing this cost of incentive. Such a leader-followers interaction is formulated as a bilevel optimization problem: the lower level consists of followers individually optimizing their MDPs given the side payments, and the upper level involves the leader optimizing its objective function given the followers' best responses. The main challenge to solve the incentive design is that the leader’s objective is generally non-concave and the lower level optimization problems can have multiple local optima. To this end, we employ a constrained optimization reformation of this bi-level optimization problem and develop an algorithm that provably converges to a stationary point of the original problem, by leveraging several smoothness properties of value functions in MDPs. We validate our algorithm in a stochastic gridworld by examining its convergence, verifying that the constraints are satisfied, and evaluating the improvement in the leader's performance.
URL: https://openreview.net/forum?id=MsrcGJdu8p
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Title: MaskFlow: Discrete Flows For Flexible and Efficient Long Video Generation
Abstract: Generating long, high-quality videos remains a challenge due to the complex interplay of spatial and temporal dynamics and hardware limitations. In this work, we introduce \textit{MaskFlow}, a unified video generation framework that combines discrete representations with flow-matching to enable efficient generation of high-quality long videos. By leveraging a frame-level masking strategy during training, MaskFlow conditions on previously generated unmasked frames to generate videos with lengths ten times beyond that of the training sequences. MaskFlow does so very efficiently by enabling the use of fast Masked Generative Model (MGM)-style sampling and can be deployed in both fully autoregressive and full-sequence generation modes. We validate the quality of our method on the FaceForensics (FFS) and Deepmind Lab (DMLab) datasets and report Fréchet Video Distance (FVD) competitive with state-of-the-art approaches. We also provide a detailed analysis on the sampling efficiency of our method and demonstrate that MaskFlow can be applied to both timestep-dependent and timestep-independent models in a training-free manner. Code will be released.
URL: https://openreview.net/forum?id=lo7fPALECP
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Title: Data Valuation in the Presence of Noisy Labels for Linear Models
Abstract: Parameter estimation is central to scientific inference, yet standard data collection practices, such as random sampling, often yield inefficient or suboptimal results when data are noisy, imbalanced, or expensive to obtain. In such settings, not all samples equally contribute to inference, motivating the need for principled methods to identify and prioritize the most informative data. We propose a data valuation framework based on Fisher information that quantifies each sample's contribution to the precision of parameter estimates. Unlike prediction performance-driven active learning, our method explicitly targets the improvement of inference precision rather than predictive generalization. By incorporating an adjusted Fisher Information metric, the framework naturally accounts for measurement noise and heteroscedasticity, assigning higher value to samples that most effectively reduce estimator variance. We provide theoretical guarantees for both linear and logistic regression, demonstrating faster convergence than CoreSet and BAIT approaches, with gains that scale logarithmically with the unlabeled pool size. Extensions to multivariate and non-Gaussian settings further show that parameter-focused data valuation offers a principled, efficient strategy for subset selection -- prioritizing the most informative observations under realistic, high-noise scientific conditions.
URL: https://openreview.net/forum?id=TNORVIkjqW
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