GenAI4ELab/papercli-papers huggingface dataset:
Beyond Spectra: Eigenvector Overlaps in Loss Geometry
Authors: Gabriel Mel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Local loss geometry in machine learning is inherently a two-operator concept. While a single loss is locally characterized by its Hessian spectrum, practical learning depends on both training and test losses, whose joint geometry is determined not only by their spectra but by the alignment of their eigenspaces. We establish general foundations for this two-loss geometry by deriving a universal local fluctuation law: the expected test-loss increment under small training perturbations is a trace combining train and test spectral data with a precise factor quantifying eigenvector overlap. We further prove a transfer law describing how overlaps transform under noise. As a solvable model, we apply these results to ridge regression under arbitrary covariate shift, where operator-valued free probability yields asymptotically exact overlap decompositions that identify overlaps as the natural quantities for specifying shift, and resolve multiple descent: error peaks are governed by eigenspace misalignment rather than Hessian ill-conditioning alone. We then validate the fluctuation law in multilayer perceptrons, develop scalable estimators for overlap functionals based on subspace iteration and kernel polynomial methods, and apply them to a ResNet-20 trained on CIFAR-10, showing that class imbalance reshapes train–test geometry through induced misalignment. Together, these results establish eigenvector overlaps as the fundamental missing ingredient in local loss geometry, providing both theoretical foundations and practical tools for analyzing generalization in modern neural networks.
Knowledge Distillation for Large Language Models through Residual Learning
Authors: Thinh On; Hengzhi Pei; Leonard Lausen; George Karypis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge distillation has become a crucial technique to transfer the capacities of large language models (LLMs) to smaller, more efficient models for practical deployment. While recent work exploits rich information from intermediate states of the teacher model for more effective knowledge transfer, imperfect knowledge from the teacher can also mislead student learning, restricting the student’s generalization capacity. In this work, we propose a two-stage distillation framework that is effective for diverse knowledge distillation scenarios. In the first stage, we pretrain projectors to extract and compress teacher knowledge into a low-dimensional vector space via self-reconstruction. In the second stage, we perform distillation with a hybrid objective that combines learning from the compressed teacher representations with standard supervised fine-tuning on ground-truth data. Our key innovation is residual learning for LLM distillation, where the student learns to make predictions based on the differential between its representations and projected states from the teacher. This approach encourages the student to further improve its representations beyond potentially erroneous teacher knowledge. For Mixture-of-Experts (MoE) teacher models, we further fuse the experts’ outputs using a self-attention mechanism for better utilizing the teacher knowledge. Moreover, to support the cross-tokenizer distillation setting, where the teacher and student models have different vocabularies, we adopt a cross-model attention mechanism that eliminates the need for explicit token alignment rules. Experimental results show the superior performance of our proposed framework under both same- and cross-tokenizer settings, demonstrating the effectiveness in preserving teacher knowledge and improving student generalization capability.
DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models
Authors: Mor Ventura; Michael Toker; Or Patashnik; Yonatan Belinkov; Roi Reichart
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Text-to-Image (T2I) models have advanced rapidly, yet they remain vulnerable to semantic leakage, the unintended transfer of semantically related features between distinct entities. Existing mitigation strategies are often optimization-based or dependent on external inputs. We introduce **DeLeaker**, a lightweight, optimization-free inference-time approach that mitigates leakage by directly intervening on the model’s attention maps. Throughout the diffusion process, DeLeaker dynamically reweights attention maps to suppress excessive cross-entity interactions while strengthening the identity of each entity. To support systematic evaluation, we introduce **SLIM** (Semantic Leakage in IMages), the first dataset dedicated to semantic leakage, comprising 1,130 human-verified samples spanning diverse scenarios, together with a novel automatic evaluation framework. Experiments demonstrate that DeLeaker consistently outperforms all baselines, even when they are provided with external information, achieving effective leakage mitigation without compromising fidelity or quality. These results underscore the value of attention control and pave the way for more semantically precise T2I models.
Efficient Autoregressive Inference for Transformer Probabilistic Models
Authors: Conor Hassan; Nasrulloh Ratu Bagus Satrio Loka; Cen-You Li; Daolang Huang; Paul Edmund Chang; Yang Yang; Francesco Silvestrin; Samuel Kaski; Luigi Acerbi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tabular foundation models, excel at single-pass _marginal_ prediction. However, many applications require _joint distributions_ over multiple predictions. Purely autoregressive architectures generate
these efficiently but sacrifice flexible set-conditioning. Obtaining joint distributions from set-based models requires re-encoding the entire context at each autoregressive step, which scales poorly. We introduce a _causal autoregressive buffer_ that combines the strengths of both paradigms. The model encodes the context once and caches it; a lightweight causal buffer captures dependencies among generated targets, with each new prediction attending to both the cached context and all previously predicted targets added to the buffer. This enables efficient batched autoregressive sampling and joint predictive density evaluation. Training integrates set-based and autoregressive modes through masked attention at minimal overhead. Across synthetic functions, EEG time series, a Bayesian model comparison task, and tabular regression, our method closely matches the performance of full context re-encoding while delivering up to $20\times$ faster joint sampling and density evaluation, and up to $7\times$ lower memory usage.
Multi-modal Data Spectrum: Multi-modal Datasets are Multi-dimensional
Authors: Divyam Madaan; Varshan Muhunthan; Kyunghyun Cho; Sumit Chopra
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding the interplay between intra-modality dependencies (the contribution of an individual modality to a target task) and inter-modality dependencies (the relationships between modalities and the target task) is fundamental to advancing multi-modal learning. However, the nature of and interaction between these dependencies within current benchmark evaluations remains poorly characterized. In this work, we present a large-scale empirical study to quantify these dependencies across 23 visual question-answering benchmarks using multi-modal large language models (MLLMs) covering domains such as general and expert knowledge reasoning, optical character recognition, and document understanding. Our findings show that the reliance on vision, question (text), and their interaction varies significantly, both across and within benchmarks. We discover that numerous benchmarks intended to mitigate text-only biases have inadvertently amplified image-only dependencies. This characterization persists across model sizes and types, with models often obtaining high performance by using each modality independently and showing limited dependence on their interaction. We provide a quantitative characterization of multi-modal datasets, enabling a principled approach to multi-modal benchmark design and evaluation.
True Self-Supervised Novel View Synthesis is Transferable
Authors: Thomas Mitchel; Hyunwoo Ryu; Vincent Sitzmann
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: In this paper, we identify that the key criterion for determining whether a model is truly capable of novel view synthesis (NVS) is transferability: Whether any pose representation extracted from one video sequence can be used to re-render the same camera trajectory in another. We analyze prior work on self-supervised NVS and find that their predicted poses do not transfer: The same set of poses lead
to different camera trajectories in different 3D scenes. Here, we present XFactor, the first geometry-free self-supervised model capable of true NVS. XFactor combines pair-wise pose estimation with a simple augmentation scheme of the inputs and outputs that jointly enables disentangling camera pose from scene content and facilitates geometric reasoning. Remarkably, we show that XFactor achieves transferability with unconstrained latent pose variables, without any 3D inductive biases or concepts from multi-view geometry — such as an explicit parameterization of poses as elements of SE(3). We introduce a new metric to quantify transferability, and through large-scale experiments, we demonstrate that XFactor significantly outperforms prior pose-free NVS transformers, and show that latent poses are highly correlated with real-world poses through probing experiments.
Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning
Authors: Siyang Li; Yize Chen; Yan Guo; Ming Huang; Hui Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Advanced deep learning-based approaches have been actively applied to forecast the spatiotemporal physical dynamics governed by partial differential equations (PDEs), which acts as a critical procedure in tackling many science and engineering problems. As real-world physical environments like PDE system parameters are always capricious, how to generalize across unseen out-of-distribution (OOD) forecasting scenarios using limited training data is of great importance. To bridge this barrier, existing methods focus on discovering domain-generalizable representations across various PDE dynamics trajectories. However, their zero-shot OOD generalization capability remains deficient, since extra test-time samples for domain-specific adaptation are still required. This is because the fundamental physical invariance in PDE dynamical systems are yet to be investigated or integrated. To this end, we first explicitly define a two-fold PDE invariance principle, which points out that ingredient operators and their composition relationships remain invariant across different domains and PDE system evolution. Next, to capture this two-fold PDE invariance, we propose a physics-guided invariant learning method termed iMOOE, featuring an Invariance-aligned Mixture Of Operator Expert architecture and a frequency-enriched invariant learning objective. Extensive experiments across simulated benchmarks and real-world applications validate iMOOE's superior in-distribution performance and zero-shot generalization capabilities on diverse OOD forecasting scenarios.
Only Brains Align with Brains: Cross-Region Alignment Patterns Expose Limits of Normative Models
Authors: Larissa Höfling; Matthias Tangemann; Lotta Piefke; Susanne Keller; Matthias Bethge; Katrin Franke
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neuroscientists and computer vision researchers use model–brain alignment benchmarks to compare artificial and biological vision systems. These benchmarks rank models according to alignment measures such as the similarity of representational geometry or the predictability of neural responses from model activations. However, recent works have identified a number of problems with these rankings, among them their lack of discriminative power and robustness, raising the conceptual question of what it means for a model to be 'brain-aligned'.
Here we introduce *alignment patterns* - characteristic functional relationship profiles of each brain region to all others - and propose that models should reproduce these patterns to qualify as brain-aligned. 
First, we apply a standard benchmarking pipeline to a broad spectrum of vision models on the BOLD Moments video fMRI dataset across visual regions of interest (ROIs). 
We find diverse models appear *equivalent* in their brain alignment, reflecting the lack of discriminative power of conventional alignment benchmarking pipelines based on pointwise, i.e. ROI-layer, comparisons.
In contrast, *alignment pattern analysis (APA)* is a second-order structural consistency test: a model aligned to a given ROI should reproduce that ROI’s characteristic cross-region alignment profile. 
Applying APA, we find that, while these patterns are highly stable across brains of different subjects, even top-ranked models often fail to capture them. Notably, models that appear effectively equivalent in their pointwise alignment to an ROI diverge sharply under the relational criterion, demonstrating the added discriminative value of APA.
Finally, we argue for a clearer distinction between the criteria a model must meet to serve as a tool versus as a computational model for human visual cortex. Conventional alignment measures may be sufficient for identifying neurally predictive models, but claims about computational or algorithmic similarity may require a stronger basis of evidence, including the reproducibility of relational alignment patterns.
Temporal Generalization: A Reality Check
Authors: Divyam Madaan; Sumit Chopra; Kyunghyun Cho
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Machine learning (ML) models often struggle to maintain performance under distribution shifts, leading to inaccurate predictions on unseen future data. In this work, we investigate whether and under what conditions models can achieve such a generalization when relying solely on past data. We explore two primary approaches: convex combinations of past model parameters (parameter interpolation) and explicit extrapolation beyond the convex hull of past parameters (parameter extrapolation). We benchmark several methods within these categories on a diverse set of temporal tasks, including language modeling, news summarization, news tag prediction, academic paper categorization, satellite image-based land use classification over time, and historical yearbook photo gender prediction. Our empirical findings show that none of the evaluated methods consistently outperforms the simple baseline of using the latest available model parameters in all scenarios. In the absence of access to future data or robust assumptions about the underlying data-generating process, these results underscore the inherent difficulties of generalizing and extrapolating to future data and warrant caution when evaluating claims of such generalization.
SUSD: Structured Unsupervised Skill Discovery through State Factorization
Authors: Seyed Mohammad Hadi Hosseini; Mahdieh Soleymani Baghshah
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards. One of the most common USD approaches is to maximize the Mutual Information (MI) between skill latent variables and states. However, MI-based methods tend to favor simple, static skills due to their invariance properties, limiting the discovery of dynamic, task-relevant behaviors. Distance-Maximizing Skill Discovery (DSD) promotes more dynamic skills by leveraging state-space distances, yet still fall short in encouraging comprehensive skill sets that engage all controllable factors or entities in the environment. 
In this work, we introduce SUSD, a novel framework that harnesses the compositional structure of environments by factorizing the state space into independent components (e.g., objects or controllable entities). SUSD allocates distinct skill variables to different factors, enabling more fine-grained control on the skill discovery process. A dynamic model also tracks learning across factors, adaptively steering the agent’s focus toward underexplored factors.
This structured approach not only promotes the discovery of richer and more diverse skills, but also yields a factorized skill representation that enables fine-grained and disentangled control over individual entities which facilitates efficient training of compositional downstream tasks via Hierarchical Reinforcement Learning (HRL).
Our experimental results across three environments, with factors ranging from 1 to 10, demonstrate that our method can discover diverse and complex skills without supervision, significantly outperforming existing unsupervised skill discovery methods in factorized and complex environments. Code is available at the anonymous repository: [https://anonymous.4open.science/r/SUSD](https://anonymous.4open.science/r/SUSD).
Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
Authors: Daqian Shao; Thomas Kleine Buening; Marta Kwiatkowska
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a general framework for causal Imitation Learning (IL) with hidden confounders, which subsumes several existing settings. Our framework accounts for two types of hidden confounders: (a) variables observed by the expert but not by the imitator, and (b) confounding noise hidden from both. By leveraging trajectory histories as instruments, we reformulate causal IL in our framework into a Conditional Moment Restriction (CMR) problem. We propose DML-IL, an algorithm that solves this CMR problem via instrumental variable regression, and upper bound its imitation gap. Empirical evaluation on continuous state-action environments, including Mujoco tasks, demonstrates that DML-IL outperforms existing causal IL baselines.
Information-based Value Iteration Networks for Decision Making Under Uncertainty
Authors: Cynthia Chen; Samantha Johnson; Cindy Poo; Michael A Buice; Koosha Khalvati
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks that incorporate classic reinforcement learning methods, such as value iteration, into their structure significantly outperform randomly structured networks in learning and generalization. These networks, however, are mostly limited to environments with no or very low uncertainty and do not extend well to partially observable environments. In this paper, we propose a new planning module architecture, the VI$^2$N (Value Iteration with Value of Information Network), that learns to act in novel environments with high perceptual ambiguity. This architecture over-emphasizes reducing uncertainty before exploiting the reward. VI$^2$N can also utilize factorization in environments with mixed observability to decrease the computational complexity of calculating the policy and to facilitate learning. Tested on a range of grid-based navigation tasks, each containing various types of environments with different degrees of observability, our network outperforms other deep architectures. Moreover, VI$^2$N generates interpretable cognitive maps highlighting both rewarding and informative locations. These maps highlight the key states the agent must visit to achieve its goal.
Query-Level Uncertainty in Large Language Models
Authors: Lihu Chen; Gerard de Melo; Fabian M. Suchanek; Gaël Varoquaux
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: It is important for Large Language Models (LLMs) to be aware of the boundary of their knowledge, i.e., the mechanism of identifying known and unknown queries. This type of awareness enables models to perform adaptive inference, such as invoking retrieval-augmented generation (RAG), engaging in slow and deep thinking, or abstaining from answering when appropriate. These mechanisms are beneficial to the development of efficient and trustworthy AI. 
In this work, we propose a method to detect knowledge boundaries via \textbf{\emph{Query-Level Uncertainty }}, which estimates if a model is capable of to answering a given query before generating any tokens. 
To this end, we propose a novel, training-free method called \textbf{\emph{Internal Confidence}}, which leverages self-evaluations across layers and tokens to provide a reliable signal of uncertainty.
Empirical studies on both factual question answering and mathematical reasoning tasks demonstrate that our internal confidence can outperform several baselines. Furthermore, we showcase that our proposed method can be used for adaptive inference,  such as efficient RAG and model cascading, thereby reducing inference costs while preserving overall performance.
Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models
Authors: Dang Nguyen; Jiping Li; Jinghao Zheng; Baharan Mirzasoleiman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Synthetically augmenting training datasets with diffusion models has become an effective strategy for improving the generalization of image classifiers. However, existing approaches typically increase dataset size by 10–30× and struggle to ensure generation diversity, leading to substantial computational overhead. In this work, we introduce TADA (**TA**rgeted **D**iffusion **A**ugmentation), a principled framework that selectively augments examples that are not learned early in training using faithful synthetic images that preserve semantic features while varying noise. We show that augmenting only this targeted subset consistently outperforms augmenting the entire dataset. Through theoretical analysis on a two-layer CNN, we prove that TADA improves generalization by promoting homogeneity in feature learning speed without amplifying noise. Extensive experiments demonstrate that by augmenting only 30–40% of the training data, TADA improves generalization by up to 2.8% across diverse architectures including ResNet, ViT, ConvNeXt, and Swin Transformer on CIFAR-10/100, TinyImageNet, and ImageNet, using optimizers such as SGD and SAM. Notably, TADA combined with SGD outperforms the state-of-the-art optimizer SAM on CIFAR-100 and TinyImageNet. Furthermore, TADA shows promising improvements on object detection benchmarks, demonstrating its applicability beyond image classification. Our code is available at https://github.com/BigML-CS-UCLA/TADA.