GenAI4ELab/papercli-papers huggingface dataset:
Single-Loop Byzantine-Resilient Federated Bilevel Optimization
Authors: Yangnan Li; Shenghui Song; Xuanyu Cao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated bilevel optimization plays a crucial role in solving complex problems with nested optimization structures. However, its distributed nature makes it highly susceptible to faulty or Byzantine behaviors. Existing Byzantine-resilient approaches are either restricted to simple single-level optimization problems or rely on sub-loop updates that introduce significant computational and communication overhead. To address these limitations, we propose a family of Byzantine-resilient federated bilevel algorithms, which (i) operate within a single-loop structure, (ii) achieve optimal Byzantine resilience, and (iii) ensure computational and communication efficiency. The core of the proposed method, BR-FedBi, leverages an auxiliary variable that facilitates efficient hypergradient estimation while simultaneously solving the lower- and upper-level problems. Building on BR-FedBi, we further integrate the algorithm with Polyak’s momentum and the probabilistic gradient estimator (PAGE) (Li et al., 2021), resulting in provable optimal Byzantine resilience and optimal sample complexity. Both theoretical analysis and empirical results demonstrate the superior performance of the proposed algorithms.
Uni-DPO: A Unified Paradigm for Dynamic Preference Optimization of LLMs
Authors: Shangpin Peng; Weinong Wang; Zhuotao Tian; Senqiao Yang; Xing W; Haotian Xu; Chengquan Zhang; Takashi Isobe; Baotian Hu; Min Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Direct Preference Optimization (DPO) has emerged as a cornerstone of reinforcement learning from human feedback (RLHF) due to its simplicity and efficiency. However, existing DPO-based methods typically treat all preference pairs equally, overlooking substantial variations in data quality and learning difficulty, which leads to inefficient data utilization and suboptimal performance. To address this limitation, we propose **Uni-DPO**, a unified dynamic preference optimization framework that jointly considers (a) the inherent quality of preference pairs and (b) the model's evolving performance during training. By adaptively reweighting samples based on both factors, Uni-DPO enables more effective use of preference data and achieves superior performance. Extensive experiments across models and benchmarks demonstrate the effectiveness and generalization of Uni-DPO. On textual tasks, Gemma-2-9B-IT fine-tuned with Uni-DPO surpasses the leading LLM, Claude 3 Opus, by 6.7 points on Arena-Hard. On mathematical and multimodal tasks, Uni-DPO consistently outperforms baseline methods across all benchmarks, providing strong empirical evidence of its effectiveness and robustness.
A Unified Total Variation Framework for Membrane Potential Perturbation Dynamic
Authors: Zhao-Rong Lai; Xiwen Yuan; Ziliang Chen; Liangda Fang; Yongsen Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Membrane potential perturbation dynamic (MPPD) is an emerging approach to capture perturbation intensity and stabilize the performance of spiking neural networks (SNN). It discards the neuronal reset part to intuitively reduce fluctuations of dynamics, but this treatment may be insufficient in perturbation characterization. In this study, we prove that MPPD is total variation (TV), which is a widely-used methodology for robust signal reconstruction. Moreover, we propose a novel TV-$\ell_1$ framework for MPPD, which allows for a wider range of network functions and has better denoising advantage than the existing TV-$\ell_2$ framework, based on the coarea formula. Experiments show that MPPD-TV-$\ell_1$ achieves robust performance in both Gaussian noise training and adversarial training for image classification tasks. This finding may provide a new insight into the essence of perturbation characterization.
On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
Authors: Aki Rehn; Linzh Zhao; Mikko A. Heikkilä; Antti Honkela
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Differentially private (DP) transfer learning, i.e., fine-tuning a pretrained model on private data, is the current state-of-the-art approach for training large models under privacy constraints. 
We focus on two key hyperparameters in this setting: the clipping bound $C$ and batch size $B$.
We show a clear mismatch between the current theoretical understanding of how to choose an optimal $C$ (stronger privacy requires smaller $C$) and empirical outcomes (larger $C$ performs better under strong privacy), caused by changes in the gradient distributions. 
Assuming a limited compute budget (fixed epochs), we demonstrate that the existing heuristics for tuning $B$ do not work, while cumulative DP noise better explains whether smaller or larger batches perform better. 
We also highlight how the common practice of using a single $(C,B)$ setting across tasks can lead to suboptimal performance. 
We find that performance drops especially when moving between loose and tight privacy and between plentiful and limited compute, which we explain by analyzing clipping as a form of gradient re-weighting and examining cumulative DP noise.
Visual Planning: Let's Think Only with Images
Authors: Yi Xu; Chengzu Li; Han Zhou; Xingchen Wan; Caiqi Zhang; Anna Korhonen; Ivan Vulić
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Recent advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have substantially enhanced machine reasoning across diverse tasks. However, these models predominantly rely on pure text as the medium for both expressing and structuring reasoning, even when visual information is present. In this work, we argue that language may not always be the most natural or effective modality for reasoning, particularly in tasks involving spatial and geometrical information. Motivated by this, we propose a new paradigm, Visual Planning, which enables planning through purely visual representations for these "vision-first'' tasks, as a supplementary channel to language-based reasoning. In this paradigm, planning is executed via sequences of images that encode step-by-step inference in the visual domain, akin to how humans sketch or visualize future actions. We introduce a novel reinforcement learning framework, Visual Planning via Reinforcement Learning (VPRL), empowered by GRPO for post-training large vision models, leading to substantial improvements in planning in a selection of representative visual navigation tasks, FrozenLake, Maze, and MiniBehavior. Our visual planning paradigm outperforms all other planning variants that conduct reasoning in the text-only space. Our results establish Visual Planning as a viable and promising supplement to language-based reasoning, opening new avenues for tasks that benefit from intuitive, image-based inference.
Variational Autoencoding Discrete Diffusion with Enhanced Dimensional Correlations Modeling
Authors: Tianyu Xie; Shuchen Xue; Zijin Feng; Tianyang Hu; Jiacheng Sun; Zhenguo Li; Cheng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Discrete diffusion models have recently shown great promise for modeling complex discrete data, with masked diffusion models (MDMs) offering a compelling trade-off between quality and generation speed. MDMs denoise by progressively unmasking multiple dimensions from an all-masked input, but their performance can degrade when using few denoising steps due to limited modeling of inter-dimensional dependencies. In this paper, we propose Variational Autoencoding Discrete Diffusion (VADD), a novel framework that enhances discrete diffusion with latent variable modeling to implicitly capture correlations among dimensions. By introducing an auxiliary recognition model, VADD enables stable training via variational lower bounds maximization and amortized inference over the training set. Our approach retains the efficiency of traditional MDMs while significantly improving sample quality, especially when the number of denoising steps is small. Empirical results on 2D toy data, pixel-level image generation, and text generation demonstrate that VADD consistently outperforms MDM baselines.
CortiLife: A Unified Framework for Cortical Representation Learning across the Lifespan
Authors: Pengcheng Xue; Dong Nie; Jie Luo; Daoqiang Zhang; Xuyun Wen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The human cerebral cortex encodes rich neurobiological information that is essential for understanding brain development, aging, and disease. Although various cortical representation learning methods have been proposed, existing models are typically restricted to stage-specific cohorts and lack generalization across the lifespan. While recent vision-language models offer a promising direction, building a unified framework for cortical representation faces three key challenges: (1) the non-Euclidean manifold structure of cortical surfaces, (2) homogenization of individual folding patterns induced by registration, and (3) distribution shifts of cortical features across the lifespan. To address these issues, we present CortiLife, the first unified vision-language framework for lifespan-aware cortical representation learning. Specifically, CortiLife introduces a surface tokenizer that integrates icosahedron-based surface patchification with multi-level patch encoding to transform complex cortical manifolds into compact token representations. The multi-level encoding incorporates three complementary streams that capture local topology, global interactions, and patch-wise distributional patterns, effectively mitigating the challenges of homogenization and distribution shifts. Furthermore, CortiLife integrates masked self-distillation with metadata language prompting, embedding information such as age, sex, health status, and attribution type into the text encoder to better capture individual-specific cortical representations while enabling both age-aware and modality-aware modeling. Extensive experiments on downstream tasks, including two encoder-frozen tasks (age prediction and cortical parcellation) and four encoder fine-tuning tasks (brain disorder diagnosis), demonstrate that CortiLife consistently outperforms state-of-the-art baselines across different age stages and modality types, underscoring its effectiveness and generalization ability.
CoDi: Subject-Consistent and Pose-Diverse Text-to-Image Generation
Authors: Zhanxin Gao; Beier Zhu; Liangyao; Jian Yang; Ying Tai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Subject-consistent generation (SCG)-aiming to maintain a consistent subject identity across diverse scenes-remains a challenge for text-to-image (T2I) models. Existing training-free SCG methods often achieve consistency at the cost of layout and pose diversity, hindering expressive visual storytelling. To address the limitation, we propose subject-Consistent and pose-Diverse T2I framework, dubbed as CoDi, that enables consistent subject generation with diverse pose and layout. Motivated by the progressive nature of diffusion, where coarse structures emerge early and fine details are refined later, CoDi adopts a two-stage strategy: Identity Transport (IT) and Identity Refinement (IR). IT operates in the early denoising steps, using optimal transport to transfer identity features to each target image in a pose-aware manner. This promotes subject consistency while preserving pose diversity. IR is applied in the later denoising steps, selecting the most salient identity features to further refine subject details. Extensive qualitative and quantitative results on subject consistency, pose diversity, and prompt fidelity demonstrate that CoDi achieves both better visual perception and stronger performance across all metrics. The code is provided in the supplementary material.
MILPnet: A Multi-Scale Architecture with Geometric Feature Sequence Representations for Advancing MILP Problems
Authors: Ruobing Wang; Xin Li; Mingzhong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose MILPnet, a multi-scale hybrid attention framework that models Mixed Integer Linear Programming (MILP) problems as geometric sequences rather than graphs. This approach directly addresses the challenge of Foldable MILP instances, a class of problems that graph-based models, specifically Graph Neural Networks (GNNs), fail to distinguish due to expressiveness limits imposed by the Weisfeiler-Lehman test. By representing MILPs through sequences of constraint and objective features, MILPnet captures both local and global geometric structure using a theoretically grounded multi-scale attention mechanism. We theoretically prove that MILPnet can approximate feasibility, optimal objective value, and optimal solution mappings over a measurable topological space with arbitrarily small error. Empirically, MILPnet outperforms graph-based methods in feasibility prediction accuracy and convergence speed on Foldable MILPs, while using significantly fewer parameters. It also generalizes effectively among problem scales and demonstrates strong performance on real-world MILP benchmarks when integrated into an end-to-end solver pipeline. Our code is available.
Causal Score Conditioning for Multi-Resolution Latent Systems
Authors: Xuechun Li; Shan Gao; Susu Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Complex causal systems with interdependent variables require inference from heterogeneous observations that vary in spatial resolution, temporal frequency, and noise characteristics due to data acquisition constraints. Existing multi-modal fusion approaches assume uniform data quality or complete observability -- assumptions often violated in real-world applications. Current methods face three limitations: they treat causally-related variables independently, failing to exploit causal relationships; they cannot integrate multi-resolution observations effectively; and they lack theoretical frameworks for cascaded approximation errors. We introduce the Score-based Variational Graphical Diffusion Model (SVGDM), which integrates score-based diffusion within causal graphical structures for inference under heterogeneous incomplete observations. SVGDM introduces causal score decomposition enabling information propagation across causally-connected variables while preserving original observation characteristics. Diffusion provides a natural way to model scale-dependent sensing noise, which is common in remote-sensing, climate, and physical measurement systems, while the causal graph encodes well-established mechanistic dependencies between latent processes. We provide theoretical analysis and demonstrate superior performance on both synthetic and real-world datasets compared to relevant baselines.
Adaptive Debiasing Tsallis Entropy for Test-Time Adaptation
Authors: Xiangyu Wu; Dongming Jiang; Feng Yu; Yueying Tian; Jiaqi Tang; Qing-Guo Chen; Yang Yang; Jianfeng Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mainstream Test-Time Adaptation (TTA) methods for adapting vision-language models, e.g., CLIP, typically rely on Shannon Entropy (SE) at test time to measure prediction uncertainty and inconsistency. However, since CLIP has a built-in bias from pretraining on highly imbalanced web-crawled data, SE inevitably results in producing biased estimates of uncertainty entropy. To address this issue, we notably find and demonstrate that Tsallis Entropy (TE), a generalized form of SE, is naturally suited for characterizing biased distributions by introducing a non-extensive parameter q, with the performance of SE serving as a lower bound for TE. Building upon this, we generalize TE into Adaptive Debiasing Tsallis Entropy (ADTE) for TTA, customizing a class-specific parameter q^l derived by normalizing the estimated label bias from continuously incoming test instances, for each category. This adaptive approach allows ADTE, even without hyperparameter tuning required by TE, to accurately select high-confidence views and seamlessly integrate with label adjustment strategy to enhance adaptation. Besides, our investigation reveals that both TE and ADTE can serve as direct, advanced alternatives to SE in TTA, without any other modifications. Experimental results show that ADTE outperforms state-of-the-art methods on ImageNet and its five variants, and achieves the highest average performance on 10 cross-domain benchmarks, regardless of the model architecture or text prompts used. Our code is available at https://anonymous.4open.science/r/TTA-Entropy.
Policy Newton Algorithm in Reproducing Kernel Hilbert Space
Authors: Yixian Zhang; Huaze Tang; Changxu Wei; Chao Wang; Wenbo Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning (RL) policies represented in Reproducing Kernel Hilbert Spaces (RKHS) offer powerful representational capabilities. While second-order optimization methods like Newton's method demonstrate faster convergence than first-order approaches, current RKHS-based policy optimization remains constrained to first-order techniques. This limitation stems primarily from the intractability of explicitly computing and inverting the infinite-dimensional Hessian operator in RKHS. We introduce Policy Newton in RKHS, the first second-order optimization framework specifically designed for RL policies represented in RKHS. Our approach circumvents direct computation of the inverse Hessian operator by optimizing a cubic regularized auxiliary objective function. Crucially, we leverage the Representer Theorem to transform this infinite-dimensional optimization into an equivalent, computationally tractable finite-dimensional problem whose dimensionality scales with the trajectory data volume. We establish theoretical guarantees proving convergence to a local optimum with a local quadratic convergence rate. Empirical evaluations on a toy financial asset allocation problem validate these theoretical properties, while experiments on standard RL benchmarks demonstrate that Policy Newton in RKHS achieves superior convergence speed and higher episodic rewards compared to established first-order RKHS approaches and parametric second-order methods. Our work bridges a critical gap between non-parametric policy representations and second-order optimization methods in reinforcement learning.
TIPO: Text to Image with Text Pre-sampling for Prompt Optimization
Authors: Shih-Ying Yeh; YI LI; SangHyun Park; Giyeong Oh; Xuehai Wang; Min Song; Youngjae Yu; Shang-Hong Lai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer, detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or reinforcement learning (RL), TIPO provides computational efficiency and scalability, opening new possibilities for effective, automated prompt engineering in T2I tasks.

We provide visual results, human preference report to investigate TIPO's effectiveness. Experimental evaluations on benchmark datasets demonstrate substantial improvements in aesthetic quality, significant reduction of visual artifacts, and enhanced alignment with target distributions along with significant human preference proficiency. These results highlight the importance of targeted prompt engineering in text-to-image tasks and indicate broader opportunities for automated prompt refinement.
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
Authors: Taishi Nakamura; Satoki Ishikawa; Masaki Kawamura; Takumi Okamoto; Daisuke Nohara; Jun Suzuki; Rio Yokota
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Empirical scaling laws have driven the evolution of large language models (LLMs), yet their coefficients shift whenever the model architecture or data pipeline changes.
Mixture‑of‑Experts (MoE) models, now standard in state‑of‑the‑art systems, introduce a new sparsity dimension that current dense‑model frontiers overlook.
We investigate how MoE sparsity influences two distinct capability regimes: memorization skills and reasoning skills.
By training MoE families that vary total parameters, active parameters, and top-$k$ routing under fixed compute budgets, we disentangle pre-training loss from downstream accuracy. 
Our results reveal two principles. First, Active FLOPs: models with identical training loss but greater active compute achieve higher reasoning accuracy. Second, Total tokens per parameter (TPP): memorization tasks improve with more parameters, while reasoning tasks benefit from optimal TPP, indicating that reasoning is data-hungry. 
Neither reinforcement learning post-training (GRPO) nor increased test-time compute alters these trends. 
We therefore argue that optimal MoE sparsity must be determined jointly by active FLOPs and TPP, revising the classical picture of compute-optimal scaling. 
All code, data sources, and logs are released to facilitate reproducibility and future work.
Unified In-Context Video Editing
Authors: Zixuan Ye; Xuanhua He; Quande Liu; Qiulin Wang; Xintao Wang; Pengfei Wan; Di ZHANG; Kun Gai; Qifeng Chen; Wenhan Luo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional adapter modules) or dedicated customizations (e.g., DDIM inversion), which limit the integration of versatile editing conditions and the unification of various editing tasks. In this paper, we introduce UNified In-Context Video Editing (UNIC), a simple yet effective framework that unifies diverse video editing tasks within a single model in an in-context manner. To achieve this unification, we represent the inputs of various video editing tasks as three types of tokens: the source video tokens, the noisy video latent, and the multi-modal conditioning tokens that vary according to the specific editing task. Based on this formulation, our key insight is to integrate these three types into a single consecutive token sequence and jointly model them using the native attention operations of DiT, thereby eliminating the need for task-specific adapter designs. Nevertheless, direct task unification under this framework is challenging, leading to severe token collisions and task confusion due to the varying video lengths and diverse condition modalities across tasks. To address these, we introduce task-aware RoPE to facilitate consistent temporal positional encoding, and condition bias that enables the model to clearly differentiate different editing tasks. This allows our approach to adaptively perform different video editing tasks by referring the source video and varying condition tokens "in context", and support flexible task composition. To validate our method, we construct a unified video editing benchmark containing six representative video editing tasks. Results demonstrate that our unified approach achieves comparable performance with task specialists and exhibits emergent task composition abilities.