GenAI4ELab/papercli-papers huggingface dataset:
Understanding the Dynamics of Forgetting and Generalization in Continual Learning via the Neural Tangent Kernel
Authors: Guodong Zheng; Peng Wang; Shengchao Hu; Quan Zheng; Li Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Continual learning (CL) enables models to acquire new tasks sequentially while retaining previously learned knowledge. 
However, most theoretical analyses focus on simplified, converged models or restrictive data distributions and therefore fail to capture how forgetting and generalization evolve during training in more general settings. 
Current theory faces two fundamental challenges: (i) analyses confined to the converged regime cannot characterize intermediate training dynamics; and (ii) establishing forgetting bounds requires two-sided bounds on the population risk for each task. 
To address these challenges, we analyze the training-time dynamics of forgetting and generalization in standard CL within the Neural Tangent Kernel (NTK) regime, showing that decreasing the loss’s Lipschitz constant and minimizing the cross-task kernel jointly reduce forgetting and improve generalization. 
Specifically, we (i) characterize intermediate training stages via kernel gradient flow and (ii) employ Rademacher complexity to derive both upper and lower bounds on population risk. 
Building on these insights, we propose \emph{OGD+}, which projects the current task’s gradient onto the orthogonal complement of the subspace spanned by gradients of the most recent task evaluated on all prior samples. 
We further introduce \emph{Orthogonal Penalized Gradient Descent} (OPGD), which augments OGD+ with gradient-norm penalization to jointly reduce forgetting and enhance generalization. 
Experiments on multiple benchmarks corroborate our theoretical predictions and demonstrate the effectiveness of OPGD, providing a principled pathway from theory to algorithm design in CL.
AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Difference
Authors: Jing Yao; Shitong Duan; Xiaoyuan Yi; Dongkuan Xu; Peng Zhang; Tun Lu; Ning Gu; Zhicheng Dou; Xing Xie
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Assessing Large Language Models’ (LLMs) underlying value differences enables comprehensive comparison of their misalignment, cultural adaptability, and biases. Nevertheless, current value measurement methods face the informativeness challenge: with often outdated, contaminated, or generic test questions, they can only capture the orientations on comment safety values, e.g., HHH, shared among different LLMs, leading to indistinguishable and uninformative results. To address this problem, we introduce AdAEM, a novel, self-extensible evaluation algorithm for revealing LLMs’ inclinations. Distinct from static benchmarks, AdAEM automatically and adaptively generates and extends its test questions. This is achieved by probing the internal value boundaries of a diverse set of LLMs developed across cultures and time periods in an in-context optimization manner. Such a process theoretically maximizes an information-theoretic objective to extract diverse controversial topics that can provide more distinguishable and informative insights about models’ value differences. In this way, AdAEM is able to co-evolve with the development of LLMs, consistently tracking their value dynamics. We use AdAEM to generate novel questions and conduct an extensive analysis, demonstrating our method’s validity and effectiveness, laying the groundwork for better interdisciplinary research on LLMs’ values and alignment. Codes and the generated evaluation questions are released at https://github.com/ValueCompass/AdAEM.
DUET: Optimizing LLM Training Data Mixtures via Noisy Feedback from Unseen, Downstream Evaluation Tasks
Authors: Zhiliang Chen; Gregory Kang Ruey Lau; Chuan-Sheng Foo; Bryan Kian Hsiang Low
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The performance of an LLM depends heavily on how well the training data matches the downstream evaluation task. However, in many practical settings, we typically do not know the data in the evaluation task (e.g., conversations between a chatbot and users are end-to-end encrypted). We refer to such tasks as unseen evaluation tasks. We can only deploy the LLM on these unseen evaluation tasks to gather multiple rounds of feedback on how well the model performs (e.g., gathering user ratings from a chatbot). In addition, this feedback can be noisy. How can we exploit such noisy feedback efficiently to optimize the LLM training data-mixture? Our paper presents DUET, a novel global-to-local algorithm that optimizes training data mixtures by interleaving data selection with Bayesian optimization to exploit coarse and noisy feedback from a downstream evaluation task. DUET is flexible enough to incorporate different data selection methods, each with different performance-compute tradeoffs. By analyzing DUET's cumulative regret, we theoretically show that DUET converges to the optimal training data mixture even without any fine-grained data information from an unseen task. Finally, our experiments across a variety of language tasks demonstrate that DUET attains substantial performance improvements over existing data selection and mixing methods in the unseen-task setting. Our library, which is flexible enough to optimize different LLM training ingredients, can be found at  https://github.com/chenzhiliang94/BO-for-LLM.
Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
Authors: Minhyuk Seo; Taeheon Kim; Hankook Lee; Jonghyun Choi; Tinne Tuytelaars
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As AI becomes more personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases.
Personalized federated learning (PFL) enables each client to collaboratively leverage other clients' knowledge for better adaptation to the task of interest, without privacy risks. Despite its potential, existing PFL methods remain confined to rather simplified scenarios where data and models are the same across clients. To move towards realistic scenarios, we move beyond these restrictive assumptions by addressing both data and model heterogeneity. We propose a task-relevance-aware model aggregation strategy to reduce parameter interference under heterogeneous data. Moreover, we introduce Co-LoRA, a dimension-invariant module that enables knowledge sharing across heterogeneous architectures. To mimic the real-world task diversity, we propose a multi-modal PFL benchmark spanning 40 distinct tasks with distribution shifts over time. Extensive experiments shows that our proposed method significantly outperforms the state-of-the-art PFL methods under heterogeneous scenarios. Code is available at https://github.com/snumprlab/fedmosaic.
From Evaluation to Defense: Advancing Safety in Video Large Language Models
Authors: Yiwei Sun; Peiqi Jiang; Chuanbin Liu; Luohao Lin; Zhiying Lu; Hongtao Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While the safety risks of image-based large language models (Image LLMs) have been extensively studied, their video-based counterparts (Video LLMs) remain critically under-examined. To systematically study this problem, we introduce \textbf{VideoSafetyEval} -- a large-scale, real-world benchmark for Video LLM safety, which comprises 11.4k video-query pairs and spans 19 principal risk categories. Based on this, \textit{we reveal that integrating video modality degrades safety performance by an average of 34.2\%, thereby exposing systemic risks in multimodal attack exploitation.}
To address this vulnerability, we propose \textbf{VideoSafety-R1}, a dual-stage framework achieving unprecedented safety gains through three innovations: (1) VideoSafetyThinking dataset contains 46k video-query–thinking response triplets.  (2) Alarm Token-Guided Safety Fine-Tuning (AT-SFT) injects learnable alarm tokens into visual and textual sequences, enabling explicit harm perception across modalities via multitask objectives. (3) Safety-guided GRPO enhances defensive reasoning through dynamic policy optimization with rule-based rewards derived from dual-modality verification. These components synergize to shift safety alignment from harm perception to active reasoning. The framework achieves a 71.1\% improvement on VSE-HH, and improves by 59.1\%, 44.3\%, and 15.0\% on the image safety datasets MMBench, VLGuard, and FigStep, respectively. Our code and dataset are  available at \url{https://github.com/Emiya-syw/VideoSafety-R1.git}.
\textcolor{red}{Note: This paper contains harmful language and image examples, and reader discretion is recommended.}
InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models
Authors: Yuchen Yan; Yongliang Shen; Yang Liu; Jin Jiang; Mengdi Zhang; Jian Shao; Yueting Zhuang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Advanced reasoning in large language models has achieved remarkable performance on challenging tasks, but the prevailing long-context reasoning paradigm faces critical limitations: quadratic computational scaling with sequence length, reasoning constrained by maximum context boundaries, and performance degradation beyond pre-training context windows. Existing approaches primarily compress reasoning chains without addressing the fundamental scaling problem.
To overcome these challenges, we introduce InftyThink, a paradigm that transforms monolithic reasoning into an iterative process with intermediate summarization. By interleaving short reasoning segments with concise progress summaries, our approach enables unbounded reasoning depth while maintaining bounded computational costs. This creates a characteristic sawtooth memory pattern that significantly reduces computational complexity compared to traditional approaches.
Furthermore, we develop a methodology for reconstructing long-context reasoning datasets into our iterative format, transforming OpenR1-Math into 333K training instances. Experiments across multiple model architectures demonstrate that our approach reduces computational costs while improving performance, with Qwen2.5-Math-7B showing 3-11% improvements across MATH500, AIME24, and GPQA_diamond benchmarks. Our work challenges the assumed trade-off between reasoning depth and computational efficiency, providing a more scalable approach to complex reasoning without architectural modifications.
Diffusion Transformers with Representation Autoencoders
Authors: Boyang Zheng; Nanye Ma; Shengbang Tong; Saining Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Latent generative modeling has become the standard strategy for Diffusion Transformers (DiTs), but the autoencoder has barely evolved. Most DiTs still use the legacy VAE encoder, which introduces several limitations: large convolutional backbones that compromise architectural simplicity, low-dimensional latent spaces that restrict information capacity, and weak representations resulting from purely reconstruction-based training. In this work, we investigate replacing the VAE encoder–decoder with pretrained representation encoders (e.g., DINO, SigLIP, MAE) combined with trained decoders, forming what we call \emph{Representation Autoencoders} (RAEs). These models provide both high-quality reconstructions and semantically rich latent spaces, while allowing for a scalable transformer-based architecture. A key challenge arises in enabling diffusion transformers to operate effectively within these high-dimensional representations. We analyze the sources of this difficulty, propose theoretically motivated solutions, and validate them empirically. Our approach achieves faster convergence without auxiliary representation alignment losses. Using a DiT variant with a lightweight wide DDT-head, we demonstrate state-of-the-art image generation performance, reaching FIDs of 1.18 @256 resolution and 1.13 @512 on ImageNet.
Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
Authors: Zheng Zhou; Yu-Jie Xiong; Jia-Chen Zhang; Chun-Ming Xia; Xihe Qiu; Hongjian Zhan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs when persistent large Gaussians cannot meet adaptive splitting thresholds during density control. This is exacerbated by conflicting gradient directions that prevent effective splitting of these Gaussians; (2) Over-densification of Gaussians occurs in regions with aligned gradient aggregation, leading to redundant component proliferation. This redundancy significantly increases memory overhead due to unnecessary data retention. We present Gradient-Direction-Aware Gaussian Splatting (GDAGS) to address these challenges. Our key innovations: the Gradient Coherence Ratio (GCR), computed through normalized gradient vector norms, which explicitly discriminates Gaussians with concordant versus conflicting gradient directions; and a nonlinear dynamic weighting mechanism leverages the GCR to enable gradient-direction-aware density control. Specifically, GDAGS prioritizes conflicting-gradient Gaussians during splitting operations to enhance geometric details while suppressing redundant concordant-direction Gaussians. Conversely, in cloning processes, GDAGS promotes concordant-direction Gaussian densification for structural completion while preventing conflicting-direction Gaussian overpopulation. Comprehensive evaluations across diverse real-world benchmarks demonstrate that GDAGS achieves superior rendering quality while effectively mitigating over-reconstruction, suppressing over-densification, and constructing compact scene representations.
Riesz Neural Operator for Solving Partial Differential Equations
Authors: Shouyi Liu; Xiaokang Yang; Yuntian Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Local non-stationarity is pivotal to solving partial differential equations (PDEs). However, in operator learning, the spatially local information inherent in the data is often overlooked. Even when explicitly modeled, it is usually collapsed into local superpositions within the model architecture, preventing full exploitation of local features in physical phenomena. To address this limitation, our paper proposes a novel Riesz Neural Operator (RNO) based on the spectral derivative representation. Since PDEs are fundamentally governed by local derivatives, RNO leverages the Riesz transform, a natural spectral representation of derivatives, to mix global spectral information with local directional variations. This approach allows the RNO to outperform existing operators in complex scenarios that require sensitivity to local detail. Our design bridges the gap between physical interpretability and local dynamics. Experimental results demonstrate that the RNO consistently achieves superior prediction accuracy and generalization performance compared to existing approaches across various benchmark PDE  problems and complex real-world datasets, presenting superior non-linear reconstruction capability in model analysis.
Adaptive Moments are Surprisingly Effective for Plug-and-Play Diffusion Sampling
Authors: Christian Belardi; Justin Lovelace; Kilian Q Weinberger; Carla P Gomes
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Guided diffusion sampling relies on approximating often intractable likelihood scores, which introduces significant noise into the sampling dynamics. We propose using adaptive moment estimation to stabilize these noisy likelihood scores during sampling. Despite its simplicity, our approach achieves state-of-the-art results on image restoration and class-conditional generation tasks, outperforming more complicated methods, which are often computationally more expensive. We provide empirical analysis of our method on both synthetic and real data, demonstrating that mitigating gradient noise through adaptive moments offers an effective way to improve alignment.
Diverse Text Decoding via Iterative Reweighting
Authors: Ruiqi Shi; Sinno Jialin Pan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models (LLMs) have led to impressive results in text generation. However, current decoding methods still lack diversity when combined with popular sampling techniques. We propose a Reweighting-based Iterative DEcoding (OverRIDE) approach that dynamically adjusts the decoding process with history responses. Our method fine-tunes auxiliary output heads iteratively on previously generated sequences to capture and suppress semantic patterns that appear in the history responses. This inference-time training process only incurs minimal loss of efficiency. We conduct extensive experiments on various tasks, including code generation, mathematical reasoning and story generation, demonstrating that OverRIDE increases output diversity while maintaining quality. We implement OverRIDE on LLM serving systems like vLLM, achieving a 6.4% throughput loss for 72B models under parallel decoding. The code is available at https://github.com/shi-rq/OverRIDE.
Terminal Velocity Matching
Authors: Linqi Zhou; Mathias Parger; Ayaan Haque; Jiaming Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose Terminal Velocity Matching (TVM), a generalization of flow matching that enables high-fidelity one- and few-step generative modeling. TVM models the transition between any two diffusion timesteps and regularizes its behavior at its terminal time rather than at the initial time. We prove that TVM provides an upper bound on the $2$-Wasserstein distance between data and model distributions when the model is Lipschitz continuous. However, since Diffusion Transformers lack this property, we introduce minimal architectural changes that achieve stable, single-stage training. To make TVM efficient in practice, we develop a fused attention kernel that supports backward passes on Jacobian-Vector Products, which scale well with transformer architectures. On ImageNet-256x256, TVM achieves 3.29 FID with a single function evaluation (NFE) and 1.99 FID with 4 NFEs. It similarly achieves 4.32 1-NFE FID and 2.94 4-NFE FID on ImageNet-512x512, representing state-of-the-art performance for one/few-step models from scratch.
Robust Spiking Neural Networks Against Adversarial Attacks
Authors: Shuai Wang; Malu Zhang; Yulin Jiang; Dehao Zhang; Ammar Belatreche; Yu Liang; Yimeng Shan; Zijian Zhou; Yang Yang; Haizhou Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing due to their bio-plausible and spike-driven characteristics. 
However, the robustness of SNNs in complex adversarial environments remains significantly constrained. In this study, we theoretically demonstrate that those threshold-neighboring spiking neurons are the key factors limiting the robustness of directly trained SNNs.
We find that these neurons set the upper limits for the maximum potential strength of adversarial attacks and are prone to state-flipping under minor disturbances. To address this challenge, we propose a Threshold Guarding Optimization (TGO) method, which comprises two key aspects. First, we incorporate additional constraints into the loss function to move neurons' membrane potentials away from their thresholds. It increases SNNs' gradient sparsity, thereby reducing the theoretical upper bound of adversarial attacks. Second, we introduce noisy spiking neurons to transition the neuronal firing mechanism from deterministic to probabilistic, decreasing their state-flipping probability due to minor disturbances. Extensive experiments conducted in standard adversarial scenarios prove that our method significantly enhances the robustness of directly trained SNNs. These findings pave the way for advancing more reliable and secure neuromorphic computing in real-world applications.
Neural Dynamics Self-Attention for Spiking Transformers
Authors: Dehao Zhang; Fukai Guo; Shuai Wang; Jingya Wang; Jieyuan Zhang; Yimeng Shan; Malu Zhang; Yang Yang; Haizhou Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Integrating Spiking Neural Networks (SNNs) with Transformer architectures offers a promising pathway to balance energy efficiency and performance, particularly for edge vision applications. However, existing Spiking Transformers face two critical challenges: 
(i) a substantial performance gap compared to their Artificial Neural Networks (ANNs) counterparts and (ii) high memory overhead during inference. Through theoretical analysis, we attribute both limitations to the Spiking Self-Attention (SSA) mechanism: the lack of locality bias and the need to store large attention matrices. Inspired by the localized receptive fields (LRF) and membrane-potential dynamics of biological visual neurons, we propose LRF-Dyn, which uses spiking neurons with localized receptive fields to compute attention while reducing memory requirements. Specifically, we introduce a LRF method into SSA to assign higher weights to neighboring regions, strengthening local modeling and improving performance. Building on this, we approximate the resulting attention computation via charge–fire–reset dynamics, eliminating explicit attention-matrix storage and reducing inference-time memory. Extensive experiments on visual tasks confirm that our method reduces memory overhead while delivering significant performance improvements. These results establish it as a key unit for achieving energy-efficient Spiking Transformers.
Label Smoothing Improves Machine Unlearning
Authors: Zonglin Di; Zhaowei Zhu; Jinghan Jia; Jiancheng Liu; Zafar Takhirov; Bo Jiang; Yuanshun Yao; Sijia Liu; Yang Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The objective of machine unlearning (MU) is to eliminate previously learned data from a model. However, it can be challenging to strike a balance between computation cost and performance when using existing MU techniques. Taking inspiration from the influence of label smoothing on model confidence and differential privacy, we propose a simple gradient-based MU approach that uses an inverse process of label smoothing. This work introduces UGradSL, a simple, plug-and-play MU approach that uses smoothed labels. We provide theoretical analyses demonstrating why properly introducing label smoothing improves MU performance. We conducted extensive experiments on several datasets of various sizes and different modalities, demonstrating the effectiveness and robustness of our proposed method. UGradSL also shows close connection to improve the local differential privacy. The consistent improvement in MU performance is only at a marginal cost of additional computations. For instance, UGradSL improves over the gradient ascent MU baseline constantly on different unlearning tasks without sacrificing unlearning efficiency. A self-adaptive UGradSL is also given for simple parameter selection.
Guided Policy Optimization under Partial Observability
Authors: Yueheng Li; Guangming Xie; Zongqing Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, such as that available in simulations, can enhance training, effectively leveraging it remains an open problem. To address this, we introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner. The guider takes advantage of privileged information while ensuring alignment with the learner's policy that is primarily trained via imitation learning. We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches. Empirical evaluations show strong performance of GPO across various tasks, including continuous control with partial observability and noise, and memory-based challenges, significantly outperforming existing methods.