GenAI4ELab/papercli-papers huggingface dataset:
3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations
Authors: Xi Wang; Yang Zhang; Yingjia Zhang; Yejia Cai; Shenji Wan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Molecular representations (MRs) that capture 3D conformations are critical for applications such as reaction prediction, drug design, and material discovery. Yet despite the rapid development of molecular representation models, there is no comprehensive benchmark to evaluate their treatment of 3D conformational information.
We introduce 3DCS, the first benchmark for 3D Conformational Sensitivity in MRs. 3DCS evaluates whether representations within the same molecule (i) preserve geometric variation, (ii) capture chirality, and (iii) reflect the energy landscape. To enable this, we curate three large-scale datasets ($>$1M molecules, $\sim$10M conformers) spanning relaxed torsional scans, chiral drug candidates, and AIMD trajectories, and propose a unified Geometry–Chirality–Energy (GCE) evaluation framework.
Empirical analysis reveals that while modern data-driven MRs are highly geometry-sensitive, they inconsistently handle chirality and poorly align with energy, which is often overlooked. 3DCS thus provides the first rigorous benchmark for developing physically grounded, functionally reliable 3D molecular representations. GitHub repository: https://github.com/ComDec/3DCS.
Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
Authors: Weizhi Fei; Hao Shi; Jing Xu; Jingchen Peng; Jiazheng Li; Jingzhao Zhang; Bo Bai; Wei Han; Zhenyuan Chen; Xueyan Niu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Efficiently editing knowledge stored in Large Language Models (LLMs) enables model updates without large-scale training. One promising solution is Locate-and-Edit (L\&E), allowing simultaneous modifications of a massive number of factual knowledge. However, such editing may compromise the general abilities of LLMs and even result in forgetting edited facts when scaling up to thousands of edits.  In this paper, we model existing linear L\&E methods as querying a Key-Value (KV) database. From this perspective, we then propose NeuralDB, an editing framework that explicitly represents the edited facts as a neural KV database equipped with a non-linear gated retrieval module. With simple modification over L\&E methods, our framework not only significantly extends the capacity of knowledge editing but also eliminates the associated side effects. Comprehensive experiments involving the editing of 10,000 facts were conducted on the ZsRE and CounterFact datasets, including GPT2-XL, GPT-J (6B) and Llama-3 (8B). The results demonstrate that NeuralDB excels in all metrics of editing success while maintaining original performance evaluated by six representative text understanding and generation tasks. Further experiments indicate that NeuralDB maintains its effectiveness even when scaled to 100,000 facts (\textbf{50}$\mathbf{\times}$ more than in prior work).
CDBridge: A Cross-omics Post-training Bridge Strategy for Context-aware Biological Modeling
Authors: Chang Yu; Siyuan Li; Zicheng Liu; Jingbo Zhou; Xianglong Guo; Kai Yu; Yuqing Zhou; Ken Li; Zelin Zang; Zhen Lei; Stan Z. Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Linking genomic DNA to quantitative, context-specific expression remains a central challenge in computational biology. Current foundation models capture either tissue context or sequence features, but not both. Cross-omics systems, in turn, often overlook critical mechanisms such as alternative splicing and isoform reuse. We present CDBridge, a post-training strategy that unifies pretrained DNA and protein models into a context-aware framework without full retraining. CDBridge operates in two stages: (a) Seq-context learning, where a splicing-inspired token merge compresses long genomic regions into isoform-aware representations, and (b) Env-context learning, where a conditional decoder injects tissue embeddings to model expression under diverse biological contexts. To benchmark this setting, we introduce GTEx-Benchmark, derived from GTEx and Ensembl, which requires models to capture long-range exon dependencies, resolve isoform reuse, and predict tissue-specific expression levels. Across qualitative and quantitative tasks, CDBridge consistently outperforms prior methods that ignore central dogma constraints or context dependence, offering a scalable and biologically faithful solution for DNA-to-expression modeling.
Robust Selective Activation with Randomized Temporal K-Winner-Take-All in Spiking Neural Networks for Continual Learning
Authors: Jiangrong Shen; Liang Zhao; Qi Xu; Yuqi Yang; Liangjun Chen; Gang Pan; Badong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The human brain exhibits remarkable efficiency in processing sequential information, a capability deeply rooted in the temporal selectivity and stochastic competition of neuronal activation. Current continual learning in spiking neural networks (SNNs) faces a critical challenge: balancing task-specific selectivity with adaptive resource allocation and enhancing the robustness with perturbations to mitigate catastrophic forgetting. Considering the intrinsic temporal dynamics of spiking neurons instead of traditional K-winner-take-all (K-WTA) based on firing rate, we explore how to leave networks robust to temporal perturbations in SNNs on lifelong learning tasks.
In this paper, we propose Randomized Temporal K-winner-take-all (RTK-WTA) SNNs for lifelong learning, a biologically grounded approach that integrates trace-dependent neuronal activation with probabilistic top-k selection. By dynamically prioritizing neurons based on their spatiotemporal relevance, RTK-WTA SNNs emulate the brain’s ability to modulate neural resources in spatial and temporal dimensions while introducing controlled randomness to prevent overlapping task representations. The proposed RTK-WTA SNNs enhance inter-class margins and robustness through expanded feature space utilization theoretically. The experimental results show that RTK-WTA surpasses deterministic K-WTA by 3.07–5.0\% accuracy on splitMNIST and splitCIFAR100 with elastic weight consolidation. Controlled stochasticity balances temporal coherence and adaptability, offering a scalable framework for lifelong learning in neuromorphic systems.
Rethinking Causal Mask Attention for Vision-Language Inference
Authors: Xiaohuan Pei; Tao Huang; Yanxiang Ma; Chang Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Causal attention has become a foundational mechanism in autoregressive Vision-Language models (VLMs), unifying textual and visual inputs under a single generative framework. However, existing causal mask-based strategies are inherited from large language models (LLMs) where they are tailored for text-only decoding, and their adaptation to vision tokens is insufficiently addressed in the prefill stage. Strictly masking future positions for vision queries introduces overly rigid constraints, which hinder the model’s ability to leverage future context that often contains essential semantic cues for accurate inference.
In this work, we empirically investigate how different causal masking strategies affect vision-language inference and then propose a family of future-aware attentions tailored for this setting.
We first empirically analyze the effect of previewing future tokens for vision queries and demonstrate that rigid masking undermines the model’s capacity to capture useful contextual semantic representations. Based on these findings, we propose a lightweight attention family that aggregates future visual context into past representations via pooling, effectively preserving the autoregressive structure while enhancing cross-token dependencies.
We evaluate a range of causal masks across diverse vision-language inference settings and show that selectively compressing future semantic context into past representations benefits the inference.
Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding
Authors: Jiarui Li; Zixiang Yin; Haley Smith; Zhengming Ding; Samuel J Landry; Ramgopal R. Mettu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: CD8+ “killer” T cells and CD4+ “helper” T cells play a central role in the adaptive immune system by recognizing antigens presented by Major Histocompatibility Complex (pMHC) molecules via T Cell Receptors (TCRs). Modeling binding between T cells and the pMHC complex is fundamental to understanding basic mechanisms of human immune response as well as in developing therapies. While transformer-based  models such as TULIP have achieved impressive performance in this domain, their black-box nature precludes  interpretability and thus limits a deeper mechanistic understanding of T cell response. 
Most existing post-hoc explainable AI (xAI) methods are confined to encoder-only, co-attention, or model-specific architectures and cannot handle encoder-decoder transformers used in TCR-pMHC modeling. To address this gap, we propose Quantifying Cross-Attention Interaction (QCAI), a new post-hoc method designed to interpret the cross-attention mechanisms in transformer decoders. Quantitative evaluation is a challenge for XAI methods; we have compiled TCR-XAI, a benchmark consisting of 274 experimentally determined TCR-pMHC structures to serve as ground truth for binding. Using these structures we compute physical distances between relevant amino acid residues in the TCR-pMHC interaction region and evaluate how well our method and others estimate the importance of residues in this region across the dataset. We show that QCAI achieves state-of-the-art performance on both interpretability and prediction accuracy under the TCR-XAI benchmark.
LiveWeb-IE: A Benchmark For Online Web Information Extraction
Authors: Seungbin Yang; Jihwan Kim; Jaemin Choi; Dongjin Kim; Soyoung Yang; ChaeHun Park; Jaegul Choo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Web information extraction (WIE) is the task of automatically extracting data from web pages, offering high utility for various applications.
The evaluation of WIE systems has traditionally relied on benchmarks built from HTML snapshots captured at a single point in time.
However, this offline evaluation paradigm fails to account for the temporally evolving nature of the web; consequently, performance on these static benchmarks often fails to generalize to dynamic real-world scenarios.
To bridge this gap, we introduce LiveWeb-IE, a new benchmark designed for evaluating WIE systems directly against live websites.
Based on trusted and permission-granted websites, we curate natural language queries that require information extraction of various data categories, such as text, images, and hyperlinks.
We further design these queries to represent four levels of complexity, based on the number and cardinality of attributes to be extracted, enabling a granular assessment of WIE systems.
In addition, we propose Visual Grounding Scraper (VGS), a novel multi-stage agentic framework that mimics human cognitive processes by visually narrowing down web page content to extract desired information. 
Extensive experiments across diverse backbone models demonstrate the effectiveness and robustness of VGS.
We believe that this study lays the foundation for developing practical and robust WIE systems.
Understanding Dataset Distillation via Spectral Filtering
Authors: Deyu Bo; Songhua Liu; Xinchao Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain largely unexplored. In this paper, we introduce UniDD, a spectral filtering framework that unifies diverse DD objectives. UniDD interprets each DD objective as a specific filter function applied to the eigenvalues of the feature-feature correlation (FFC) matrix to extract certain frequency information of the feature-label correlation (FLC) matrix. In this way, UniDD reveals that the essence of DD fundamentally lies in matching frequency-specific features. Moreover, we characterize the roles of different filters. For example, low-pass filters, \eg, DM and DC, capture blurred patches, while high-pass filters, \eg, MTT and FrePo, prefer to synthesize fine-grained textures and have better diversity. However, existing methods can only learn the sole frequency information as they rely on fixed filter functions throughout distillation. To address this limitation, we further propose Curriculum Frequency Matching (CFM), which gradually adjusts the filter parameter to cover both low- and high-frequency information of the FFC and FLC matrices. Extensive experiments on small-scale datasets, such as CIFAR-10/100, and large-scale ImageNet-1K, demonstrate the superior performance of CFM over existing baselines and validate the practicality of UniDD.
MobiEdit: Resource-efficient Knowledge Editing for Personalized On-device LLMs
Authors: Zhenyan Lu; Daliang Xu; Dongqi Cai; Zexi Li; Wei Liu; Jian Luan; Fangming Liu; Shangguang Wang; Mengwei Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are deployed on mobile devices to power killer applications such as intelligent assistants.  LLMs pre-trained on general corpora often hallucinate when handling personalized or unseen queries, leading to incorrect or outdated responses. Knowledge editing addresses this by identifying and adjusting a small crucial portion of model weights, without compromising the general knowledge.  However, prior knowledge editing methods are impractical to run on local devices due to the resource-heavy backpropagation (BP) needed for updates.  We present MobiEdit, the first mobile knowledge editing framework that enables efficient LLM personalization on commercial off-the-shelf (COTS) mobile devices. MobiEdit replaces full-precision BP with quantized forward-only gradient estimation, thus compatible with the energy-efficient mobile neural processing units (NPUs).  To further improve gradient estimation efficiency, we introduce two optimizations: an early stopping mechanism that adaptively terminates editing upon success and prefix activation reusing that reduce redundant computation across steps. Our approach enables real-time editing of 3B-parameter models (Qwen2.5-3B-Instruct and Llama3.2-3B-Instruct) on COTS mobile devices with 7.1$\times$ less memory, 15.8 $\times$ less energy and 3.4$\times$ less latency compared to previous knowledge editing methods.
EffiVMT: Video Motion Transfer via Efficient Spatial-Temporal Decoupled Finetuning
Authors: Yue Ma; Yulong Liu; Qiyuan Zhu; Xiangpeng Yang; Kunyu Feng; Xinhua Zhang; Zexuan Yan; Zhifeng Li; Sirui Han; Chenyang Qi; Qifeng Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, breakthroughs in the video diffusion transformer have shown remarkable capabilities in diverse motion generations. As for the motion-transfer task, current methods mainly use two-stage Low-Rank Adaptations (LoRAs) finetuning to obtain better performance. However, existing adaptation-based motion transfer still suffers from **motion inconsistency** and **tuning inefficiency** when applied to large video diffusion transformers. Naive two-stage LoRA tuning struggles to maintain motion consistency between generated and input videos due to the inherent spatial-temporal coupling in the 3D attention operator. In addition, they require time-consuming fine-tuning processes in both stages. To tackle these issues, we propose EffiVMT, an efficient **three-stage** video motion transfer framework that finetunes a powerful video diffusion transformer to synthesize complex motion. In **stage 1**, we propose a spatial-temporal head classification technique to decouple the heads of 3D attention to distinct groups for spatial-appearance and temporal motion processing. We then finetune the spatial heads in the **stage 2**.  In the **stage 3** of temporal head tuning, we design the sparse motion sampling and adaptive RoPE to accelerate the tuning speed. To address the lack of a benchmark for this field, we introduce MotionBench, a comprehensive benchmark comprising diverse motion, including creative camera motion, single object motion, multiple object motion, and complex human motion. We show extensive evaluations on MotionBench to verify the superiority of EffiVMT.
Federated ADMM from Bayesian Duality
Authors: Thomas Möllenhoff; Siddharth Swaroop; Finale Doshi-Velez; Mohammad Emtiyaz Khan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a new Bayesian approach to generalize the federated Alternating Direction Method of Multipliers (ADMM). We show that the solutions of variational-Bayesian (VB) objectives are associated with a duality structure that not only resembles the structure of ADMM's fixed-points but also generalizes it. For example, ADMM-like updates are recovered when the VB objective is optimized over the isotropic-Gaussian family, and new non-trivial extensions are obtained for other exponential-family distributions. These extensions include a Newton-like variant that converges in one step on quadratic objectives and an Adam-like variant that yields up to 7% accuracy boosts for deep heterogeneous cases. Our work opens a new Bayesian way to generalize ADMM and other primal-dual methods.
LapFlow: Laplacian Multi-scale Flow Matching for Generative Modeling
Authors: Zelin Zhao; Petr Molodyk; Haotian Xue; Yongxin Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we present Laplacian multiscale flow matching (LapFlow), a novel framework that enhances flow matching by leveraging multi-scale representations for image generative modeling. Our approach decomposes images into Laplacian pyramid residuals and processes different scales in parallel through a mixture-of-transformers (MoT) architecture with causal attention mechanisms. Unlike previous cascaded approaches that require explicit renoising between scales, our model generates multi-scale representations in parallel, eliminating the need for bridging processes. The proposed multi-scale architecture not only improves generation quality but also accelerates the sampling process and promotes scaling flow matching methods. Through extensive experimentation on CelebA-HQ and ImageNet, we demonstrate that our method achieves superior sample quality with fewer GFLOPs and faster inference compared to single-scale and multi-scale flow matching baselines. The proposed model scales effectively to high-resolution generation (up to 1024×1024) while maintaining lower computational overhead.
KeepLoRA: Continual Learning with Residual Gradient Adaptation
Authors: Mao-Lin Luo; Zi-Hao Zhou; Yi-Lin Zhang; Yuanyu Wan; Min-Ling Zhang; Tong Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge. This paper presents a simple but effective approach called KeepLoRA to effectively balance these objectives. We first analyze the knowledge retention mechanism within the model parameter space and find that general knowledge is mainly encoded in the principal subspace, while task-specific knowledge is encoded in the residual subspace. Motivated by this finding, KeepLoRA learns new tasks by restricting LoRA parameter updates in the residual subspace to prevent interfering with previously learned capabilities. Specifically, we infuse knowledge for a new task by projecting its gradient onto a subspace orthogonal to both the principal subspace of pre-trained model and the dominant directions of previous task features. Our theoretical and empirical analyses confirm that KeepLoRA balances the three objectives and achieves state-of-the-art performance. The implementation code is available at https://github.com/MaolinLuo/KeepLoRA.
Near Optimal Robust Federated Learning Against Data Poisoning Attack
Authors: Jingfan Yu; Zhixuan Fang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We revisit data poisoning attacks in the federated learning system. There will be $m$ worker nodes (each has $n$ training data samples) cooperatively training one model for a machine-learning task, and a fraction (i.e.,  $\alpha$) of the workers may suffer from the data poisoning attack. We mainly focus on the challenging and practical case where $n$ is small and $m$ is large, such that each worker does not have enough statistical information to identify the poisoned data by itself, while in total they have enough data to learn the task if the poisoned data are detected. Therefore, we propose a mechanism for workers to cooperatively detect workers with poisoned data. In terms of attack loss, our mechanism achieves $\tilde{O}((\frac{1}{n})^{\frac{1}{2}}+(\frac{d}{mn})^{\frac{1}{2}})$ in IID setting and $\tilde{O}((\frac{1}{\gamma})^{\frac{1}{2}}+(\frac{1}{n})^{\frac{1}{2}}+(\frac{d}{mn})^{\frac{1}{2}})$ in non-IID setting, where $d$ is the VC-dimension of the learning model and $\gamma$ is a concentration parameter characterizing the non-IIDness. Alongside attack loss, our mechanism limits the adversary’s free-ride gain even when it cannot be directly quantified by the attack loss. We also propose the lower bound of the attack loss, and our proposed algorithm matches the lower bound when $m\rightarrow \infty$ both in IID setting and non-IID setting.
Revisiting Long-context Modeling from Context Denoising Perspective
Authors: Zecheng Tang; Baibei Ji; Juntao Li; Lijun Wu; Haijia Gui; Min Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-context models (LCMs) have demonstrated great potential in processing long sequences, facilitating many real-world applications. The success of LCMs can be attributed to their ability to locate implicit critical information within the context for further prediction. However, recent research reveals that LCMs are often susceptible to contextual noise, i.e., irrelevant tokens, that can mislead model attention. In this paper, we conduct a fine-grained analysis of the context noise and propose an effective metric, the Integrated Gradient (IG) score, to detect and quantify the noise information within the context. Our findings reveal that even simple mitigation of detected context noise can substantially boost the model's attention on critical tokens and benefit subsequent predictions. Building on this insight, we propose Context Denoising Training (CDT), a straightforward yet effective training strategy that improves attention on critical tokens while reinforcing their influence on model predictions. Extensive experiments across four tasks, under both context window scaling and long-context alignment settings, demonstrate the superiority of CDT. Notably, when trained with CDT, an open-source 8B model can achieve performance (50.92) comparable to GPT-4o (51.00).