GenAI4ELab/papercli-papers huggingface dataset:
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Authors: Jingying Ma; Feng Wu; Qika Lin; Yucheng Xing; Chenyu Liu; Ziyu Jia; Mengling Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models, current approaches still yield clinically uninterpretable and weakly discriminative representations, inefficiently capturing global dependencies and neglecting important local neural events. We present CodeBrain, a two-stage EFM designed to fill this gap. In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens, quadratically expanding the representation space to enhance discriminative power and offering domain-specific representation-level interpretability by suggesting potential links to neural events and spectral rhythms. In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention to efficiently capture both sparse long-range and local dependencies, reflecting the brain’s small-world topology. Pretrained on the largest public EEG corpus, CodeBrain achieves strong generalization across eight downstream tasks and ten datasets under distribution shifts, supported by comprehensive ablations, scaling-law analyzes, and interpretability evaluations. The code and the pretrained weights are available at https://github.com/jingyingma01/CodeBrain.
Mobile-GS: Real-time Gaussian Splatting for Mobile Devices
Authors: Xiaobiao Du; Yida Wang; Kun Zhan; Xin Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-quality rendering across a wide range of applications.
    However, its high computational demands and large storage costs pose significant challenges for deployment on mobile devices. 
    In this work, we propose a mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference of Gaussian Splatting on edge devices.
    Specifically, we first identify alpha blending as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. 
    To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering.
    Although this order-independent rendering improves rendering speed, it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. 
    To address this problem, we propose a neural view-dependent enhancement strategy, enabling more accurate modeling of view-dependent effects conditioned on viewing direction, 3D Gaussian geometry, and appearance attributes. 
    In this way, Mobile-GS can achieve both high-quality and real-time rendering.
        Furthermore, to facilitate deployment on memory-constrained mobile platforms, we propose first-degree spherical harmonics distillation, a neural vector quantization technique, and a contribution-based pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assistance of neural networks. 
    Extensive experiments demonstrate that our proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it well-suited for mobile applications.
D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
Authors: Haozhe Lou; Mingtong Zhang; Haoran Geng; Hanyang Zhou; Sicheng He; Zhiyuan Gao; Siheng Zhao; Jiageng Mao; Pieter Abbeel; Jitendra Malik; Daniel Seita; Yue Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Simulation provides a cost-effective and flexible platform for data generation and policy learning to develop robotic systems. However, bridging the gap between simulation and real-world dynamics remains a significant challenge, especially in physical parameter identification. In this work, we introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine, enabling object mass identification from real-world visual observations and robot control signals, while enabling grasping policy learning simultaneously. Through optimizing the mass of the manipulated object, our method automatically builds high-fidelity and physically plausible digital twins. Additionally, we propose a novel approach to train force-aware grasping policies from limited data by transferring feasible human demonstrations into simulated robot demonstrations. Through comprehensive experiments, we demonstrate that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values. Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping, effectively reducing the sim-to-real gap.
Block-wise Adaptive Caching for Accelerating Diffusion Policy
Authors: Kangye Ji; Yuan Meng; Hanyun Cui; Ye Li; Jianbo Zhou; Shengjia Hua; Lei Chen; Zhi Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control.
Despite huge redundancy across repetitive denoising steps, existing diffusion acceleration techniques fail to generalize to Diffusion Policy due to fundamental architectural and data divergences.
In this paper, we propose **B**lock-wise **A**daptive **C**aching (**BAC**), a method to accelerate Diffusion Policy by caching intermediate action features. BAC achieves lossless action generation acceleration by adaptively updating and reusing cached features at the block level, based on a key observation that feature similarities exhibit non-uniform temporal dynamics and distinct block-specific patterns. 
To operationalize this insight, we first design an Adaptive Caching Scheduler to identify optimal update timesteps by maximizing the global feature similarities between cached and skipped features. However, applying this scheduler for each block leads to significant error surges due to the inter-block propagation of caching errors, particularly within Feed-Forward Network (FFN) blocks. To mitigate this issue, we develop the Bubbling Union Algorithm, which truncates these errors by updating the upstream blocks with significant caching errors before downstream FFNs.
As a training-free plugin, BAC is readily integrable with existing transformer-based Diffusion Policy and vision-language-action models.  Extensive experiments on multiple robotic benchmarks demonstrate that BAC achieves up to 3x inference speedup for free. Project page: https://block-wise-adaptive-caching.github.io.
Long-tailed Test-Time Adaptation for Vision-Language Models
Authors: Xucong Wang; Zhe Zhao; Zekun Wang; Xiaofeng Cao; Xu Wang; Di Wu; Pengkun Wang; Yang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Test-Time Adaptation (TTA) aims to further adapt models to unlabeled test sets arriving in a sequential datastream, thereby progressively strengthening the model's generalization ability. While existing TTA methods for Vision-Language Models (VLMs) are primarily designed and evaluated on (nearly) balanced dataset configurations, real-world test sets may exhibit a long-tailed distribution where major classes dominate the decision boundaries of minor classes, presenting unique challenges. As the first attempt to solve this problem, this paper proposes Long-tailed Test-Time Adaptation (dubbed as L-TTA), which consists of three co-designed mechanisms: Synergistic Prototypes (SyPs), Rebalancing Shortcuts (RSs), and Balanced Entropy Minimization (BEM). SyPs introduce two fine-grained prototypes to enrich tail classes with extra inter-class knowledge; RSs employ learnable shortcuts to achieve learnable adaptation, regularized by class re-allocation loss to enforce distinct feature clustering; BEM restrains excessive entropy minimization of confident classes with extra penalty term, with theoretical propositions to justify its rebalancing capabilities. Extensive experiments over 15 datasets under various long-tailed settings highlight the superior performance of L-TTA in both accuracy and class balancing.
Realtime Video Frame Interpolation using One-Step Diffusion Sampling
Authors: Yongrui Ma; Shijie Zhao; Mingde Yao; Junlin Li; Li zhang; Xiaohong Liu; Qi Dou; Jinwei Gu; Tianfan Xue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video Frame Interpolation (VFI) involving large, complex motions remains a significant challenge due to the difficulty of modeling diverse pixel trajectories from limited inputs. Traditional methods struggle with low-order approximations, and recent Latent Video Diffusion Models (LVDM) improve it through a conditional generation modeling. Still, current LVDMs often prioritize pixel fidelity over motion coherence in their reconstruction objective, leading to artifacts in extreme motion scenarios. To address this, we propose RDVFI, a novel approach that leverages an LVDM to generate sparse latent keyframes which define high-order, continuous pixel trajectories. The estimated continuous pixel trajectories accurately index pixel movements from inputs to arbitrary timestamps, generating optical flows to warp input pixels into the target frame. By decoupling sequence motion generation from high-resolution rendering, RDVFI operates on a fixed, lower resolution, and fewer diffusion sampling steps, introducing significant efficiency gains. Extensive experiments demonstrate that RDVFI achieves state-of-the-art visual and numerical performance, with over 75\% of viewers selecting it as the best method in terms of motion and frame quality compared to leading baselines. Furthermore, RDVFI is the first LVDM-based VFI method to achieve real-time performance (17 FPS at $1024\times 576$), offering a $\times 44$ acceleration over the current state-of-the-art and also robustly handling challenging motions.
Closing the Safety Gap: Surgical Concept Erasure in Visual Autoregressive Models
Authors: Xinhao Zhong; Yimin Zhou; Zhiqi Zhang; Junhao Li; Sun Yi; Bin Chen; Shu-Tao Xia; Xuan Wang; Ke Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid progress of visual autoregressive (VAR) models has brought new opportunities for text-to-image generation, but also heightened safety concerns. Existing concept erasure techniques, primarily designed for diffusion models, fail to generalize to VARs due to their next-scale token prediction paradigm. In this paper, we first propose a novel VAR Erasure framework **VARE** that enables stable concept erasure in VAR models by leveraging auxiliary visual tokens to reduce fine-tuning intensity. Building upon this, we introduce **S-VARE**, a novel and effective concept erasure method designed for VAR, which incorporates a filtered cross entropy loss to precisely identify and minimally adjust unsafe visual tokens, along with a preservation loss to maintain semantic fidelity, addressing the issues such as language drift and reduced diversity introduce by na\"ive fine-tuning. Extensive experiments demonstrate that our approach achieves surgical concept erasure while preserving generation quality, thereby closing the safety gap in autoregressive text-to-image generation by earlier methods.
Aurora: Towards Universal Generative Multimodal Time Series Forecasting
Authors: Xingjian Wu; Jianxin Jin; Wanghui Qiu; Peng Chen; Yang Shu; Bin Yang; Chenjuan Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinct future trends due to the domain-specific characteristics. Recent works focus on building unimodal time series
foundation models and end-to-end multimodal supervised models. Since domain-specific knowledge is often contained in modalities like texts, the former lacks the explicit utilization of them, thus hindering the performance. The latter is tailored for end-to-end scenarios and does not support zero-shot inference for cross-domain scenarios. In this work, we introduce Aurora, the first Multimodal Time Series Foundation Model, which supports multimodal inputs and zero-shot inference. Pretrained on Cross-domain Multimodal Time Series Corpus, Aurora can adaptively extract and focus on key domain knowledge contained in corresponding text or image modalities, thus possessing strong cross-domain generalization capability. Through tokenization, encoding, and distillation, Aurora can extract multimodal domain knowledge as guidance and then utilizes a Modality-Guided Multi-head Self-Attention to inject them into the modeling of temporal representations. In the decoding phase, the multimodal representations are used to generate the conditions and prototypes of future tokens, contributing to a novel Prototype-Guided Flow Matching for generative probabilistic forecasting. Comprehensive experiments on 5 well-recognized benchmarks, including TimeMMD, TSFM-Bench, ProbTS, TFB, and EPF, demonstrate the consistent state-of-the-art performance of Aurora on both unimodal and multimodal scenarios.
HEEGNet: Hyperbolic Embeddings for EEG
Authors: Shanglin Li; Chu Shiwen; Okan Koç; Yi Ding; Qibin Zhao; Motoaki Kawanabe; Ziheng Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. However, their utility is currently limited because EEG decoding often suffers from poor generalization due to distribution shifts across domains (e.g., subjects). Learning robust representations that capture underlying task-relevant information would mitigate these shifts and improve generalization. One promising approach is to exploit the underlying hierarchical structure in EEG, as recent studies suggest that hierarchical cognitive processes, such as visual processing, can be encoded in EEG. Yet, most existing decoding methods rely on Euclidean embeddings, which are not well-suited for capturing hierarchical structures. 
In contrast, hyperbolic spaces, regarded as the continuous analogue of tree structures, provide a natural geometry for representing hierarchical data. In this study, we first demonstrate that EEG data exhibit hyperbolicity and show that hyperbolic embeddings improve generalization. Motivated by these findings, we propose HEEGNet, a hybrid hyperbolic network architecture to capture the hierarchical structure in EEG and learn domain-invariant hyperbolic embeddings. To this end, HEEGNet combines both Euclidean and hyperbolic encoders and employs a novel coarse-to-fine domain adaptation strategy. Extensive experiments on multiple public EEG datasets, covering visual evoked potentials, emotion recognition, and intracranial EEG, demonstrate that HEEGNet achieves state-of-the-art performance.
On Discriminative vs. Generative classifiers: Rethinking MLLMs for Action Understanding
Authors: Zhanzhong Pang; Dibyadip Chatterjee; Fadime Sener; Angela Yao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have advanced open-world action understanding and can be adapted as generative classifiers for closed-set settings by autoregressively generating action labels as text. However, this approach is inefficient, and shared subwords across action labels introduce semantic overlap, leading to ambiguity in generation. In contrast, discriminative classifiers learn task-specific representations with clear decision boundaries, enabling efficient one-step classification without autoregressive decoding.
We first compare generative and discriminative classifiers with MLLMs for closed-set action understanding, revealing the superior accuracy and efficiency of the latter. To bridge the performance gap, we design strategies that elevate generative classifiers toward performance comparable with discriminative ones. Furthermore, we show that generative modeling can complement discriminative classifiers, leading to better performance while preserving efficiency.
To this end, we propose Generation-Assisted Discriminative (GAD) classifier for closed-set action understanding. GAD operates only during fine-tuning, preserving full compatibility with MLLM pretraining. Extensive experiments on temporal action understanding benchmarks demonstrate that GAD improves both accuracy and efficiency over generative methods, achieving state-of-the-art results on four tasks across five datasets, including an average 2.5\% accuracy gain and 3$\times$ faster inference on our largest COIN benchmark.
LLM Unlearning with LLM Beliefs
Authors: Kemou Li; Qizhou Wang; Yue Wang; Fengpeng Li; Jun Liu; Bo Han; Jiantao Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models trained on vast corpora inherently risk memorizing sensitive or harmful content, which may later resurface in their outputs.
Prevailing unlearning methods generally rely on gradient ascent and its variants to lower the probability of specific target responses.
However, we find that this strategy induces a critical side effect: probability mass is redistributed into high-likelihood regions, often corresponding to semantically related rephrasings of the targets.
We refer to this as the ***squeezing effect***, which explains why many methods yield merely spurious unlearning, a problem further obscured by automated metrics (e.g., ROUGE, truth ratio) that misreport actual success.
To address this, we propose a ***bootstrapping*** (BS) framework that explicitly links the squeezing effect with the model’s own high-confidence generations, namely its ***model beliefs***.
Since model beliefs inherently capture the very high-likelihood regions where probability mass is squeezed, incorporating them into the unlearning objective directly counters the squeezing effect.
By jointly suppressing both target responses and model beliefs, BS-T (token) attenuates high-probability tokens, whereas BS-S (sequence) removes entire high-confidence generations, together achieving more thorough forgetting while preserving utility.
Extensive experiments on diverse benchmarks confirm the effectiveness of our approach.
Beyond Student: An Asymmetric Network for Neural Network Inheritance
Authors: Yiyun Zhou; Jingwei Shi; Mingjing Xu; Zhonghua Jiang; Jingyuan Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge Distillation (KD) has emerged as a powerful technique for model compression, enabling lightweight student networks to benefit from the performance of redundant teacher networks. However, the inherent capacity gap often limits the performance of student networks. Inspired by the expressiveness of pretrained teacher networks, a compelling research question arises: is there a type of network that can not only inherit the teacher’s structure but also maximize the inheritance of its knowledge? Furthermore, how does the performance of such an inheriting network compare to that of student networks, all benefiting from the same teacher network? To further explore this question, we propose InherNet, a neural network inheritance method that performs asymmetric low-rank decomposition on the teacher’s weights and reconstructs a lightweight yet expressive network without significant architectural disruption. By leveraging Singular Value Decomposition (SVD) for initialization to ensure the inheritance of principal knowledge, InherNet effectively balances depth, width, and compression efficiency. Experimental results across unimodal and multimodal tasks demonstrate that InherNet achieves higher performance compared to student networks of similar parameter sizes. Our findings reveal a promising direction for future research in efficient model compression beyond traditional distillation.
Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation
Authors: Kang Liao; Size Wu; Zhonghua Wu; Linyi Jin; Chao Wang; Yikai Wang; Fei Wang; Wei Li; Chen Change Loy
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Camera-centric understanding and generation are two cornerstones of spatial intelligence, yet they are typically studied in isolation. We present Puffin, a unified camera-centric multimodal model that extends spatial awareness along the camera dimension. Puffin integrates language regression and diffusion-based generation to interpret and create scenes from arbitrary viewpoints. To bridge the modality gap between cameras and vision-language, we introduce a novel paradigm that treats camera as language, enabling thinking with camera. This guides the model to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. Puffin is trained on Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets. We incorporate both global camera parameters and pixel-wise camera maps, yielding flexible and reliable spatial generation. Experiments demonstrate Puffin’s superior performance over specialized models for camera-centric generation and understanding. With instruction tuning, Puffin generalizes to diverse cross-view tasks such as spatial imagination, world exploration, and photography guidance. We will release the code, models, dataset pipeline, and benchmark to advance multimodal spatial intelligence research.
Spurious Correlation-Aware Embedding Regularization for Worst-Group Robustness
Authors: Subeen Park; JOOWANG KIM; Hakyung Lee; Sunjae yoo; Kyungwoo Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep learning models achieve strong performance across various domains but often rely on spurious correlations, making them vulnerable to distribution shifts. This issue is particularly severe in subpopulation shift scenarios, where models struggle in underrepresented groups. While existing methods have made progress in mitigating this issue, their performance gains are still constrained. They lack a theoretical motivation connecting the embedding space representations with worst-group error. 
To address this limitation, we propose Spurious Correlation-Aware Embedding Regularization for Worst-Group Robustness (SCER), a novel approach that directly regularizes feature representations to suppress spurious cues. We theoretically show that worst-group error is influenced by how strongly the classifier relies on spurious versus core directions, as identified from differences in group-wise mean embeddings across domains and classes. 
By imposing theoretical constraints at the embedding level, SCER encourages models to focus on core features while reducing sensitivity to spurious patterns. Through systematic evaluation on multiple vision and language tasks, we show that SCER outperforms prior state-of-the-art methods in worst-group accuracy. Our code is available at \href{https://github.com/MLAI-Yonsei/SCER}{https://github.com/MLAI-Yonsei/SCER}.