GenAI4ELab/papercli-papers huggingface dataset:
One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Learning
Authors: Thanh Xuan Nguyen; Chang D. Yoo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Q-Learning (DQL) has established diffusion policies as a high-performing paradigm for offline reinforcement learning, but its reliance on multi-step denoising for action generation renders both training and inference slow and fragile. Existing efforts to accelerate DQL toward one-step denoising typically rely on auxiliary modules or policy distillation, sacrificing either simplicity or performance. It remains unclear whether a one-step policy can be trained directly without such trade-offs. To this end, we introduce One-Step Flow Q-Learning (OFQL), a novel framework that enables effective one-step action generation during both training and inference, without auxiliary modules or distillation. OFQL reformulates the DQL policy within the Flow Matching (FM) paradigm but departs from conventional FM by learning an average velocity field that directly supports accurate one-step action generation. This design removes the need for multi-step denoising and backpropagation-through-time updates, resulting in substantially faster and more robust learning. Extensive experiments on the D4RL benchmark show that OFQL, despite generating actions in a single step, not only significantly reduces computation during both training and inference but also outperforms multi-step DQL by a large margin. Furthermore, OFQL surpasses all other baselines, achieving state-of-the-art performance in D4RL.
AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration
Authors: Binqi Chen; Hongjun Ding; Ning Shen; Taian Guo; Jinsheng Huang; Luchen Liu; Ming Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The automated mining of predictive signals, or alphas, is a central challenge in quantitative finance. While Reinforcement Learning (RL) has emerged as a promising paradigm for generating formulaic alphas, existing frameworks are fundamentally hampered by a triad of interconnected issues. First, they suffer from reward sparsity, where meaningful feedback is only available upon the completion of a full formula, leading to inefficient and unstable exploration. Second, they rely on semantically inadequate sequential representations of mathematical expressions, failing to capture the structure that determine an alpha's behavior. Third, the standard RL objective of maximizing expected returns inherently drives policies towards a single optimal mode, directly contradicting the practical need for a diverse portfolio of non-correlated alphas. To overcome these challenges, we introduce **AlphaSAGE** (**S**tructure-**A**ware Alpha Mining via **G**enerative Flow Networks for Robust **E**xploration), a novel framework is built upon three cornerstone innovations: (1) a structure-aware encoder based on Relational Graph Convolutional Network (RGCN); (2) a new framework with Generative Flow Networks (GFlowNets); and (3) a dense, multi-faceted reward structure. Empirical results demonstrate that AlphaSAGE outperforms existing baselines in mining a more diverse, novel, and highly predictive portfolio of alphas, thereby proposing a new paradigm for automated alpha mining. Our code is available at https://anonymous.4open.science/r/AlphaSAGE-3BA9.
Naming to Learn: Class Incremental Learning for Vision-Language Model with Unlabeled Data
Authors: Qiwei Li; Xiaochen Yang; Jiahuan Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Class Incremental Learning (CIL) enables models to adapt to evolving data distributions by learning new classes over time without revisiting previous data. While recent methods utilizing pre-trained models have shown promising results, they often assume access to fully labeled data for each incremental task, which is often impractical. In this paper, we instead tackle a more realistic scenario in which only unlabeled data and the class-name set are available for each new class. Although one could generate pseudo labels with a vision-language model and apply existing CIL methods, the inevitable noise in these pseudo labels tends to aggravate catastrophic forgetting. To overcome this challenge, we propose a method named N2L employing a regression objective with mean squared error loss, which can be solved in a recursive manner. To refine the pseudo labels, N2L applies feature dimensionality reduction to the extracted image features and iteratively updates the labels using a classifier trained on these reduced features. Furthermore, a bi-level weight adjustment strategy is proposed to downweight low-confidence pseudo labels via intra-class adjustment and compensate for pseudo-label class imbalance through inter-class adjustment. This incremental learning with adjustment can be solved recursively, yielding identical performance to joint training with unlabeled data and thereby mitigating forgetting. Our theoretical analysis supports the effectiveness of the pseudo label refinement process, and experiments on various datasets demonstrate that our proposed method outperforms SOTA methods. Code is available at https://github.com/zhoujiahuan1991/ICLR2026-N2L
CerebraGloss: Instruction-Tuning a Large Vision-Language Model for Fine-Grained Clinical EEG Interpretation
Authors: Wei Gu; Luo Tianming; Qiran Zhang; Mohan Ye; Xiao Shen; Wenxin Chen; Yunhuan Li; Yichen Zhang; Jing Hong; Bao-liang Lu; Wei-Long Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Interpreting clinical electroencephalography (EEG) is a laborious, subjective process, and existing computational models are limited to narrow classification tasks rather than holistic interpretation. A key bottleneck for applying powerful Large Vision-Language Models (LVLMs) to this domain is the scarcity of datasets pairing EEG visualizations with fine-grained, expert-level annotations. We address this by introducing CerebraGloss, an instruction-tuned LVLM for nuanced EEG interpretation. We first introduce a novel, automated data generation pipeline, featuring a bespoke YOLO-based waveform detector, to programmatically create a large-scale corpus of EEG-text instruction data. Using this data, we develop CerebraGloss, the first model of its kind capable of unified, generative analysis—performing tasks from detailed waveform description to multi-turn, context-aware dialogue. To evaluate this new capability, we construct and release CerebraGloss-Bench, a comprehensive benchmark for open-ended EEG interpretation. CerebraGloss demonstrates strong performance, surpassing leading LVLMs, including proprietary models like GPT-5, on this benchmark and achieving a new state-of-the-art on the TUSZ seizure detection task. Models, benchmark and tools are available at https://github.com/iewug/CerebraGloss.
ATLAS: Alibaba Dataset and Benchmark for Learning-Augmented Scheduling
Authors: Zhiyun Jiang; Tianming Zhao; Chunqiu xia; Albert Zomaya
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning-augmented scheduling uses ML predictions to improve decision-making under uncertainty. Many algorithms in this class have been proposed with better theoretical guarantees than the classic methods. Translating these theoretical results into practice, however, requires an understanding of real workloads. Such an understanding is hard to develop because existing production traces either lack the ground-truth processing times or are not publicly available, while synthetic benchmarks fail to represent real-world complexity. We fill this gap by introducing *Alibaba Trace for Learning-Augmented Scheduling (ATLAS)*, a research-ready dataset derived from Alibaba's Platform of Artificial Intelligence (PAI) cluster trace—a production system that processes hundreds of thousands of ML jobs per day. The ATLAS dataset has been cleaned and features engineered to represent the inputs and constraints of non-clairvoyant scheduling, including user tags, resource requests (CPU/GPU/memory), and job structures with ground-truth processing times. We develop a prediction benchmark reporting prediction error metrics, along with feature importance analysis, and introduce a novel multiple-stage ML model. We also provide a scheduling benchmark for minimizing the total completion time, max-stretch, and makespan. ATLAS is a reproducible foundation for researchers to study learning-augmented scheduling on real workloads, available at https://github.com/zhiyunjiang0810/non-clairvoyant-with-predictions.
Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing
Authors: Xu Wang; Chenkai Xu; Yijie Jin; Jiachun Jin; Hao Zhang; Kai Yu; Zhijie Deng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs for text generation, with the potential to decode multiple tokens in a single iteration. However, none of the existing open-source dLLMs have achieved superior inference speed over AR LLMs of similar size.  This paper breaks this barrier based on a simple and effective strategy named discrete diffusion forcing (D2F). D2F equips dLLMs with two key capabilities: (1) block-wise autoregressive generation to enable KV cache utilization; (2) prediction of following tokens without requiring completion of prior blocks for inter-block parallel decoding. In this way, the vanilla dLLMs are refurbished into an AR-diffusion hybrid paradigm for efficient inference. D2F can be implemented with an asymmetric distillation process based on pre-trained dLLMs to achieve rapid convergence.We further propose a pipelined parallel decoding algorithm, which enables a trade-off between efficiency and efficacy. Empirically, D2F dLLMs achieve more than $\mathbf{2.5\times}$ inference speed than LLaMA3 and Qwen2.5 on GSM8K. Compared to the vanilla dLLMs like LLaDA and Dream, the acceleration can be more than $\mathbf{50\times}$ while maintaining comparable output quality.
M$^2$-Miner: Multi-Agent Enhanced MCTS for Mobile GUI Agent Data Mining
Authors: Rui Lv; Juncheng Mo; Tianyi Chu; Chen Rao; Hongyi Jing; Jiajie Teng; Jiafu Chen; Shiqi Zhang; Liangzi Ding; Shuo Fang; Huaizhong Lin; Ziqiang Dang; Chenguang Ma; Lei Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graphical User Interface (GUI) agent is pivotal to advancing intelligent human-computer interaction paradigms. Constructing powerful GUI agents necessitates the large-scale annotation of high-quality user-behavior trajectory data (i.e., intent–trajectory pairs) for training. However, manual annotation methods and current GUI agent data mining approaches typically face three critical challenges: high construction cost, poor data quality, and low data richness. To address these issues, we propose M$^2$-Miner, the first low-cost and automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search (MCTS). For better data mining efficiency and quality, we present a collaborative multi-agent framework, comprising InferAgent, OrchestraAgent, and JudgeAgent for guidance, acceleration, and evaluation. To further enhance the efficiency of mining and enrich intent diversity, we design an intent recycling strategy to extract extra valuable interaction trajectories. Additionally, a progressive model-in-the-loop training strategy is introduced to improve the success rate of data mining. Extensive experiments have demonstrated that the GUI agent fine-tuned using our mined data achieves state-of-the-art performance on several commonly used mobile GUI benchmarks. Our work will be released to facilitate the community research.
Towards a Transferable Acceleration Method for Density Functional Theory
Authors: Zhe Liu; Yuyan Ni; Zhichen Pu; Qiming Sun; Siyuan Liu; Wen Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, sophisticated deep learning-based approaches have been developed for generating efficient initial guesses to accelerate the convergence of density functional theory (DFT) calculations. While the actual initial guesses are often density matrices (DM), quantities that can convert into density matrices also qualify as alternative forms of initial guesses. Hence, existing works mostly rely on the
prediction of the Hamiltonian matrix for obtaining high-quality initial guesses. However, the Hamiltonian matrix is both numerically difficult to predict and intrinsically non-transferable, hindering the application of such models in real scenarios. In light of this, we propose a method that constructs DFT initial guesses by predicting the electron density in a compact auxiliary basis representation using
E(3)-equivariant neural networks. Trained exclusively on small molecules with up to 20 atoms, our model achieves an average 33.3% reduction in SCF iterations for molecules three times larger (up to 60 atoms). This result is particularly significant
given that baseline Hamiltonian-based methods fail to generalize, often increasing the iteration count by over 80% or failing to converge entirely on these larger systems. Furthermore, we demonstrate that this acceleration is robustly scalable: the model successfully accelerates calculations for systems with up to 900 atoms (polymers and polypeptides) without retraining. To the best of our knowledge,
this work represents the first and robust candidate for a universally transferable DFT acceleration method. We also released the SCFbench dataset and its accompanying code to facilitate future research in this promising direction.
A Step to Decouple Optimization in 3DGS
Authors: Renjie Ding; Yaonan Wang; Min Liu; Jialin Zhu; Jiazheng Wang; Jiahao Zhao; Wenting Shen; Feixiang He; Xiang Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis. As an explicit representation optimized through gradient propagation among primitives, optimization widely accepted in deep neural networks (DNNs) is actually adopted in 3DGS, such  as synchronous weight updating and Adam with the adaptive gradient. However, considering the physical significance and specific design in 3DGS, there are two overlooked details in the optimization of 3DGS: (i) update step coupling, which induces optimizer state rescaling and costly attribute updates outside the viewpoints, and (ii) gradient coupling in the moment, which may lead to under- or over-effective regularization. Nevertheless, such a complex coupling is under-explored. After revisiting the optimization of 3DGS, we take a step to decouple it and recompose the process into: Sparse Adam, Re-State Regularization and Decoupled Attribute Regularization. Taking a large number of experiments under the 3DGS and 3DGS-MCMC frameworks, our work provides a deeper understanding of these components. Finally, based on the empirical analysis, we re-design the optimization and propose AdamW-GS by re-coupling the beneficial components, under which better optimization efficiency and representation effectiveness are achieved simultaneously.
ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation
Authors: Ruihang Xu; Dewei Zhou; Fan Ma; Yi Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-instance image generation (MIG) remains a significant challenge for modern diffusion models due to key limitations in achieving precise control over object layout and preserving the identity of multiple distinct subjects. To address these limitations, we introduce **ContextGen**, a novel Diffusion Transformer framework for multi-instance generation that is guided by both layout and reference images. Our approach integrates two key technical contributions: a **Contextual Layout Anchoring (CLA)** mechanism that incorporates the composite layout image into the generation context to robustly anchor the objects in their desired positions, and **Identity Consistency Attention (ICA)**, an  innovative attention mechanism that leverages contextual reference images to ensure the identity consistency of multiple instances. To address the absence of a large-scale, high-quality dataset for this task, we introduce **IMIG-100K**, the first dataset to provide detailed layout and identity annotations specifically designed for Multi-Instance Generation. Extensive experiments demonstrate that ContextGen sets a new state-of-the-art, outperforming existing methods especially in layout control and identity fidelity.
UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
Authors: Jingbo Lin; Zhilu Zhang; Wenbo Li; Renjing Pei; Hang Xu; Hongzhi Zhang; Wangmeng Zuo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation estimation-based priors, and the latter suffer from the inevitable error in degradation estimation. Consequently, the performance of existing methods has a large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model. Then, UniRestorer adopts both degradation and granularity estimation to adaptively select an appropriate expert for image restoration. In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation-specific restoration, and use granularity estimation to make the model robust to degradation estimation error. Experimental results show that our UniRestorer outperforms state-of-the-art all-in-one methods by a large margin, and is promising in closing the performance gap to specific single-task models. The code and pre-trained models will be publicly available.
Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields
Authors: Tianyu Xiong; Skylar Wurster; Han Wei Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding-based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm. DRR leverages a deep refiner network, alongside non-parametric transformations, in a one-time offline process to encode rich representations into a compact and efficient embedding structure. This approach decouples slow neural networks with high representational capacity from the fast inference path. We introduce DRR-Net, a simple network that validates this paradigm, and a novel data augmentation strategy, Variational Pairs (VP) for improving INRs under complex tasks like high-dimensional surrogate modeling. Experiments on several ensemble simulation datasets demonstrate that our approach achieves state-of-the-art fidelity, while being up to 27$\times$ faster at inference than high-fidelity baselines and remaining competitive with the fastest models. The DRR paradigm offers an effective strategy for building powerful and practical neural field surrogates and INRs in broader applications, with a minimal compromise between speed and quality.
MoM: Linear Sequence Modeling with Mixture-of-Memories
Authors: Jusen Du; Weigao Sun; Disen Lan; Jiaxi Hu; Tao Zhang; Yu Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, offer significant efficiency improvements by reducing the complexity of training and inference. However, these methods typically compress the entire input sequence into a single fixed-size memory state, which leads to suboptimal performance on recall-intensive tasks. To address this limitation, we introduce a novel architecture called Mixture-of-Memories (MoM). MoM utilizes multiple independent memory states, with a router network directing input tokens to specific memory states. This approach greatly enhances the overall memory capacity while minimizing memory interference. MoM serves as a general framework that can be seamlessly combined with diverse memory update mechanisms across linear models. As a result, MoM performs exceptionally well on recall-intensive tasks, surpassing existing linear sequence modeling techniques. Despite incorporating multiple memory states, the computation of each memory state remains linear in complexity, allowing MoM to retain the linear-complexity advantage during training, while constant-complexity during inference. Our experimental results show that MoM outperforms current linear sequence models on downstream language tasks, particularly recall-intensive tasks, and even achieves performance comparable to Transformer models.
Hyperspherical Latents Improve Continuous-Token Autoregressive Generation
Authors: Guolin Ke; HUI XUE
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autoregressive (AR) models are promising for image generation, yet continuous-token AR variants often trail latent diffusion and masked-generation models. The core issue is heterogeneous variance in VAE latents, which is amplified during AR decoding, especially under classifier-free guidance (CFG), and can cause variance collapse. We propose SphereAR to address this issue. Its core design is to constrain all AR inputs and outputs---including after CFG---to lie on a fixed-radius hypersphere (constant $\ell_2$ norm), leveraging hyperspherical VAEs.  Our theoretical analysis shows that hyperspherical constraint removes the scale component (the primary cause of variance collapse), thereby stabilizing AR decoding. Empirically, on ImageNet generation, SphereAR-H (943M) sets a new state of the art for AR models, achieving FID 1.34. Even at smaller scales, SphereAR-L (479M) reaches FID 1.54 and SphereAR-B (208M) reaches 1.92, matching or surpassing much larger baselines such as MAR-H (943M, 1.55) and VAR-d30 (2B, 1.92).
To our knowledge, this is the first time a pure next-token AR image generator with raster order surpasses diffusion and masked-generation models at comparable parameter scales.
Measuring and Mitigating Rapport Bias of Large Language Models under Multi-Agent Social Interactions
Authors: Maojia Song; Tej Deep Pala; Ruiwen Zhou; Weisheng Jin; Amir Zadeh; Chuan Li; Dorien Herremans; Soujanya Poria
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS) as components of collaborative intelligence, where peer interactions dynamically shape individual decision-making. While prior work has largely focused on conformity bias, we broaden the scope to examine how LLMs build rapport from previous interactions, resist misinformation, and integrate peer input during collaboration, which are key factors for achieving collective intelligence under complex social dynamics. We introduce KAIROS, a benchmark simulating quiz contests with peer agents of varying reliability, offering fine-grained control over conditions such as expert–novice roles, noisy crowds, and adversarial peers. LLMs receive both historical interactions and current peer responses, allowing systematic investigation into how rapport, peer action, and self-confidence influence decisions. To mitigate this vulnerability, we evaluate prompting, supervised fine-tuning, and reinforcement learning using Group Relative Policy Optimization (GRPO) across multiple models. Our results show that model size plays a central role in moderating susceptibility to social influence: larger models exhibit stronger resilience and benefit from prompting-based mitigation, whereas smaller models are more vulnerable. For the latter, carefully configured GRPO training improves both robustness and overall performance. Our code and datasets are available at: https://anonymous.4open.science/r/KAIROS-4F71