GenAI4ELab/papercli-papers huggingface dataset:
VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
Authors: Kyoungjun Park; Yifan Yang; Juheon Yi; Shicheng Zheng; Muhammad Muaz; Yifei Shen; Dongqi Han; Caihua Shan; Lili Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid proliferation of AI-generated video necessitates robust detection tools that offer both high accuracy and human-interpretable explanations. While existing MLLM-based detectors rely on supervised fine-tuning (SFT) or direct preference optimization (DPO), these methods are often bottlenecked by static, pre-labeled datasets that fail to capture the evolving, multi-step physical inconsistencies of modern generative models. To bridge this gap, we introduce VidGuard-R1, the first video authenticity detector to utilize group relative policy optimization (GRPO). Moving beyond passive preference matching, VidGuard-R1 employs a reinforcement learning framework that encourages the model to explore and rank multiple reasoning paths. By introducing specialized reward models for temporal stability and diffusion-aware complexity, we incentivize the model to discover 'physics-grounded' artifacts. Our contributions include: (1) a curated dataset of 140,000 challenging real/fake video pairs; (2) a GRPO-based training paradigm that achieves state-of-the-art zero-shot performance; and (3) a reasoning-first architecture that provides precise, verifiable rationales for its forensic judgments. Project website: https://vidguard-r1.github.io
FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing
Authors: Jeongsol Kim; Yeobin Hong; Jonghyun Park; Jong Chul Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent inversion-free, flow-based image editing methods such as FlowEdit leverages a pre-trained noise-to-image flow model such as Stable Diffusion 3, enabling text-driven manipulation by solving an ordinary differential equation (ODE). While the lack of exact latent inversion is a core advantage of these methods, it often results in unstable editing trajectories and poor source consistency. To address this limitation, we propose {\em FlowAlign}, a novel inversion-free flow-based framework for consistent image editing with principled trajectory control. FlowAlign introduces a flow-matching loss as a regularization mechanism to promote smoother and more stable trajectories during the editing process. Notably, the flow-matching loss is shown to explicitly balance semantic alignment with the edit prompt and structural consistency with the source image along the trajectory. Furthermore, FlowAlign naturally supports reverse editing by simply reversing the ODE trajectory, highliting the reversible and consistent nature of the transformation. Extensive experiments demonstrate that FlowAlign outperforms existing methods in both source preservation and editing controllability.
Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning
Authors: Shenao Zhang; Yaqing Wang; Yinxiao Liu; Tianqi Liu; Peter Grabowski; Eugene Ie; Zhaoran Wang; Yunxuan Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) trained via Reinforcement Learning (RL) have exhibited strong reasoning capabilities and emergent reflective behaviors, such as rethinking and error correction, as a form of in-context exploration. However, the Markovian policy obtained from conventional RL training does not give rise to reflective exploration behaviors since the policy depends on the history only through the state and therefore has no incentive to enrich identical states with additional context. Instead, RL exploration is only useful during training to learn the optimal policy in a trial-and-error manner. Therefore, it remains unclear whether reflective reasoning will emerge during RL, or why it is beneficial. To remedy this, we recast reflective exploration within a Bayesian RL framework, which optimizes the expected return under a posterior distribution over Markov decision processes induced by the training data. This Bayesian formulation admits uncertainty-adaptive policies that, through belief updates, naturally incentivize information-gathering actions and induce self-reflection behaviors. Our resulting algorithm, BARL, instructs the LLM to stitch and switch strategies based on the observed outcomes, offering principled guidance on when and how the model should reflectively explore. Empirical results on both synthetic and mathematical reasoning tasks demonstrate that BARL outperforms conventional RL approaches, achieving superior test-time performance and token efficiency. Our code is available at https://github.com/shenao-zhang/BARL.
R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
Authors: Yi Lu; Jianing Wang; Linsen Guo; Wei He; Hongyin Tang; Tao Gui; Xuanjing Huang; Xuezhi Cao; Wei Wang; Xunliang Cai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models’ ability to understand and respond to complex, long-horizon scenarios. To address this incomplete evaluation of Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to stimulate long-horizon reasoning behaviors in LRMs through query composition. Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising complex multi-step reasoning tasks with interdependent problems that span long reasoning horizons. Through comprehensive evaluation of LRMs using the R-HORIZON benchmark, we find that even the most advanced LRMs suffer significant performance degradation. Our analysis reveals that LRMs exhibit limited effective reasoning length and struggle to allocate thinking budget across multiple problems appropriately. Recognizing these limitations, we use R-HORIZON to construct long-horizon reasoning data for reinforcement learning with verified rewards (RLVR). Compared to training with single-horizon data, RLVR with R-HORIZON not only substantially improves performance on the multi-horizon reasoning tasks, but also promotes accuracy on standard reasoning tasks (+7.5 on AIME2024). These results position R-HORIZON as a scalable, controllable, and low-cost paradigm for enhancing and evaluating the long-horizon reasoning capabilities of LRMs.
Shift-and-Sum Quantization for Visual Autoregressive Models
Authors: Jaehyeon Moon; Bumsub Ham
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Post-training quantization (PTQ) enables efficient deployment of deep networks using a small set of data. Its application to visual autoregressive models (VAR), however, remains relatively unexplored. We identify two key challenges for applying PTQ to VAR: (i) large reconstruction errors in attention–value products, especially at coarse scales where high attention scores occur more frequently; and (ii) a discrepancy between the sampling frequencies of codebook entries and their predicted probabilities due to limited calibration data. To address these challenges, we propose a PTQ framework tailored for VAR. First, we introduce a shift-and-sum quantization method that reduces reconstruction errors by aggregating quantized results from symmetrically shifted duplicates of value tokens. Second, we present a resampling strategy for calibration data that aligns sampling frequencies of codebook entries with their predicted probabilities. Experiments on class-conditional image generation, in-painting, out-painting, and class-conditional editing show consistent improvements across VAR architectures, establishing a new state of the art in PTQ for VAR.
Tokenization to Transfer: Do Genomic Foundation Models Learn Good Representations?
Authors: Kirill Vishniakov; Karthik Viswanathan; Aleksandr Medvedev; Praveenkumar Kanithi; Marco AF Pimentel; Ronnie Rajan; Shadab Khan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of Large Language Models has inspired the development of Genomic Foundation Models (GFMs) through similar pretraining techniques. However, the relationship between pretraining performance and effectiveness in downstream genomic tasks remains unclear. Additionally, the high computational cost of pretraining raises questions about its cost-efficiency. To assess the usefulness of pretraining in genomics, we evaluated seven different GFMs across 52 diverse genomic tasks, comparing them to their counterparts with randomly initialized weights. Across benchmarks, we find that randomly initialized models provide surprisingly strong baselines and tokenizer and architecture choices strongly shape both these baselines and the gains from pretraining. Specifically, character‑token models often match or exceed the performance of larger pretrained k‑mer or BPE models, whereas subword models appear to benefit from pretraining. We also find that the evaluated GFMs fail to capture clinically relevant genetic mutations, with embeddings and log‑likelihood ratios showing limited sensitivity to annotated variants. For the tasks we study, these results suggest that current NLP‑style pretraining strategies provide modest, tokenizer‑gated improvements over strong random baselines and motivate more biologically informed tokenization and variant‑aware objectives. Our code is available at https://github.com/m42-health/gfm-random-eval.
COSA: Context-aware Output-Space Adapter for Test-Time Adaptation in Time Series Forecasting
Authors: Jeonghwan Im; Hyuk-Yoon Kwon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deployed time-series forecasters suffer performance degradation under non-stationarity and distribution shifts.
Test-time adaptation (TTA) for time-series forecasting differs from vision TTA because ground truth becomes observable shortly after prediction. 
Existing time-series TTA methods typically employ dual input/output adapters that indirectly modify data distributions, making their effect on the frozen model difficult to analyze. 
We introduce the Context-aware Output-Space Adapter (COSA), a minimal, plug-and-play adapter that directly corrects predictions of a frozen base model.
COSA performs residual correction modulated by gating, utilizing the original prediction and a lightweight context vector that summarizes statistics from recently observed ground truth.
At test time, only the adapter parameters (linear layer and gating) are updated under a leakage-free protocol, using observed ground truth with an adaptive learning rate schedule for faster adaptation.
Across diverse scenarios, COSA demonstrates substantial performance gains versus baselines without TTA (13.91$\sim$17.03\%) and SOTA TTA methods (10.48$\sim$13.05\%), with particularly large improvements at long horizons, while adding a reasonable level of parameters and negligible computational overhead.
The simplicity of COSA makes it architecture-agnostic and deployment-friendly.
Source code: https://github.com/bigbases/COSA_ICLR2026
GTA1: GUI Test-time Scaling Agent
Authors: Yan Yang; Dongxu Li; Yutong Dai; Yuhao Yang; Ziyang Luo; Zirui Zhao; Zhiyuan Hu; JUNZHE HUANG; Amrita Saha; Zeyuan Chen; Ran Xu; Liyuan Pan; Caiming Xiong; Junnan Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graphical user interface (GUI) agents autonomously complete tasks across platforms (\eg, Linux) by sequentially decomposing user instructions into action proposals that iteratively interact with visual elements in the evolving environment. However, two main challenges arise: i) planning (\ie, the action proposal sequence) under expansive action space, where selecting an appropriate plan is non-trivial, as many valid ones may exist; ii) accurately grounding actions in complex and high-resolution interfaces, \ie, precisely interacting with visual targets. This paper investigates the aforementioned challenges with our \textbf{G}UI \textbf{T}est-time Scaling \textbf{A}gent, namely \name. First, we conduct test-time scaling to select the most appropriate action proposal: at each step, multiple candidate proposals are sampled and evaluated and selected by a judge model. It trades off computation for better decision quality by concurrent sampling. Second, we propose a model that improves grounding of the selected action proposals to its corresponding visual elements. Our key insight is that reinforcement learning (RL) facilitates grounding through inherent objective alignments, rewarding successful clicks on interface elements. Experimentally, \name achieves state-of-the-art performance on both grounding and agent task execution benchmarks.
Composition-Grounded Data Synthesis for Visual Reasoning
Authors: Xinyi Gu; Jiayuan Mao; Zhang-Wei Hong; Zhuoran Yu; Pengyuan Li; Dhiraj Joshi; Rogerio Feris; Zexue He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pretrained multi-modal large language models (MLLMs) demonstrate strong performance on diverse multimodal tasks, but remain limited in reasoning capabilities for domains where annotations are difficult to collect. In this work, we focus on artificial image domains such as charts, rendered documents, and webpages, which are abundant in practice yet lack large-scale human annotated reasoning datasets. We introduce COGS (COmposition-Grounded data Synthesis), a data-efficient framework for equipping MLLMs with advanced reasoning abilities from a small set of seed questions. The key idea is to decompose each seed question into primitive perception and reasoning *factors*, which can then be systematically recomposed with new images to generate large collections of synthetic question-answer pairs. Each generated question is paired with subquestions and intermediate answers, enabling reinforcement learning with factor-level process rewards. Experiments on chart reasoning show that COGS substantially improves performance on unseen questions, with the largest gains on reasoning-heavy and compositional questions. Moreover, training with a factor-level mixture of different seed data yields better transfer across multiple datasets, suggesting that COGS induces generalizable capabilities rather than dataset-specific overfitting. We further demonstrate that the framework extends beyond charts to other domains such as webpages. We release the code and data at https://cogsynthesis.github.io.
Sim2Real VLA: Zero-Shot Generalization of Synthesized Skills to Realistic Manipulation
Authors: Runyi Zhao; Sheng Xu; Ruixing Jin; Yueci Deng; Yunxin Tai; Kui Jia; Guiliang Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language-Action (VLA) models represent a critical milestone toward embodied intelligence in robotic manipulation. To support their training, recent research has developed high-performance simulation engines for data synthesis. However, their effectiveness is still significantly limited by the simulation-to-reality (Sim2Real) gap, as policies trained on synthetic data often fail to generalize reliably to the real world. To address this challenge, we present Sim2Real-VLA, a generalist robot control model trained exclusively on synthetic data, yet capable of transferring seamlessly to real-world manipulation tasks. Sim2Real-VLA features a dual-system architecture: a high-level planner that infers object-centered chains-of-affordances, and a low-level actor that executes and validates these plans in real time via a tokenized action space. This design filters out manipulation-irrelevant features and prioritizes motion-critical dynamics, thereby enhancing Sim2Real domain transfer. Besides, a notable advantage of Sim2Real-VLA lies in its tight integration with automated data generation for manipulation skills, eliminating the need for manual fine-tuning and enabling scalable, hands-free training. Empirical evaluations across bimanual, dexterous, and long-horizon tasks show that Sim2Real-VLA consistently outperforms previous VLA baselines under diverse real-world environments and domain shifts.
Expressive yet Efficient Feature Expansion with Adaptive Cross-Hadamard Products
Authors: Xuyang Zhang; Xi Zhang; Liang Chen; Hao Shi; Qingshan Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent theoretical advances reveal that the Hadamard product induces nonlinear representations and implicit high-dimensional mappings for the field of deep learning, yet their practical deployment in resource-constrained vision models remains largely unexplored. To address this gap, we introduce the Adaptive Cross-Hadamard (ACH) module, a novel operator that embeds learnability through differentiable discrete sampling and dynamic softsign normalization. This facilitates highly efficient feature reuse without incurring additional convolutional parameters, while ensuring stable gradient flow. Integrated into Hadaptive-Net (Hadamard Adaptive Network) via neural architecture search, our approach achieves unprecedented efficiency. Comprehensive experiments demonstrate state-of-the-art accuracy/speed trade-offs on image classification tasks, establishing Hadamard operations as specific building blocks for efficient vision models.
Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals
Authors: Octavio Pappalardo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks. A promising direction, grounded in human development, investigates agents that learn by setting and pursuing their own goals. The core challenge lies in how to effectively generate, select, and learn from such goals. Our focus is on broad distributions of downstream tasks where solving every task zero-shot is infeasible. Such settings naturally arise when the target tasks lie outside of the pre-training distribution or when their identities are unknown to the agent. In this work, we (i) optimize for efficient multi-episode exploration and adaptation within a meta-learning framework, and (ii) guide the training curriculum with evolving estimates of the agent’s post-adaptation performance. We present ULEE, an unsupervised meta-learning method that combines an in-context learner with an adversarial goal-generation strategy that maintains training at the frontier of the agent’s capabilities. On XLand-MiniGrid benchmarks, ULEE pre-training yields improved exploration and adaptation abilities that generalize to novel objectives, environment dynamics, and map structures. The resulting policy attains improved zero-shot and few-shot performance, and provides a strong initialization for longer fine-tuning processes. It outperforms learning from scratch, DIAYN pre-training, and alternative curricula.
Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking
Authors: Wen Wen; tianwu zhi; Kanglong FAN; Yang Li; Xinge Peng; Yabin ZHANG; Yiting Liao; Junlin Li; Li zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Improving vision-language models (VLMs) in the post-training stage typically relies on supervised fine-tuning or reinforcement learning, methods that necessitate costly, human-annotated data.  While self-supervised techniques such as self-consistency have proven effective for enhancing reasoning capabilities, their application to perceptual domains such as image quality assessment (IQA) remains largely unexplored.  In this work, we introduce EvoQuality, a novel framework that enables a VLM to autonomously refine its quality perception capabilities without any ground-truth labels.  EvoQuality adapts the principle of self-consistency to the ranking-based nature of IQA.  It generates pseudo-labels by performing pairwise majority voting on the VLM's own outputs to establish a consensus on relative quality.  These pseudo-rankings are then formulated into a fidelity reward that guides the model's iterative evolution through group relative policy optimization (GRPO).  By iteratively leveraging its own predictions, EvoQuality progressively refines the VLM’s perceptual capability. Extensive experiments show that EvoQuality boosts the base VLM's zero-shot performance by 31.8% on PLCC across diverse IQA benchmarks. Remarkably, despite being entirely self-supervised, EvoQuality achieves performance that is competitive with, or even surpasses, state-of-the-art supervised VLM-based IQA models, outperforming these models on 5 out of 7 IQA benchmarks. Furthermore, the framework demonstrates significant flexibility, allowing it to be stacked with pre-trained IQA models to bolster generalization on unseen datasets. Codes and checkpoints will be available at \url{https://github.com/bytedance/EvoQuality}.
SSD-GS: Scattering and Shadow Decomposition for Relightable 3D Gaussian Splatting
Authors: Iris Zheng; Guojun Tang; Alexander Doronin; Paul D. Teal; Fang-Lue Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photorealistic relighting under novel lighting conditions. In physically-based relighting, accurately modeling light-material interactions is essential for faithful appearance reproduction. However, existing 3DGS-based relighting methods adopt coarse shading decompositions, either modeling only diffuse and specular reflections or relying on neural networks to approximate shadows and scattering. This leads to limited fidelity and poor physical interpretability, particularly for anisotropic metals and translucent materials. To address these limitations, SSD-GS decomposes reflectance into four components: diffuse, specular, shadow, and subsurface scattering. We introduce a learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation that integrates visibility estimates with a refinement network, and an enhanced specular component with an anisotropic Fresnel-based model. Through progressive integration of all components during training, SSD-GS effectively disentangles lighting and material properties, even for unseen illumination conditions, as demonstrated on the challenging OLAT dataset. Experiments demonstrate superior quantitative and perceptual relighting quality compared to prior methods and pave the way for downstream tasks including controllable light source editing and interactive scene relighting. The source code is available at: [https://github.com/irisfreesiri/SSD-GS](https://github.com/irisfreesiri/SSD-GS).
LucidFlux: Caption-Free Photo-Realistic Image Restoration via a Large-Scale Diffusion Transformer
Authors: Song Fei; Tian Ye; Lujia Wang; Lei Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Universal image restoration (UIR) aims to recover images degraded by unknown mixtures while preserving semantics—conditions under which discriminative restorers and UNet-based diffusion priors often oversmooth, hallucinate, or drift. We present LucidFlux, a caption-free UIR framework that adapts a large diffusion transformer (Flux.1) to restoration with minimal parameter overhead. LucidFlux introduces a lightweight \emph{dual-branch conditioner} that injects signals from the degraded input and a lightly restored proxy to respectively anchor geometry and suppress artifacts. A timestep- and layer-adaptive modulation schedule routes these cues across the backbone’s hierarchy, yielding coarse-to-fine, context-aware updates that protect global structure while recovering texture. To avoid the latency and instability of text prompts or VLM captions, we enforce \emph{caption-free semantic alignment} via SigLIP features extracted from the proxy. A scalable curation pipeline further filters large-scale data for structure-rich supervision.  Across synthetic and in-the-wild benchmarks, LucidFlux consistently surpasses strong open-source and commercial baselines across seven metrics, with clear visual gains in realism, detail, and artifact suppression. Ablations confirm that, for large DiTs, when, where, and what to condition—rather than scaling parameters or relying on text prompts—is the key lever for robust, prompt-free restoration.