GenAI4ELab/papercli-papers huggingface dataset:
MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents
Authors: Yifan Xu; Xiao Liu; Xinghan Liu; Jiaqi Fu; Jiayu Huang; Hanchen Zhang; Bohao Jing; Shudan Zhang; Yuting Wang; Zhao wenyi; Yuxiao Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile GUI agents with reinforcement learning (RL) remains challenging due to the heavy-tailed distribution of task difficulty and the inefficiency of large-scale environment sampling. We present an online agentic reinforcement learning framework MOBILERL to enhance GUI agents in mobile environments. Its core component is the Difficulty-Adaptive GRPO (ADAGRPO) algorithm. In ADAGRPO, we design difficulty-adaptive positive replay and failure curriculum filtering to adapt the model to different task difficulties. We introduce the shortest-path reward adjustment strategy to reshape rewards concerning the task length in multi-turn agentic tasks. Those strategies jointly stabilize RL training, improve sample efficiency, and generate strong performance across diverse mobile apps and tasks. We apply MOBILERL to two open models (Qwen2.5-VL-7B-Instruct and GLM-4.1V-9B-Base). The resultant MOBILERL-9B model achieves state-of-the-art results in terms of success rates on both AndroidWorld (80.2%) and Android-Lab (53.6%). The MOBILERL is open-sourced at https://github.com/THUDM/MobileRL.
FastVMT: Eliminating Redundancy in Video Motion Transfer
Authors: Yue Ma; Zhikai Wang; Tianhao Ren; Mingzhe Zheng; Hongyu Liu; Jiayi Guo; Kunyu Feng; Yuxuan Xue; Zixiang Zhao; Konrad Schindler; Qifeng Chen; Linfeng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed in a reference video. Recent methods predominantly use the Diffusion Transformer (DiT) architecture. To achieve satisfactory runtime, several methods attempt to accelerate the computations in the DiT, but fail to address structural sources of inefficiency. In this work, we identify and remove two types of computational redundancy in earlier work: **motion redundancy** arises because the generic DiT architecture does not reflect the fact that frame-to-frame motion is small and smooth; **gradient redundancy** occurs if one ignores that gradients change slowly along the diffusion trajectory. To mitigate motion redundancy, we mask the corresponding attention layers to a local neighborhood such that interaction weights are not computed unnecessarily distant image regions. To exploit gradient redundancy, we design an optimization scheme that reuses gradients from previous diffusion steps and skips unwarranted gradient computations. On average, FastVMT achieves a 3.43× speedup without degrading the visual fidelity or the temporal consistency of the generated videos.
SURGE: Surprise-Guided Token Reduction for Efficient Video Understanding with VLMs
Authors: Chong Tang; Sannara Ek; Dirk Koch; Robert D. Mullins; Alex S. Weddell; Jagmohan Chauhan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Videos contain rich information but also high redundancy, as consecutive frames often share similar backgrounds and predictable motions. Current video-language models (VLMs) are unable to exploit this redundancy and therefore perform a significant amount of superfluous computation, processing thousands of patch tokens even when little new information is present. What is missing is an on-the-fly, model-agnostic signal of temporal predictability to decide whether tokens carry unpredictable information that merits computation. We propose SURGE, a training-free and backbone-agnostic method that measures surprise in token space. Surprise scores are defined by the prediction error of each token from its recent history; high-surprise tokens are retained, while predictable ones are pruned. Aggregating scores over time produces a surprise curve that highlights key events, which can be further refined with CLIP-based query relevance to form a compact spatio-temporal mask. Experiments on multiple video understanding benchmarks show that SURGE reduces tokens by up to 7$\times$ and prefill cost by 86–98\%, while maintaining accuracy within $\pm$1 point of full-token baselines. By aligning computation with novelty, SURGE enables video VLMs to handle long contexts efficiently and without retraining.
Uni-X: Mitigating Modality Conflict with a Two-End-Separated Architecture for Unified Multimodal Models
Authors: Jitai Hao; Hao Liu; Xinyan Xiao; Qiang Huang; Jun Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified Multimodal Models (UMMs) built on shared autoregressive (AR) transformers are attractive for their architectural simplicity. However, we identify a critical limitation: when trained on multimodal inputs, modality-shared transformers suffer from severe gradient conflicts between vision and text, particularly in shallow and deep layers. We trace this issue to the fundamentally different low-level statistical properties of images and text, while noting that conflicts diminish in middle layers where representations become more abstract and semantically aligned. 
To overcome this challenge, we propose Uni-X, a two-end-separated, middle-shared architecture. Uni-X dedicates its initial and final layers to modality-specific processing, while maintaining shared parameters in the middle layers for high-level semantic fusion. This X-shaped design not only eliminates gradient conflicts at both ends but also further alleviates residual conflicts in the shared layers.
Extensive experiments validate the effectiveness of Uni-X. Under identical training conditions, Uni-X achieves superior training efficiency compared to strong baselines. When scaled to 3B parameters with larger training data, Uni-X matches or surpasses 7B AR-based UMMs, achieving a GenEval score of 82 for image generation alongside strong performance in text and vision understanding tasks.
These results establish Uni-X as a parameter-efficient and scalable foundation for future unified multimodal modeling.
Our code is available at https://github.com/CURRENTF/Uni-X.
Domain Expansion: A Latent Space Construction Framework for Multi-Task Learning
Authors: Chi-Yao Huang; Khoa Vo; Aayush Atul Verma; Duo Lu; Yezhou Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training a single network with multiple objectives often leads to conflicting gradients that degrade shared representations, forcing them into a compromised state that is suboptimal for any single task—a problem we term latent representation collapse. We introduce Domain Expansion, a framework that prevents these conflicts by restructuring the latent space itself. Our framework uses a novel orthogonal pooling to construct a latent space where each objective is assigned to a mutually orthogonal subspace. We validate our approach on the ShapeNet benchmark, simultaneously training a model for object classification and pose estimation. Our experiments demonstrate that this structure not only prevents collapse but also yields an explicit, interpretable, and compositional latent space where concepts can be directly manipulated.
Condition Matters in Full-head 3D GANs
Authors: Heyuan Li; Huimin Zhang; Yuda Qiu; Zhengwentai Sun; Keru Zheng; Lingteng Qiu; Peihao Li; Qi Zuo; Ce Chen; Yujian Zheng; Yuming Gu; Zilong Dong; Xiaoguang Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conditioning is crucial for stable training of full-head 3D-aware GANs. Without any conditioning signal, the model suffers from severe mode collapse, making it impractical to training (\cref{fig:intro}(a,b)). However, a series of previous full-head 3D-aware GANs conventionally choose the view angle as the conditioning input, which leads to a bias in the learned 3D full-head space along the conditional view direction. This is evident in the significant differences in generation quality and diversity between the conditional view and non-conditional views of the generated 3D heads, resulting in global incoherence across different head regions (\cref{fig:intro}(d-i)). In this work, we propose to use \textit{view-invariant semantic feature} as the conditioning input, thereby decoupling the generative capability of 3D heads from the viewing direction. To construct a view-invariant semantic condition for each training image, we create a novel synthesized head image dataset. We leverage FLUX.1 Kontext to extend existing high-quality frontal face datasets to a wide range of view angles. The image clip feature extracted from the frontal view is then used as a shared semantic condition across all views in the extended images, ensuring semantic alignment while eliminating directional bias. This also allows supervision from different views of the same subject to be consolidated under a shared semantic condition, which accelerates training (\cref{fig:intro}(c)) and enhances the global coherence of the generated 3D heads (\cref{fig:teaser}). Moreover, as GANs often experience slower improvements in diversity once the generator learns a few modes that successfully fool the discriminator, our semantic conditioning encourages the generator to follow the true semantic distribution, thereby promoting continuous learning and diverse generation. Extensive experiments on full-head synthesis and single-view GAN inversion demonstrate that our method achieves significantly higher fidelity, diversity, and generalizability.
Why Keep Your Doubts to Yourself? Trading Visual Uncertainties among Vision-Language Models
Authors: Jusheng Zhang; Yijia Fan; Kaitong Cai; Jing Yang; Jiawei Yao; Jian Wang; Guanlong Qu; Ziliang Chen; Keze Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language Models (VLMs) enable powerful multi-agent systems, but scaling them is economically unsustainable: coordinating heterogeneous agents under information asymmetry often spirals costs. Existing paradigms, such as Mixture-of-Agents and knowledge-based routers, rely on heuristic proxies that ignore costs and collapse uncertainty structure, leading to provably suboptimal coordination.
We introduce Agora, a framework that reframes coordination as a decentralized market for uncertainty. Agora formalizes epistemic uncertainty into a structured, tradable asset (perceptual, semantic, inferential), and enforces profitability-driven trading among agents based on rational economic rules. A market-aware broker, extending Thompson Sampling, initiates collaboration and guides the system toward cost-efficient equilibria. Experiments on five multimodal benchmarks (MMMU, MMBench, MathVision, InfoVQA, CC-OCR) show that Agora outperforms strong VLMs and heuristic multi-agent strategies, e.g., achieving +8.5% accuracy over the best baseline on MMMU while reducing cost by over 3×. These results establish market-based coordination as a principled and scalable paradigm for building economically viable multi-agent visual intelligence systems.
Codified Finite-state Machines for Role-playing
Authors: Letian Peng; Yupeng Hou; Kun Zhou; Jingbo Shang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling latent character states is crucial for consistent and engaging role-playing (RP) with large language models (LLMs).
Yet, existing prompting-based approaches mainly capture surface actions, often failing to track the latent states that drive interaction. 
We revisit finite-state machines (FSMs), long used in game design to model state transitions.
While effective in small, well-specified state spaces, traditional hand-crafted, rule-based FSMs struggle to adapt to the open-ended semantic space of RP.
To address this, we introduce Codified Finite-State Machines (CFSMs), a framework that automatically codifies textual character profiles into FSMs using LLM-based coding.
CFSMs extract key states and transitions directly from the profile, producing interpretable structures that enforce character consistency.
To further capture uncertainty and variability, we extend CFSMs into Codified Probabilistic Finite-State Machines (CPFSMs), where transitions are modeled as probability distributions over states.
Through both synthetic evaluations and real-world RP scenarios in established artifacts, we demonstrate that CFSM and CPFSM outperform generally applied baselines, verifying effectiveness not only in structured tasks but also in open-ended stochastic state exploration.
Universal Properties of Activation Sparsity in Modern Large Language Models
Authors: Filip Szatkowski; Patryk Będkowski; Alessio Devoto; Jan Dubiński; Pasquale Minervini; Mikołaj Piórczyński; Simone Scardapane; Bartosz Wójcik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and interpretability. 
However, methods relying on exact zero activations do not directly apply to modern Large Language Models (LLMs), leading to fragmented, model-specific strategies for LLM activation sparsity and a gap in its general understanding. 
In this work, we introduce a general framework for evaluating sparsity robustness in contemporary LLMs and conduct a systematic investigation of this phenomenon in their feedforward~(FFN) layers.
Our results uncover universal properties of activation sparsity across diverse model families and scales.
Importantly, we observe that the potential for effective activation sparsity grows with model size, highlighting its increasing relevance as models scale. 
Furthermore, we present the first study of activation sparsity in diffusion-based LLMs. 
Overall, our work provides a comprehensive perspective and practical guidance for harnessing activation sparsity in LLM design and acceleration.
Neural Synchrony Between Socially Interacting Language Models
Authors: Zhining Zhang; Wentao Zhu; Chi Han; Yizhou Wang; Heng Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neuroscience has uncovered a fundamental mechanism of our social nature: human brain activity becomes synchronized with others in many social contexts involving interaction. Traditionally, social minds have been regarded as an exclusive property of living beings.  Although large language models (LLMs) are widely accepted as powerful approximations of human behavior, with multi-LLM system being extensively explored to enhance their capabilities, it remains controversial whether they can be meaningfully compared to human social minds. In this work, we explore neural synchrony between socially interacting LLMs as an empirical evidence for this debate. Specifically, we introduce neural synchrony during social simulations as a novel proxy for analyzing the sociality of LLMs at the representational level. Through carefully designed experiments, we demonstrate that it reliably reflects both social engagement and temporal alignment in their interactions. Our findings indicate that neural synchrony between LLMs is strongly correlated with their social performance, highlighting an important link between neural synchrony and the social behaviors of LLMs. Our work offers a new perspective to examine the "social minds" of LLMs, highlighting surprising parallels in the internal dynamics that underlie human and LLM social interaction.
InSight-o3: Empowering Multimodal Foundation Models with Generalized Visual Search
Authors: Kaican Li; Lewei Yao; Jiannan Wu; Tiezheng YU; Jierun Chen; Haoli Bai; Lu Hou; Lanqing HONG; Wei Zhang; Nevin L. Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The ability for AI agents to "think with images" requires a sophisticated blend of reasoning and perception.
However, current open multimodal agents still largely fall short on the reasoning aspect crucial for real-world tasks like analyzing documents with dense charts/diagrams and navigating maps.
To address this gap, we introduce O3-bench, a new benchmark designed to evaluate multimodal reasoning with interleaved attention to visual details.
O3-bench features challenging problems that require agents to piece together subtle visual information from distinct image areas through multi-step reasoning.
The problems are highly challenging even for frontier systems like OpenAI o3, which only obtains 40.8\% accuracy on O3-bench.
To make progress, we propose InSight-o3, a multi-agent framework consisting of a visual reasoning agent (vReasoner) and a visual search agent (vSearcher) for which we introduce the task of generalized visual search---locating relational, fuzzy, or conceptual regions described in free-form language, beyond just simple objects or figures in natural images.
We then present a multimodal LLM purpose-trained for this task via reinforcement learning.
As a plug-and-play agent, our vSearcher empowers frontier multimodal models (as vReasoners), significantly improving their performance on a wide range of benchmarks.
This marks a concrete step towards powerful o3-like open systems.
Our code and dataset can be found at https://github.com/m-Just/InSight-o3.
JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence
Authors: Qiushi Sun; Jingyang Gong; Yang Liu; Qiaosheng Chen; Lei Li; Kai Chen; Qipeng Guo; Ben Kao; Fei Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate. This visual dimension is critical for advanced applications like flexible content generation and precise, program-driven editing of visualizations. However, progress has been impeded by the scarcity of high-quality multimodal code data, a bottleneck stemming from challenges in synthesis and quality assessment. To address these challenges, we make contributions from both a data and modeling perspective. We first introduce a complete synthesis toolkit that leverages reciprocal synergies between data modalities to efficiently produce a large-scale, high-quality corpus spanning from standard charts to complex interactive web UIs and code-driven animations. Leveraging this toolkit, we construct JanusCode-800K, the largest multimodal code corpus to date. This powers the training of our models, JanusCoder and JanusCoderV, which establish a visual-programmatic interface for generating code from textual instructions, visual inputs, or a combination of both. Our unified model is a departure from existing approaches that build specialized models for isolated tasks. Extensive experiments on both text-centric and vision-centric coding tasks demonstrate the superior performance of the JanusCoder series, with our 7B to 14B scale models approaching or even exceeding the performance of commercial models. Furthermore, extensive analysis provides key insights into harmonizing programmatic logic with its visual expression. Our code and checkpoints are available at \url{https://github.com/InternLM/JanusCoder}.
ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
Authors: Qiushi Sun; Zhoumianze Liu; Chang Ma; Zichen Ding; Fangzhi Xu; Zhangyue Yin; Haiteng Zhao; Zhenyu Wu; Kanzhi Cheng; Zhaoyang Liu; Jianing Wang; Qintong Li; Xiangru Tang; Tianbao Xie; Xiachong Feng; Xiang Li; Ben Kao; Wenhai Wang; Biqing Qi; Lingpeng Kong; Zhiyong Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdisciplinary research. Recently, various LLM-based agents have been developed to assist scientific discovery progress across multiple aspects and domains. Among these, computer-using agents, capable of interacting with operating systems as humans do, are paving the way to automated scientific problem-solving and addressing routines in researchers' workflows. Recognizing the transformative potential of these agents, we introduce ScienceBoard, which encompasses two complementary contributions: (i) a realistic, multi-domain environment featuring dynamic and visually rich scientific workflows with integrated professional software, where agents can autonomously interact via different interfaces to accelerate complex research tasks and experiments; and (ii) a challenging benchmark of 169 high-quality, rigorously validated real-world tasks curated by humans, spanning scientific-discovery workflows in domains such as biochemistry, astronomy, and geoinformatics. Extensive evaluations of agents with state-of-the-art backbones (e.g., GPT-5, Claude-Opus-4.6, UI-TARS) show that, despite some promising results, they still fall short of reliably assisting scientists in complex workflows, achieving only a 15% overall success rate. In-depth analysis further provides valuable insights for addressing current agent limitations and more effective design principles, paving the way to build more capable agents for scientific discovery. Our code, benchmark, and leaderboard are available at https://qiushisun.github.io/ScienceBoard-Home/.
Diverse Dictionary Learning
Authors: Yujia Zheng; Zijian Li; Shunxing Fan; Andrew Gordon Wilson; Kun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Given only observational data $X = g(Z)$, where both the latent variables $Z$ and the generating process $g$ are unknown, recovering $Z$ is ill-posed without additional assumptions. Existing methods often assume linearity or rely on auxiliary supervision and functional constraints. However, such assumptions are rarely verifiable in practice, and most theoretical guarantees break down under even mild violations, leaving uncertainty about how to reliably understand the hidden world. To make identifiability *actionable* in the real-world scenarios, we take a complementary view: in the general settings where full identifiability is unattainable, *what can still be recovered with guarantees*, and *what biases could be universally adopted*? We introduce the problem of *diverse dictionary learning* to formalize this view. Specifically, we show that intersections, complements, and symmetric differences of latent variables linked to arbitrary observations, along with the latent-to-observed dependency structure, are still identifiable up to appropriate indeterminacies even without strong assumptions. These set-theoretic results can be composed using set algebra to construct structured and essential views of the hidden world, such as *genus-differentia* definitions. When sufficient structural diversity is present, they further imply full identifiability of all latent variables. Notably, all identifiability benefits follow from a simple inductive bias during estimation that can be readily integrated into most models. We validate the theory and demonstrate the benefits of the bias on both synthetic and real-world data.