GenAI4ELab/papercli-papers huggingface dataset:
WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
Authors: Jack Hong; Shilin Yan; Jiayin Cai; Xiaolong Jiang; Yao Hu; Weidi Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce WorldSense, the first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs. In contrast to existing benchmarks, our WorldSense has several features: (i) collaboration of omni-modality, we design the evaluation tasks to feature a strong coupling of audio and video, requiring models to effectively utilize the synergistic perception of omni-modality; (ii) diversity of videos and tasks, WorldSense encompasses a diverse collection of 1,662 audio-visual synchronised videos, systematically categorized into 8 primary domains and 67 fine-grained subcategories to cover the broad scenarios, and 3,172 multi-choice QA pairs across 26 distinct tasks to enable the comprehensive evaluation; (iii) high-quality annotations, all the QA pairs are manually labeled by 80 expert annotators with multiple rounds of correction to ensure quality. Based on our WorldSense, we extensively evaluate various state-of-the-art models. The experimental results indicate that existing models face significant challenges in understanding real-world scenarios (65.1% best accuracy). By analyzing the limitations of current models, we aim to provide valuable insight to guide development of real-world understanding. We hope our WorldSense can provide a platform for evaluating the ability in constructing and understanding coherent contexts from omni-modality.
SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
Authors: Zhuoheng Gao; Yihao Li; Jiyao Zhang; Rui Zhao; Tong Wu; Hao Tang; Zhaofei Yu; Hao Dong; Guozhang Chen; Tiejun Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microsecond-level resolution, providing an alternative sensing modality. However, existing methods lack specialized stereo algorithms and benchmarks tailored to the spike data. To address this gap, we propose SpikeStereoNet, a brain-inspired framework to estimate stereo depth directly from raw spike streams. The model fuses raw spike streams from two viewpoints and iteratively refines depth estimation through a recurrent spiking neural network (RSNN) update module. To benchmark our approach, we introduce a large-scale synthetic spike stream dataset and a real-world stereo spike dataset with dense depth annotations. SpikeStereoNet outperforms existing methods on both datasets by leveraging spike streams' ability to capture subtle edges and intensity shifts in challenging regions such as textureless surfaces and extreme lighting conditions. Furthermore, our framework exhibits strong data efficiency, maintaining high accuracy even with substantially reduced training data.
iFusion: Integrating Dynamic Interest Streams via Diffusion Model for Click-Through Rate Prediction
Authors: Ziheng Ni; Congcong Liu; Yuying Chen; Zhiwei Fang; Changping Peng; Zhangang Lin; Ching Law; Jingping Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Click-through rate (CTR) prediction is crucial for recommendation systems and online advertising, relying heavily on effective user behavior modeling. While existing methods separately refine long-term and short-term interest representations, the fusion of these behaviors remains a critical yet understudied challenge due to misaligned feature spaces, disjointed modeling, and noise propagation in short-term interests. To address these limitations, we propose iFusion, a diffusion-based generative user interest fusion method, which reformulates interest fusion as a conditional generation process. iFusion leverages short-term interests as conditional guidance and progressively integrates long-term representations through denoising, eliminating reliance on linear fusion assumptions. Our framework introduces two key components: (1) the Disentangled Classifier-Free Diffusion Guidance (DCFG) Mechanism, which adaptively disentangles core preferences from transient fluctuations, and (2) the Mixture AutoRegressive Denoising Network (MARN), which enables joint interest modeling and fusion through autoregressive denoising. Experiments demonstrate that iFusion outperforms baselines across public and industrial datasets, as well as in online A/B tests, validating its effectiveness in robust CTR prediction. This work establishes a new paradigm for generative user interests  fusion in CTR prediction.
IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video?
Authors: Yang Chen; Minghao Liu; Yufan Shen; Yunwen Li; Tianyuan Huang; Xinyu Fang; Tianyu Zheng; Wenxuan Huang; Cheng Yang; Licheng Wen; Xuemeng Yang; Daocheng Fu; Jianbiao Mei; Rong Wu; Song Mao; Qunshu Lin; Zhi Yu; Yongliang Shen; Yu Qiao; Botian Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code.
However, existing benchmarks primarily focus on static screenshot-to-code tasks, thereby overlooking the dynamic interactions fundamental to real-world web applications.
To address this limitation, this paper introduces IWR-Bench, a novel benchmark for evaluating the capabilities of Large Vision-Language Models (LVLMs) in interactive webpage reconstruction from video.
IWR-Bench comprises 113 meticulously curated tasks from 100 real-world websites, with 1,001 actions and featuring diverse interaction complexities (e.g., web games), visual styles, and domains.
Aligning with standard web development practices, each task includes not only user interaction videos but also all crawled static assets (e.g., images, videos).
This benchmark evaluates models on two fundamental challenges: comprehensive multi-modal reasoning to infer interaction logic from video and assets, and advanced code generation to translate this logic into functional code.
An agent-as-a-judge framework with a comprehensive metric system automatically assesses the functional correctness and visual fidelity of generated webpages.
Extensive experiments on 28 LVLMs reveal a significant challenge: the best model achieves an overall score of only 36.35\%, as functional correctness (24.39\% IFS) lags significantly behind visual fidelity (64.25\% VFS).
These results highlight critical limitations in current models' ability to reason about temporal dynamics and synthesize event-driven logic, establishing IWR-Bench as a challenging frontier for vision-language research.
The benchmark and evaluation code will be made publicly available.
Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Models
Authors: Tianren Ma; Mu Zhang; Yibing Wang; Qixiang Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Optimizing discrete diffusion model (DDM) with rewards remains a challenge—the non-autoregressive paradigm makes importance sampling intractable and rollout complex, puzzling reinforcement learning methods such as Group Relative Policy
Optimization (GRPO). In this study, we introduce **MaskGRPO**, the first viable approach to enable scalable multimodal reinforcement learning in discrete diffusion with effective importance sampling and modality-specific adaptations. To this end,
we first clarify the theoretical foundation for DDMs, which facilitates building an importance estimator that captures valuable token fluctuation for gradient updates. We then delicately tailored the rollout method for visual sequences, which yields diverse completions and reliable optimization gradients. Across math reasoning, coding, and visual generation benchmarks, MaskGRPO brings more stable and efficient updates, **doubling** reinforcement learning gains while speeding up training by up to **30%**. This study establishes MaskGRPO as a systematic policy optimization approach and the first practical way for discretized visual diffusion. The code is available at https://github.com/martian422/MaskGRPO.
Echoes as Anchors: Probabilistic Costs and Attention Refocusing in LLM Reasoning
Authors: Zhuoyuan Hao; Zhuo Li; Wu Li; Fangming Liu; Min Zhang; Jing Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Test-time compute allocation in large reasoning models (LRMs) is widely used and has applications in mathematical problem solving, code synthesis, and planning. Recent work has addressed this problem by scaling self-consistency and parallel thinking, adding generic thinking tokens and prompting models to re-read the question before answering. Unfortunately, these approaches either inject task-agnostic tokens or mandate heuristics that do not explain---and often ignore---the \emph{spontaneous} repetition that many LRMs exhibit at the head of their internal chains. In contrast, we analyze and harness the model's tendency to restate the question, which we term the \emph{Echo of Prompt (EOP)}, as a front-loaded, compute-shaping mechanism. We formalize its probabilistic cost by casting echo removal as rejection-based conditioning and defining the \emph{Echo Likelihood Gap} $\Delta\mathcal{L}$ as a computable proxy. This provides the missing theoretical link that links early repetition to likelihood gains and downstream accuracy. However, it does not by itself specify how to exploit EOP.  Consequently, we develop \emph{Echo-Distilled SFT (ED-SFT)} to instill an ``echo-then-reason'' pattern through supervised finetuning, and \emph{Echoic Prompting (EP)} to re-ground the model mid-trace without training. While promising, quantifying benefits beyond verbosity is non-trivial. Therefore, we conduct length and suffix-controlled likelihood analyses together with layer-wise attention studies, showing that EOP increases answer to answer-prefix attention in middle layers, consistent with an \emph{attention refocusing} mechanism. We evaluate under identical decoding settings and compute budgets on GSM8K, MathQA, Hendrycks-MATH, AIME24, and MATH-500 under identical decoding settings and budgets, and find consistent gains over baselines.
Video-GPT via Next Clip Diffusion
Authors: Shaobin Zhuang; Zhipeng Huang; Ying Zhang; Fangyikang Wang; Canmiao Fu; Binxin Yang; Chong Sun; Chen Li; Yali Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: GPT has shown its remarkable success in natural language processing. However, the language sequence is not sufficient to describe spatial-temporal details in the visual world. Alternatively, the video sequence is good at capturing such details. Motivated by this fact, we propose a concise Video-GPT in this paper by treating video as new language for visual world modeling. By analogy to next token prediction in GPT, we introduce a novel next clip diffusion paradigm for pretraining Video-GPT. Different from the previous works, this distinct paradigm allows Video-GPT to tackle both short-term generation and long-term prediction, by autoregressively denoising the noisy clip according to the clean clips in the history. Extensive experiments show our Video-GPT achieves the state-of-the-art performance on video prediction, which is the key factor towards world modeling (Physics-IQ Benchmark: Video-GPT 34.97 vs. Kling 23.64 vs. Wan 20.89). Moreover, it can be well adapted on 6 mainstream video tasks in both video generation and understanding, showing its great generalization capacity in downstream.
Prompt-Robust Vision-Language Models via Meta-Finetuning
Authors: Haohui Liang; Runlin Huang; Yingjun Du; Yujia Hu; Weifeng Su; Cees G. M. Snoek
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language models (VLMs) have demonstrated remarkable generalization across diverse tasks by leveraging large-scale image-text pretraining. However, their performance is notoriously unstable under variations in natural language prompts, posing a considerable challenge for reliable real-world deployment. To address this prompt sensitivity, we propose Promise, a meta-learning framework for prompt-Robust vision-language models via meta-finetuning, which explicitly learns to generalize across diverse prompt formulations. Our method operates in a dual-loop meta-finetuning setting: the inner loop adapts token embeddings based on a set of varied prompts, while the outer loop optimizes for generalization on unseen prompt variants. To further improve robustness, we introduce an adaptive prompt weighting mechanism that dynamically emphasizes more generalizable prompts and a token-specific learning rate module that fine-tunes individual prompt tokens based on contextual importance. We further establish that Promise’s weighted and preconditioned inner update provably (i) yields a one-step decrease of the outer empirical risk together with a contraction of across-prompt sensitivity, and (ii) tightens a data-dependent generalization bound evaluated at the post-inner initialization. Across 15 benchmarks spanning base-to-novel generalization, cross-dataset transfer, and domain shift, our approach consistently reduces prompt sensitivity and improves performance stability over existing prompt learning methods.
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Authors: Yuqian Fu; Tinghong Chen; Jiajun Chai; Xihuai Wang; Songjun Tu; Guojun Yin; Wei Lin; Qichao Zhang; Yuanheng Zhu; Dongbin Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have achieved remarkable progress in reasoning tasks, yet optimally integrating Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) remains a fundamental challenge. Through a comprehensive analysis of token distributions, learning dynamics, and integration mechanisms from an entropy-based perspective, we reveal key differences between these paradigms: SFT induces coarse-grained, global shifts to policy distributions, while RL performs fine-grained, selective optimizations.
Our analysis further establishes entropy as a critical indicator of training efficacy. 
Building on these observations, we introduce **S**upervised **R**einforcement **F**ine-**T**uning (**SRFT**), a single-stage framework that unifies both fine-tuning paradigms through entropy-aware weighting mechanisms. 
SRFT simultaneously applies SFT and RL to directly optimize LLMs using demonstrations and self-exploration rollouts rather than through two-stage sequential methods.
Extensive experiments show that SRFT outperforms zero-RL baselines by **9.0%** on five mathematical reasoning benchmarks and by **10.9%** on three out-of-distribution benchmarks.
Moreover, by leveraging demonstration data, SRFT maintains a more stable policy entropy, facilitating sustained policy improvement.
TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
Authors: Guowen Li; Xintong Liu; Yang Liu; Mengxuan Chen; Shilei Cao; Xuehe Wang; Juepeng Zheng; Jinxiao Zhang; Haoyuan Liang; Lixian Zhang; Jiuke Wang; Meng Jin; Hong Cheng; Haohuan Fu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management. However, it remains a challenging and underexplored problem due to the chaotic nature of the weather system. Recent data-driven studies have shown promising results, but their performance is limited by the inadequate incorporation of climate states and a model tendency to degrade, progressively losing fine-scale details and yielding over-smoothed forecasts. To overcome these limitations, we propose TianQuan-S2S, a global S2S forecasting model that integrates initial weather states with climatological means via incorporating climatology into patch embedding and enhancing variability capture through an uncertainty-augmented Transformer. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset demonstrate that our model yields a significant improvement in both deterministic and ensemble forecasting over the climatology mean, traditional numerical methods, and data-driven models. Ablation studies empirically show the effectiveness of our model designs. Remarkably, our model outperforms skillful numerical ECMWF-S2S and advanced data-driven Fuxi-S2S in key meteorological variables. The code implementation can be found in https://github.com/zhangminglang42/TianQuan.
DiMeR: Disentangled Mesh Reconstruction Model with Normal-only Geometry Training
Authors: Lutao Jiang; Jiantao Lin; Kanghao Chen; Wenhang Ge; Xin Yang; Yifan Jiang; Yuanhuiyi Lyu; Xu Zheng; LI JING; Yinchuan Li; Ying-Cong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose DiMeR, a novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction. Existing methods confront two persistent obstacles: (i) textures can conceal geometric errors, i.e., visually plausible images can be rendered even with wrong geometry, producing multiple ambiguous optimization objectives in geometry-texture mixed solution space for similar objects; and (ii) prevailing mesh extraction methods are redundant, unstable, and lack 3D supervision. To solve these challenges, we rethink the inductive bias for mesh reconstruction. First, we disentangle the unified geometry-texture solution space, where a single input admits multiple feasible solutions, into geometry and texture spaces individually. Specifically, given that normal maps are strictly consistent with geometry and accurately capture surface variations, the normal maps serve as the only input for geometry prediction in DiMeR, while the texture is estimated from RGB images. Second, we streamline the algorithm of mesh extraction by eliminating modules with low performance/cost ratios and redesigning regularization losses with 3D supervision. Notably, DiMeR still accepts raw RGB images as input by leveraging foundation models for normal prediction. Extensive experiments demonstrate that DiMeR generalises across sparse‑views-3D, single‑image-3D, and text‑to‑3D tasks, consistently outperforming baselines. On the GSO and OmniObject3D datasets, DiMeR significantly reduces Chamfer Distance by more than 30%.
WithAnyone: Toward Controllable and ID Consistent Image Generation
Authors: Hengyuan Xu; Wei Cheng; Peng Xing; Yixiao Fang; Shuhan Wu; Rui Wang; Xianfang Zeng; Daxin Jiang; Gang YU; Xingjun Ma; Yu-Gang Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Identity-consistent (ID-consistent) generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference identity. Yet, the scarcity of large-scale paired datasets—containing multiple images of the same individual—forces most approaches to adopt reconstruction-based training. This reliance often leads to a failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, or lighting. Such over-similarity undermines controllability and limits the expressive power of generation.
To address these limitations, we (1) construct a large-scale paired dataset, MultiID-2M, tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce a benchmark that quantifies both copy-paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, a diffusion-based model that effectively mitigates copy-paste while preserving high identity similarity. Extensive experiments—both qualitative and quantitative—demonstrate that WithAnyone substantially reduces copy-paste artifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive, controllable generation.
UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections
Authors: Zeyu Cai; Ziyang Li; Xiaoben Li; Boqian Li; Zeyu Wang; Zhenyu Zhang; Yuliang Xiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present UP2You, the first tuning-free solution for reconstructing high-fidelity 3D clothed portraits from extremely unconstrained in-the-wild 2D photos. Unlike previous approaches that require "clean" inputs (e.g., full-body images with minimal occlusions, or well calibrated cross-view captures), UP2You directly processes raw, unstructured photographs, which may vary significantly in pose, viewpoint, cropping, and occlusion. Instead of compressing data into tokens for slow online text-to-3D optimization, we introduce a data rectifier paradigm that efficiently converts unconstrained inputs into clean, orthogonal multi-view images in a single forward pass within seconds, simplifying the 3D reconstruction. Central to UP2You is a pose-correlated feature aggregation module PCFA, that selectively fuses information from multiple reference images w.r.t. target poses, enabling better identity preservation and nearly constant memory footprint, with more observations. Extensive experiments on 4D-Dress, PuzzleIOI, and in-the-wild captures demonstrate that UP2You consistently surpasses previous methods in both geometric accuracy (Chamfer-15\%$\\downarrow$, P2S-18\%$\\downarrow$ on PuzzleIOI) and texture fidelity (PSNR-21\%$\\uparrow$, LPIPS 46\%$\\downarrow$ on 4D-Dress). UP2You is efficient (1.5 minutes per person), and versatile (supports arbitrary pose control, and training-free multi-garment 3D virtual try-on), making it practical for real-world scenarios where humans are casually captured. Both models and code will be released to facilitate future research on this underexplored task.
DiCache: Let Diffusion Model Determine Its Own Cache
Authors: Jiazi Bu; Pengyang Ling; Yujie Zhou; Yibin Wang; Yuhang Zang; Dahua Lin; Jiaqi Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent years have witnessed the rapid development of acceleration techniques for diffusion models, especially caching-based acceleration methods. These studies seek to answer two fundamental questions: _"When to cache"_ and _"How to use cache"_, typically relying on predefined empirical laws or dataset-level priors to determine caching timings and adopting handcrafted rules for multi-step cache utilization. However, given the highly dynamic nature of the diffusion process, they often exhibit limited generalizability and fail to cope with diverse samples. In this paper, a strong sample-specific correlation is revealed between the variation patterns of the shallow-layer feature differences in the diffusion model and those of deep-layer features. Moreover, we have observed that the features from different model layers form similar trajectories. Based on these observations, we present **DiCache**, a novel training-free adaptive caching strategy for accelerating diffusion models at runtime, answering both when and how to cache within a unified framework. Specifically, DiCache is composed of two principal components: (1) _Online Probe Profiling Scheme_ leverages a shallow-layer online probe to obtain an on-the-fly indicator for the caching error in real time, enabling the model to dynamically customize the caching schedule for each sample. (2) _Dynamic Cache Trajectory Alignment_ adaptively approximates the deep-layer feature output from multi-step historical caches based on the shallow-layer feature trajectory, facilitating higher visual quality. Extensive experiments validate DiCache’s capability in achieving higher efficiency and improved fidelity over state-of-the-art approaches on various leading diffusion models including WAN 2.1, HunyuanVideo and Flux.