GenAI4ELab/papercli-papers huggingface dataset:
EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval
Authors: Jiashi Lin; Changhong Jiang; Xiangru Lin; Ruifei Zhang; Xinyi Zhu; Jiyao Liu; Cheng Tang; Ye Du; Shujian Gao; Junzhi Ning; Lihao Liu; Ziyan Huang; Tianbin Li; Jin Ye; Junjun He
Venue: CVPR 2026 (main)

Abstract: Retrieval-augmented generation (RAG) has emerged as a critical paradigm for grounding Multimodal Large Language Models (MLLMs) in external knowledge. Recent GraphRAG methods introduce structured entity-relation graphs to improve retrieval and reasoning. However, they remain limited by treating knowledge graphs as static data structures built offline and queried in a single pass. This static paradigm misaligns with the interactive, iterative nature of knowledge-intensive reasoning, creating three bottlenecks: (i) text-centric fragmentation that impedes cross-modal reasoning, (ii) frozen structures unable to incorporate new evidence or correct errors, and (iii) rigid single-pass retrieval without adaptive refinement. To overcome these limitations, we introduce EvoGraph-R1, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions.We formulate retrieval as a Markov Decision Process (MDP) where the agent observes the graph state and executes actions to query (GraphRetrieve), expand (WebSearch), refine (GraphEdit), or terminate (Answer) the reasoning. These actions reshape the hypergraph structure and generate feedback signals that guide subsequent evolution.Through this closed loop, the hypergraph evolves by integrating new evidence, correcting errors, and refining structure to support multi-hop reasoning. Experiments on multimodal VQA and text QA benchmarks demonstrate substantial improvements over existing RAG baselines in accuracy, coverage, and traceability, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.
FHAvatar: Fast and High-Fidelity Reconstruction of Face-and-Hair Composable 3D Head Avatar from Few Casual Captures
Authors: Yujie Sun; Zhuoqiang Cai; Chaoyue Niu; Jianchuan Chen; Zhiwen Chen; Chengfei Lv; Fan Wu
Venue: CVPR 2026 (main)

Abstract: We present FHAvatar, a novel framework for reconstructing 3D Gaussian avatars with composable face and hair components from an arbitrary number of views. Unlike previous approaches that couple facial and hair representations within a unified modeling process, we explicitly decouple two components in texture space by representing the face with planar Gaussians and the hair with strand-based Gaussians. To overcome the limitations of existing methods that rely on dense multi-view captures or costly per-identity optimization, we propose an aggregated transformer backbone to learn geometry-aware cross-view priors and head-hair structural coherence from multi-view datasets, enabling effective and efficient feature extraction and fusion from few casual captures. Extensive quantitative and qualitative experiments demonstrate that FHAvatar achieves state-of-the-art reconstruction quality from only a few observations of new identities within minutes, while supporting real-time animation, convenient hairstyle transfer, and stylized editing, broadening the accessibility and applicability of digital avatar creation.
Edge-RecViT: Efficient Vision Transformer via Semantic-Refined Dynamic Recursion
Authors: YiZhou Li; Jinyi Xu; Mingyu Yin; Xianyi Zhao
Venue: CVPR 2026 (main)

Abstract: Vision Transformers (ViTs) have achieved remarkable progress in visual and multimodal tasks, yet their deployment remains costly. Token-adaptive methods reduce FLOPs through dynamic depth computation, but they face two limitations: (1) Global attention overemphasizes highly similar foreground regions, causing token-adaptive modules to assign the deepest computation to semantically weak foreground tokens while prematurely exiting edge tokens rich in structural cues (as shown in Fig. 1); (2) Although token-adaption lowers FLOPs, it still relies on large parameter sets, and deep-layer weights remain underutilized due to early token exit. Parameter sharing could address redundancy but is difficult to apply in ViTs, where hierarchical abstraction typically requires diverse transformations. To address these issues, we propose Edge-RecViT, an Edge-Adaptive Dynamic Recursive Vision Transformer that integrates an edge-aware token-adaptive ranker with a recursive transformer using fully shared parameters in its hidden layers. Edge-RecViT dynamically allocates computation based on semantic richness: structurally informative edge tokens receive deeper refinement, whereas redundant low-information tokens exit early. Extensive experiments show that Edge-RecViT provides an excellent trade-off among accuracy, FLOPs, and parameter efficiency. On ImageNet-1K, it matches DeiT within 0.3% Top-1 accuracy while reducing FLOPs by 30.5% (35.1 to 24.39 GFLOPs). At the Base level, parameter drops from 86M to 23.21M with higher accuracy than ViT-Base; compared with ViT-Large, parameters are reduced by 93% while maintaining superior accuracy.
TVHighlights: LLM-Guided Human-Free Collaborative Training for Video Highlight Detection in Movies and TV Dramas
Authors: Qi Qiu; Xuan Wu; Jiawei Peng; Yuan Miao; Xu Yang; Yanlong Du
Venue: CVPR 2026 (main)

Abstract: Video highlight detection aims to identify the most engaging segments in long-form videos, supporting content editing and recommendation, especially for movies and TV dramas. However, existing methods are ill-suited to cinematic content due to its narrative complexity, while the scarcity of annotated data and the high cost of manual labeling further hinder progress. To bridge this gap, we introduce **TVHighlights**, the first large-scale dataset tailored for video highlight detection in movies and TV dramas, with 1,721 carefully curated videos covering diverse genres. Built on community-driven behaviors, it provides realistic and diverse annotations without human labeling. Based on TVHighlights, we propose **LTV-HD**: a LLM-guided, human-free collaborative training framework for video highlight detection in cinematic content. LTV-HD operates in two stages: (1) weakly supervised pre-training of a lightweight model using video-level labels, followed by (2) iterative refinement through collaboration between large language models (LLMs) and the lightweight model. LLMs generate noisy clip-level pseudo-labels, which the lightweight model learns from under a noise-robust strategy, and its high-confidence predictions are then fed back to guide the LLM in distilling genre-specific highlight patterns through a self-improving loop. Experiments demonstrate that LTV-HD achieves state-of-the-art performance on TVHighlights, validating its effectiveness in real-world, annotation-free scenarios.
RAAS: LLM Agentic System Architecture Search with GRPO
Authors: Jiayi Yang; Guancheng Wan; Man Zhang; Mang Ye
Venue: CVPR 2026 (main)

Abstract: Large Language Model (LLM) agentic systems solve complex tasks through coordinated workflows, but designing them remains labor-intensive. The Agentic Supernet paradigm automates this by optimizing a probabilistic architecture space, yet suffers from critical evaluation instabilities: absolute performance scores entangle architectural merit with query difficulty, while single-execution protocols capture execution randomness rather than true capability. These instabilities lead to unreliable search dynamics where simple queries inflate weak designs and challenging queries suppress strong ones.We introduce RAAS (Robust Architecture Adaptive Search), which establishes more stable and fair evaluation through two synergistic mechanisms. Contextual Architecture Orchestration (CAO) disentangles quality from task difficulty by evaluating cohorts of candidate architectures on identical queries, deriving context-aware merit signals through peer-group comparison. Multi-Trial Assessment Synthesis (MTAS) reduces execution variance by aggregating performance across multiple independent trials, producing statistically robust capability estimates.Together, these mechanisms provide more reliable signals for architecture discovery. Experiments on six benchmarks spanning mathematical reasoning, code generation, and one multi-step tool-use benchmark show that RAAS consistently improves over strong baselines, improving HumanEval pass@1 from 92.23% to 96.31% and MATH accuracy from 52.08% to 60.87%, while maintaining practical efficiency. These results suggest that robust evaluation is a useful ingredient for agentic architecture search in the studied settings.
VGA-Bench: A Unified Benchmark and Multi-Model Framework for Video Aesthetics and Generation Quality Evaluation
Authors: Longteng Jiang; DanDan Zheng; Qianqian Qiao; Heng Huang; Huaye Wang; Yihang Bo; Bao Peng; Jingdong Chen; Jun Zhou; Xin Jin
Venue: CVPR 2026 (main)

Abstract: The rapid advancement of AIGC-based video generation has underscored the critical need for comprehensive evaluation frameworks that go beyond traditional generation quality metrics to encompass aesthetic appeal. However, existing benchmarks remain largely focused on technical fidelity, leaving a significant gap in holistic assessment--particularly with respect to perceptual and artistic qualities. To address this limitation, we introduce VGA-Bench, a unified benchmark for joint evaluation of video generation quality and aesthetic quality. VGA-Bench is built upon a principled three-tier taxonomy: Aesthetic Quality, Aesthetic Tagging, and Generation Quality, each decomposed into multiple fine-grained sub-dimensions to enable systematic assessment. Guided by this taxonomy, we design 1,016 diverse prompts and generate a large-scale dataset of over 60,000 videos using 12 video generation models, ensuring broad coverage across content, style, and artifacts. To enable scalable and automated evaluation, we annotate a subset of the dataset via human labeling and develop three dedicated multi-task neural assessors: VAQA-Net for aesthetic quality prediction, VTag-Net for automatic aesthetic tagging, and VGQA-Net for generation and basic quality attributes. Extensive experiments demonstrate that our models achieve reliable alignment with human judgments, offering both accuracy and efficiency. We release VGA-Bench as a public benchmark to foster research in AIGC evaluation, with applications in content moderation, model debugging, and generative model optimization.
BiPA: Bilevel Prompt Adaptation for Underwater Instance Segmentation
Authors: Long Ma; Haoze Zheng; Yuhang Mao; Jinyuan Liu; Chengpei Xu; Xinwei Xue; Yi Wang; Xiangjian He; Weimin Wang
Venue: CVPR 2026 (main)

Abstract: Underwater instance segmentation is essential for fine-grained scene understanding. However, underwater imagery exhibits a strong domain gap from in-air vision due to severe degradation (e.g., turbidity).  Consequently, despite its general segmentation ability, SAM degrades sharply underwater. In this work, we propose BiPA, which effectively adapts SAM to the underwater domain. To be concrete, we construct an underwater SAM with dual prompts and introduce a foreground-attentive injection block to enhance local foreground representation. We formulate dense prompt learning as a bilevel optimization, explicitly capturing the mutual dependency between prompt and model. To make this tractable, we design a two-stage learning strategy. The first stage adapts the dense prompt itself, updating it with Bayesian optimization to learn efficiently. The second stage fine-tunes the model parameters under the frozen optimized prompt, which finally enables effective cross-domain adaptation. Extensive experiments and analyses verify the superiority and efficiency of BiPA. The code is publicly available at https://github.com/ZeAstra/BiPA.
Beyond Success: Refining Elegant Robot Manipulation from Mixed-Quality Data via Just-in-Time Intervention
Authors: Yanbo Mao; Jianlong Fu; Ruoxuan Zhang; Hongxia Xie; Meibao Yao
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) models have enabled notable progress in general-purpose robotic manipulation, yet their learned policies often exhibit variable execution quality. We attribute this variability to the mixed-quality nature of human demonstrations, where the implicit principles that govern how actions should be carried out are only partially satisfied. To address this challenge, we introduce the LIBERO-Elegant benchmark with explicit criteria for evaluating execution quality. Using these criteria, we develop a decoupled refinement framework that improves execution quality without modifying or retraining the base VLA policy. We formalize Elegant Execution as the satisfaction of Implicit Task Constraints (ITCs) and train an Elegance Critic via offline Calibrated Q-Learning to estimate the expected quality of candidate actions. At inference time, a Just-in-Time Intervention (JITI) mechanism monitors critic confidence and intervenes only at decision-critical moments, providing selective, on-demand refinement. Experiments on LIBERO-Elegant and real-world manipulation tasks show that the learned Elegance Critic substantially improves execution quality, even on unseen tasks. The proposed model enables robotic control that values not only whether tasks succeed, but also how they are performed.
GROW: Watermark Generation with Progressive Guidance for Diffusion Models
Authors: Pengcheng Luo; Zexi Jia; Yijia Zhong; Jinchao Zhang; Jie Zhou
Venue: CVPR 2026 (main)

Abstract: Digital watermarking is a cornerstone for copyright protection. With the rapid advancement of generative models like diffusion models, in-generation and training-free watermarking techniques have garnered more attention for their endogeneity and convenience. These methods typically embed a watermark into the initial noise, where watermark extraction relies on Denoising Diffusion Implicit Models (DDIM) inversion. However, the computationally intensive extraction process severely hinders their path toward practical deployment. To overcome this critical bottleneck, we propose GROW, a novel training-free paradigm that reframes watermarking from a one-shot "embedding"  to a progressive "growth". By progressively guiding using frequency-domain gradients, GROW naturally weaves the watermark into the image, which enables inversion-free extraction. Comprehensive experiments on multiple datasets show that GROW not only achieves superior robustness and imperceptibility but also offers a detection speed nearly 100x faster than inversion-based techniques. The code will be made publicly available.
Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning
Authors: Xuchen Li; Xuzhao Li; Shiyu Hu; Kaiqi Huang
Venue: CVPR 2026 (main)

Abstract: Long-form video reasoning remains a major challenge for Video Large Language Models (Video LLMs), as static uniform frame sampling leads to information dilution and obscures critical evidence. Furthermore, existing pixel-space video reasoning agents, which are designed to actively interact with the video to acquire new visual information, remain suboptimal due to their lack of rigorous reward mechanisms to enforce evidence purity and their inability to perform temporal information supplementation beyond pre-sampled frames. To address this critical gap, we propose a novel evidence-prioritized adaptive framework built upon our core philosophy: "Select Less, Reason More." Our core contribution is the evidence-aware reinforcement learning (EARL) framework, which transforms the model into an active interrogator of evidence. EARL is precisely engineered to dynamically select the most relevant frames and, crucially, to perform localized re-sampling around the selected key frames to access fine-grained temporal detail. Extensive experiments on five demanding video reasoning benchmarks demonstrate that our EARL-trained model achieves new state-of-the-art among open-source Video LLMs, simultaneously learning an effective and high-purity visual evidence selection policy. Impressively, our 7B model achieves 59.8% on LongVideoBench, 69.0% on MVBench and 64.9% on VideoMME. These results highlight the importance of prioritizing evidence purity and the effectiveness of our framework.
MultiShotMaster: A Controllable Multi-Shot Video Generation Framework
Authors: Qinghe Wang; Xiaoyu Shi; Baolu Li; Weikang Bian; Quande Liu; Huchuan Lu; Xintao Wang; Pengfei Wan; Kun Gai; Xu Jia
Venue: CVPR 2026 (main)

Abstract: Current video generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos, which require flexible shot arrangement, coherent narrative, and controllability beyond text prompts. To tackle these challenges, we propose MultiShotMaster, a framework for highly controllable multi-shot video generation. We extend a pretrained single-shot model by integrating two novel variants of RoPE. First, we introduce Multi-Shot Narrative RoPE, which applies explicit phase shift at shot transitions, enabling flexible shot arrangement while preserving the temporal narrative order. Second, we design Spatiotemporal Position-Aware RoPE to incorporate reference tokens and grounding signals, enabling spatiotemporal-grounded reference injection. In addition, to overcome data scarcity, we establish an automated data annotation pipeline to extract multi-shot videos, captions, cross-shot grounding signals and reference images. Our framework leverages the intrinsic architectural properties to support multi-shot video generation, featuring text-driven inter-shot consistency, customized subject with motion control, and background-driven customized scene. Both shot count and duration are flexibly configurable. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework.
Cubic Discrete Diffusion: Discrete Visual Generation on High-Dimensional Representation Tokens
Authors: Yuqing Wang; Chuofan Ma; Zhijie Lin; Yao Teng; Lijun Yu; Shuai Wang; Jiaming Han; Jiashi Feng; Yi Jiang; Xihui Liu
Venue: CVPR 2026 (main)

Abstract: Visual generation with discrete tokens has gained significant attention as it enables a unified token prediction paradigm shared with language models, promising seamless multimodal architectures. However, current discrete generation methods remain limited to low-dimensional latent tokens (typically 8-32 dims), sacrificing the semantic richness essential for understanding. While high-dimensional pretrained representations (768-1024 dims) could bridge this gap, their discrete generation poses fundamental challenges. In this paper, we present Cubic Discrete Diffusion (CubiD), the first discrete generation model for high-dimensional representations. CubiD performs fine-grained masking throughout the high-dimensional discrete representation--any dimension at any position can be masked and predicted from partial observations. This enables the model to learn rich correlations both within and across spatial positions, with the number of generation steps fixed at T regardless of feature dimensionality, where T << hwd. On ImageNet-256, CubiD achieves state-of-the-art discrete generation with strong scaling behavior from 900M to 3.7B parameters. Crucially, we validate that these discretized tokens preserve original representation capabilities, demonstrating that the same discrete tokens can effectively serve both understanding and generation tasks. We hope this work will inspire future research toward unified multimodal architectures. Code is available at: https://github.com/YuqingWang1029/CubiD.
NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code
Authors: Seemandhar Jain; Keshav Gupta; Kunal Gupta; Manmohan Chandraker
Venue: CVPR 2026 (main)

Abstract: The proliferation of neural radiance field (NeRF) research requires significant efforts to reimplement papers before building upon them. We introduce NERFIFY, a multi-agent framework that reliably converts NeRF research papers into trainable Nerfstudio plugins, in contrast to generic paper-to-code methods and frontier models like GPT-5 that usually fail to produce runnable code. NERFIFY achieves domain-specific executability through six key innovations: (1) Context-free grammar (CFG): LLM synthesis is constrained by Nerfstudio formalized as a CFG, ensuring generated code satisfies architectural invariants. (2) Graph-of-Thought code synthesis: Specialized multi-file-agents generate repositories in topological dependency order, validating contracts and errors at each node. (3) Compositional citation recovery: Agents automatically retrieve and integrate components (samplers, encoders, proposal networks) from citation graphs of references. (4) Visual feedback: Artifacts are diagnosed through PSNR-minima ROI analysis, cross-view geometric validation, and VLM-guided patching to iteratively improve quality. (5) Knowledge enhancement: Beyond reproduction, methods can be improved with novel regularizers or architectural optimizations. (6) Benchmarking: An evaluation framework is designed for NeRF paper-to-code synthesis across 30 diverse papers. On papers without public implementations, NERFIFY achieves visual quality matching expert human code (+-0.5 dB PSNR, +-0.2 SSIM) while reducing implementation time from weeks to minutes. NERFIFY demonstrates that a domain-aware design enables code translation for complex vision papers, potentiating accelerated and democratized reproducible research. Code, data and implementations are publicly available at https://seemandhar.github.io/NERFIFY/.
Gated Condition Injection without Multimodal Attention: Towards Controllable Linear-Attention Transformers
Authors: Yuhe Liu; Zhenxiong Tan; Yujia Hu; Songhua Liu; Xinchao Wang
Venue: CVPR 2026 (main)

Abstract: Recent advances in diffusion-based controllable visual generation have led to remarkable improvements in image quality. However, these powerful models are typically deployed on cloud servers due to their large computational demands, raising serious concerns about user data privacy. To enable secure and efficient on-device generation, we explore in this paper controllable diffusion models built upon linear attention architectures, which offer superior scalability and efficiency, even on edge devices. Yet, our experiments reveal that existing controllable generation frameworks, such as ControlNet and OminiControl, either lack the flexibility to support multiple heterogeneous condition types or suffer from slow convergence on such linear-attention models. To address these limitations, we propose a novel controllable diffusion framework tailored for linear attention backbones like SANA. The core of our method lies in a unified gated conditioning module working in a dual-path pipeline, which effectively integrates multi-type conditional inputs, such as spatially aligned and non-aligned cues. Extensive experiments on multiple tasks and benchmarks demonstrate that our approach achieves state-of-the-art controllable generation performance based on linear-attention models, surpassing existing methods in terms of fidelity and controllability.