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新论文 · v1arXiv:2601.18724Google Research

HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences

Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe

原文摘要

Recently, we have often observed hallucinated citations or references that do not correspond to any existing work in papers under review, preprints, or published papers. Such hallucinated citations pose a serious concern to scientific reliability. When they appear in accepted papers, they may also negatively affect the credibility of conferences. In this study, we refer to hallucinated citations as "HalluCitation" and systematically investigate their prevalence and impact. We analyze all papers published at ACL, NAACL, and EMNLP in 2024 and 2025, including main conference, Findings, and workshop papers. Our analysis reveals that nearly 300 papers contain at least one HalluCitation, most of which were published in 2025. Notably, half of these papers were identified at EMNLP 2025, the most recent conference, indicating that this issue is rapidly increasing. Moreover, more than 100 such papers were accepted as main conference and Findings papers at EMNLP 2025, affecting the credibility.

cs.CL · cs.AI · cs.DL

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首次观测 · 记录 #1软件学报:jos:7562软件学报

业务建模驱动TRIZ注入的人-多智能体协作创新需求捕获框架

刘新华, 金敏, 余梦姣, 谢文涛

原文摘要

当前软件市场呈现出产品同质化加重趋势, 功能性创新已成为决定软件竞争优势的关键因素. 这促使现代需求工程研究范式从被动的需求提取转向主动的创新需求捕获. 在提升需求创新性的实践中, 现有研究主要呈现两条路径: (1)通过情景建模与引导方法改进工作坊流程, 激发人类团队协作创新; (2)基于组合创新理论对既有需求进行解构重组, 快速生成新需求方案. 但两种方法均面临创新质量与参与成本难以有效平衡的核心矛盾. 生成式AI技术的突破性进展为应对这一挑战提供了新思路. 提出一种业务建模驱动下注入TRIZ理论的人-多智能体协作式创新需求捕获框架BMHACT, 该框架以统一过程业务建模协作架构为蓝本, 设计提示词定义业务流程分析员、业务设计员等5个智能体职责. 多智能体团队通过“系统愿景收集-流程痛点识别-技术矛盾分析-TRIZ创新原理匹配-需求方案生成”的协作流程生成创新需求, 并由领域专家和客户代表对需求进行创新性评估. 以工程机械领域某企业门户网站建设项目为例的实证研究表明: 相较基于需求重用的自动化方法和基于对抗样本的追溯式需求生成方法, BMHACT迭代次数分别降低50%和28.6%, 全过程耗时减少66.7%和33.3%, 同时, 创新潜力指数CNU (clarity, novelty, usefulness)分别提升22.9%和10.7%, 且CNU单轮平均增益分别提高2.16倍和2.14倍. 证明了BMHACT在提升需求创新质量和降低协作成本上的优越性.

cs.* · cs.AI · cs.MA

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:002942adad252d4093d55016458b2f85CVF · CVPR 2026

VQ-VA World: Towards High-Quality Visual Question-Visual Answering

Gou, Chenhui, Chen, Zilong, Wang, Zeyu, Li, Feng, Zhu, Deyao, Duan, Zicheng, Li, Kunchang, Deng, Chaorui 等 13 位作者

原文摘要

This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question---an ability that has recently emerged in proprietary systems such as NanoBanana and GPT-Image. To also bring this capability to open-source models, we introduce VQ-VA World, a data-centric framework built around an agentic pipeline for large-scale, targeted data construction. Leveraging web-scale deployment, this pipeline crawls a massive amount of ~1.8M high-quality, interleaved image-text samples for model training. For evaluation, we further release IntelligentBench, a human-curated benchmark that systematically assesses VQ-VA along the aspects of world knowledge, design knowledge, and reasoning. Training with VQ-VA World data yields strong empirical gains: it helps LightFusion attain 53.06 on IntelligentBench, substantially surpassing the best prior open-source baselines (i.e., 7.78 from vanilla LightFusion; 1.94 from UniWorld-V1), and significantly narrowing the gap toward leading proprietary systems (e.g., 81.67 from NanoBanana; 82.64 from GPT-Image). By releasing the full suite of model weights, datasets, and pipelines, our work will greatly stimulate future research on VQ-VA. Our project is publicly available at https://github.com/chenhuigou/VQ-VA-World.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:012500583f16cab60fd7c499c3373a7cCVF · CVPR 2026

LensWalk: Agentic Video Understanding by Planning How You See in Videos

Li, Keliang, Li, Yansong, Shen, Hongze, Liu, Mengdi, Chang, Hong, Shan, Shiguang

原文摘要

The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understanding are limited by the inherent disconnect between reasoning and perception: they rely on static, pre-processed information and cannot actively seek raw evidence from video as their understanding evolves. To address this, we introduce LensWalk, a flexible agentic framework that empowers a Large Language Model reasoner to control its own visual observation actively. LensWalk establishes a tight reason-plan-observe loop where the agent dynamically specifies, at each step, the temporal scope and sampling density of the video it observes. Using a suite of versatile, Vision-Language Model based tools parameterized by these specifications, the agent can perform broad scans for cues, focus on specific segments for fact extraction, and stitch evidence from multiple moments for holistic verification. This design allows for progressive, on-demand evidence gathering that directly serves the agent's evolving chain of thought. Without requiring any model fine-tuning, LensWalk delivers substantial, plug-and-play performance gains on multiple model recipes, boosting their accuracy by over 5% on challenging long-video benchmarks like LVBench and Video-MME. Our analysis reveals that enabling an agent to control how it sees is key to unlocking more accurate, robust, and interpretable video reasoning.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:03c02aad9994127a01b68ee2a2597e4fCVF · CVPR 2026

Geometrically-Constrained Agent for Spatial Reasoning

Chen, Zeren, Lu, Xiaoya, Zheng, Zhijie, Li, Pengrui, He, Lehan, Zhou, Yijin, Shao, Jing, Zhuang, Bohan 等 9 位作者

原文摘要

Vision Language Models (VLMs) exhibit a fundamental semantic-to-geometric gap in spatial reasoning: they excel at qualitative semantic inference but their reasoning operates within a lossy semantic space, misaligned with high-fidelity geometry. Current paradigms fail to bridge this gap. Training-based methods suffer from an "oracle paradox," learning flawed spatial logic from imperfect oracles. Tool-integrated methods constrain the final computation but critically leave the VLM's planning process unconstrained, resulting in geometrically flawed plans. In this work, we propose Geometrically-Constrained Agent (GCA), a training-free agentic paradigm that resolves this gap by introducing a formal task constraint. Specifically, we strategically decouples the VLM's role into two stages. First, acting as a semantic analyst, the VLM translates the user's ambiguous query into the formal, verifiable task constraint, which defines the reference frame and objective. Second, acting as a task solver, the VLM generates and executes tool calls strictly within the deterministic bounds defined by the constraint. This geometrically-constrained strategy successfully resolve the semantic-to-geometric gap, yielding a robust and verifiable reasoning pathway for complex spatial tasks. Comprehensive experiments demonstrate that GCA achieves SOTA performance on multiple spatial reasoning benchmarks, surpassing existing training-based and tool-integrated methods by 27%.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0740196e0d9168af2b482d06fefe10cbCVF · CVPR 2026

Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model

Hao, Zecheng, Huang, Yifan, Xu, Zijie, Liu, Wenxuan, Tang, Yuanhong, Yu, Zhaofei, Huang, Tiejun

原文摘要

Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence due to their brain-inspired and energy-efficient properties. Compared to vanilla Spatial-Temporal Back-propagation (STBP) training methods, online training can effectively avoid the risk of GPU memory explosion. However, current online learning frameworks cannot tackle the gradient discrepancy problem between the forward and backward process, merely aiming to optimize the GPU memory, resulting in no performance advantages compared to the STBP-based models in the inference stage. To address the aforementioned challenges, we propose Hybrid-Driven Leaky Integrate-and-Fire (HD-LIF) model family for efficient online learning, which respectively adopt different spiking calculation mechanism in the upper-region and lower-region of the firing threshold. We theoretically point out that our learning framework can effectively separate temporal gradients and address the misalignment problem of surrogate gradients, as well as achieving full-stage optimization towards learning precision, memory footprint and power consumption. Experimental results have demonstrated that our scheme is enable to achieve state-of-the-art performance for multiple evaluation metrics, breaking through the traditional paradigm of SNN online training and deployment. Code is available at https://github.com/hzc1208/HD_LIF.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:08fd35198084b80ce1655f90db38b207CVF · CVPR 2026

Resolving Evidence Sparsity: Agentic Context Engineering for Long-Document Understanding

Liu, Keliang, Chen, Zizhi, Li, Mingcheng, Tang, Jingqun, Yang, Dingkang, Zhang, Lihua

原文摘要

Document understanding is a long-standing practical task. Vision-Language Models (VLMs) have gradually become a primary approach in this domain, demonstrating effective performance on single-page tasks. However, their effectiveness diminishes when handling long documents. In such scenarios, clues are often scattered across multiple pages and modalities, and redundancy from lengthy inputs can impair the model's judgment. While retrieval-augmented generation mitigates this issue by filtering for question-relevant content, the retrieved results still contain substantial redundancy. To address these limitations, we propose SLEUTH, a multi-agent framework. Concretely, SLEUTH orchestrates a retriever and four collaborative agents in a coarse-to-fine process. The framework identifies key textual and visual clues within the retrieved pages, filters for salient visual evidence such as tables and charts, and analyzes the query to devise a reasoning strategy. It ultimately synthesizes a distilled, evidence-dense multimodal context to generate the final prediction. SLEUTH is model-agnostic and scalable. When paired with advanced VLM backbones, it consistently improves performance on multiple long-document benchmarks, achieving SOTA results. Ablation studies verify each module's effectiveness and confirm the benefits of our hierarchical refinement paradigm.

cs.CV · cs.CL · cs.AI · cs.MA

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:092958a76f3e2856fbe9d9407408d1d7CVF · CVPR 2026

Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs

Lu, Meng, Xu, Ran, Fang, Yi, Zhang, Wenxuan, Yu, Yue, Srivastava, Gaurav, Zhuang, Yuchen, Elhoseiny, Mohamed 等 16 位作者

原文摘要

While recent vision-language models (VLMs) demonstrate strong image understanding, their ability to "think with images," i.e., to reason through multi-step visual interactions, remains limited. We introduce VISTA-Gym, a scalable training environment for incentivizing tool-integrated visual reasoning capabilities in VLMs. VISTA-Gym unifies diverse real-world multimodal reasoning tasks (7 tasks from 13 datasets in total) with a standardized interface for visual tools (e.g., grounding, parsing), executable interaction loops, verifiable feedback signals, and efficient trajectory logging, enabling visual agentic reinforcement learning at scale. While recent VLMs exhibit strong text-only reasoning, both proprietary and open-source models still struggle with tool selection, invocation, and coordination. With VISTA-Gym, we train VISTA-R1 to interleave tool-use with agentic reasoning via multi-turn trajectory sampling and end-to-end reinforcement learning. Extensive experiments across 11 public reasoning-intensive VQA benchmarks show that VISTA-R1-8B outperforms state-of-the-art baselines with similar sizes by 9.51%-18.72%, demonstrating VISTA-Gym as an effective training ground to unlock the tool-integrated reasoning capabilities for VLMs.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0ddef86066942ee937ec9b5568b0575eCVF · CVPR 2026

Omni2Sound: Towards Unified Video-Text-to-Audio Generation

Dai, Yusheng, Chen, Zehua, Jiang, Yuxuan, Ke, Qiuhong, Cai, Jianfei, Zhu, Jun

原文摘要

Training a unified model integrating video-to-audio (V2A), text-to-audio (T2A), and joint video-text-to-audio (VT2A) generation offers significant application flexibility, yet faces two unexplored foundational challenges: (1) the scarcity of high-quality audio captions with tight V-A-T alignment, leading to severe semantic conflict between multimodal conditions, and (2) cross-task and intra-task competition, manifesting as an adverse V2A-T2A performance trade-off and modality bias in the VT2A task. First, to address data scarcity, we introduce SoundAtlas, a large-scale dataset (470k pairs) that significantly outperforms existing benchmarks and even human experts in quality. Powered by a novel agentic pipeline, it integrates Vision-to-Language Compression to mitigate visual bias of MLLMs, a Junior-Senior Agent Hand-off for a five times cost reduction, and rigorous Post-hoc Filtering to ensure fidelity. Consequently, SoundAtlas delivers semantically rich and temporally detailed captions with tight V-A-T alignment. Second, we propose Omni2Sound, a unified VT2A diffusion model supporting flexible input modalities. To resolve the inherent cross-task and intra-task competition, we design a three-stage multi-task progressive training schedule that converts cross-task competition into joint optimization and mitigates modality bias in the VT2A task, maintaining both audio-visual alignment and off-screen audio generation faithfulness. Finally, we construct VGGSound-Omni, a comprehensive benchmark for unified evaluation, including challenging off-screen tracks. With a standard DiT backbone, Omni2Sound achieves unified SOTA performance across all three tasks within a single model, demonstrating strong generalization across benchmarks with heterogeneous input conditions.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:10fa8d6b13d6a075e7cfdaded57d3bdcCVF · CVPR 2026

Don't Show Pixels, Show Cues: Unlocking Visual Tool Reasoning in Language Models via Perception Programs

Janjua, Muhammad Kamran, Silva, Hugo, Niu, Di, Rashidi, Bahador

原文摘要

Multimodal language models (MLLMs) are increasingly paired with vision tools (e.g., depth, flow, correspondence) to enhance visual reasoning. However, despite access to these tool-generated visual cues, MLLMs often fail to benefit fully from them. Existing approaches typically feed raw tool outputs into the model, but these dense, pixel-level representations are misaligned with the language-native reasoning strengths of LLMs, leading to weak perception due to reliance on language priors. We argue that, in problems where vision tools can provide the necessary visual cues, the bottleneck is not more tool calls or larger MLLMs, it is how tool outputs are represented. We introduce Perception Programs (P^2), a training-free, model-agnostic method that rewrites tool outputs into compact, structured, language-native summaries that MLLMs can directly parse and reason over. Across six perception-centric tasks in BLINK, P^2 consistently yields large improvements over base models and raw tool-augmented baselines. With GPT-5 Mini as the base model, P^2 raises its accuracy from 41.35% to 86.47% on multi-view reasoning, from 52.42 to 81.45 on relative depth, and achieves a 19.66 overall average gain across tasks, setting new state-of-the-art results. Even on smaller MLLMs, e.g., InternVL3.5-4B and Qwen3VL-4B, we observe 21-25 absolute gains from P^2 across tasks when comparing to image-based tool variants, surpassing prior agentic, supervised, and RL-based tool-use methods, without any training or model modifications.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:18b7a0b7c967bf98bd04113384c2a170CVF · CVPR 2026

RAAS: LLM Agentic System Architecture Search with GRPO

Yang, Jiayi, Wan, Guancheng, Zhang, Man, Ye, Mang

原文摘要

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.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:18ff0b6b8393cffb36bf874609e8aa1fCVF · CVPR 2026

Beyond Sequential Tools: A Unified VLM Agent System for Photographic Post-Processing via Dynamic Multi-Expert Fusion

Xiong, Honglin, Zhu, Chenjie, Ding, Jianbiao, Ni, Zixuan, Li, Wei, Mi, Zhenpeng, Wang, Qian

原文摘要

Real-world image restoration is challenged by complex, coupled degradations. Existing "all-in-one" models often lack generalization, while agentic systems suffer from inefficient sequential tool invocation. We propose a VLM-guided one-shot framework for universal photographic post-processing. Our system employs a Vision-Language Model (VLM) as an orchestrator to perform nuanced intent understanding and degradation analysis, dynamically allocating weights to a suite of specialized expert LoRA modules. To ensure superior composability, these experts adapt only Key (K) and Value (V) matrices and are simultaneously merged into a pretrained diffusion backbone for synergistic, single-pass restoration. Furthermore, we introduce a lightweight branch trained via Direct Preference Optimization (DPO) to ensure perceptually optimal weight allocation. Our method achieves state-of-the-art performance across diverse synthetic and real-world datasets. Crucially, it demonstrates remarkable zero-shot generalization on authentic real-world data without additional fine-tuning.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:24622908ff4e3db585246554b150263bCVF · CVPR 2026

See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis

Park, Jaehyun, Ahn, Minyoung, Kim, Minkyu, Lee, Jonghyun, Lee, Jae-Gil, Park, Dongmin

原文摘要

Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models might reduce artifacts, there is no assurance that they can be completely eliminated, which makes artifact mitigation a highly crucial area of study. Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets. In this paper, we propose **ArtiAgent**, which efficiently creates pairs of real and artifact-injected images. It comprises three agents: a perception agent that recognizes and grounds entities and subentities from real images, a synthesis agent that introduces artifacts via artifact injection tools through novel patch-wise embedding manipulation within a diffusion transformer, and a curation agent that filters the synthesized artifacts and generates both local and global explanations for each instance. Using our pipeline, we collect 100K pairwise instances and demonstrate both efficacy and versatility across diverse applications. Code is available at https://github.com/krafton-ai/ArtiAgent.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:252e6fe036f806eb91f283bc8dd669b9CVF · CVPR 2026

ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body

Zhang, Juze, Chen, Changan, Chen, Xin, Yu, Heng, Xiang, Tiange, Khan, Ali Sartaz, Lakshmikanth, Shrinidhi K., Adeli, Ehsan

原文摘要

Human communication is inherently multimodal and social: words, prosody, and body language jointly carry intent. Yet most prior systems model human behavior as a translation task--co-speech gesture or text-to-motion that maps a fixed utterance to motion clips--without requiring agentic decision-making about when to move, what to do, or how to adapt across multi-turn dialogue. This leads to brittle timing, weak social grounding, and fragmented stacks where speech, text, and motion are trained or inferred in isolation. We introduce ViBES (Voice in Behavioral Expression and Synchrony), a conversational 3D agent that jointly plans language and movement and executes dialogue-conditioned body actions. Concretely, ViBES is a speech-language-behavior (SLB) model with a mixture-of-modality-experts (MoME) backbone: modality-partitioned transformer experts for speech, facial expression, and body motion. The model processes interleaved multimodal token streams with hard routing by modality (parameters are split per expert), while sharing information through cross-expert attention. By leveraging strong pretrained speech-language models, the agent supports mixed-initiative interaction: users can speak, type, or issue body-action directives mid-conversation, and the system exposes controllable behavior hooks for streaming responses. We further benchmark on multi-turn conversation with automatic metrics of dialogue-motion alignment and behavior quality, and observe consistent gains over strong co-speech and text-to-motion baselines. ViBES goes beyond "speech-conditioned motion generation" toward agentic virtual bodies where language, prosody, and movement are jointly generated, enabling controllable, socially competent 3D interaction.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:294a3c8f84925252ffad318b691b9222CVF · CVPR 2026

Agentic Retoucher for Text-To-Image Generation

Shen, Shaocheng, Liang, Jianfeng, Cai, Chunlei, Geng, Cong, Duan, Huiyu, Zhang, Xiaoyun, Hu, Qiang, Zhai, Guangtao

原文摘要

Text-to-image (T2I) diffusion models such as SDXL and FLUX have achieved impressive photorealism, yet small-scale distortions remain pervasive in limbs, face, text and so on. Existing refinement approaches either perform costly iterative re-generation or rely on vision-language models (VLMs) with weak spatial grounding, leading to semantic drift and unreliable local edits. To close this gap, we propose **Agentic Retoucher**, a hierarchical decision-driven framework that reformulates post-generation correction as a human-like *perception-reasoning-action* loop.Specifically, we design (1) a **perception agent** that learns contextual saliency for fine-grained distortion localization under text-image consistency cues, (2) a **reasoning agent** that performs human-aligned inferential diagnosis via progressive preference alignment, and (3) an **action agent** that adaptively plans localized inpainting guided by user preference. This design integrates perceptual evidence, linguistic reasoning, and controllable correction into a unified, self-corrective decision process. To enable fine-grained supervision and quantitative evaluation, we further construct **GenBlemish-27K**, a dataset of 6K T2I images with 27K annotated artifact regions across 12 categories.Extensive experiments demonstrate that Agentic Retoucher consistently outperforms state-of-the-art methods in perceptual quality, distortion localization and human preference alignment, establishing a new paradigm for self-corrective and perceptually reliable T2I generation.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2cbd986d255d4c0c38ea9496c3955665CVF · CVPR 2026

CodeV: Code with Images for Faithful Visual Reasoning via Tool-Aware Policy Optimization

Hou, Xinhai, Xu, Shaoyuan, Biyani, Manan, Li, Moyan, Liu, Jia, Hollon, Todd C, Wang, Bryan

原文摘要

Agentic vision-language models are increasingly trained to "think with images" by calling image operations. However, we show that high final-answer accuracy often hides unfaithful visual reasoning: models may invoke tools on irrelevant regions or ignore tool outputs entirely, yet still guess the correct answer. In this work, we first propose a faithfulness evaluation protocol that measures whether intermediate visual tool outputs (e.g., crops) actually contain the queried evidence. This reveals that recent visual agents achieve high final-answer accuracy but exhibit low rates of faithful tool-use on visual search benchmarks. We then introduce CodeV, a code-based visual agent trained with Tool-Aware Policy Optimization (TAPO). TAPO is a process-level RL framework that augments GRPO with dense rewards defined directly on visual tool inputs and outputs, rather than on chain-of-thought tokens, making supervision easier to verify and less susceptible to reward hacking. CodeV represents visual tools as executable Python code, and TAPO assigns step-wise rewards based solely on the question and tool output, encouraging both necessary and evidence-consistent tool use. In a two-stage SFT+RL pipeline, CodeV achieves competitive or superior accuracy while substantially increasing faithful tool-use rates on related visual search benchmarks. Beyond visual search, CodeV attains strong performance on a range of multimodal reasoning and math benchmarks, suggesting that explicitly supervising intermediate tool behavior is crucial for building trustworthy, agentic visual reasoning systems.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Vinedresser3D: Towards Agentic Text-guided 3D Editing

Chi, Yankuan, Li, Xiang, Huang, Zixuan, Rehg, James Matthew

原文摘要

Text-guided 3D editing aims to modify existing 3D assets using natural-language instructions. Current methods struggle to jointly understand complex prompts, automatically localize edits in 3D, and preserve unedited content. We introduce Vinedresser3D, an agentic framework for high-quality text-guided 3D editing that operates directly in the latent space of a native 3D generative model. Given a 3D asset and an editing prompt, Vinedresser3D uses a multimodal large language model to infer rich descriptions of the original asset, identify the parts to edit and the edit type (addition, modification, deletion), and generate decomposed structural and appearance-level text guidance. The agent then selects an informative view and applies an image editing model to obtain visual guidance. Finally, an inversion-based rectified-flow inpainting pipeline with an interleaved sampling module performs editing in the 3D latent space, enforcing prompt alignment while maintaining 3D coherence and unedited regions. Experiments on diverse 3D edits demonstrate that Vinedresser3D outperforms prior baselines in both automatic metrics and human preference studies, while enabling precise, coherent, and mask-free 3D editing.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splatting

Song, Yixiao, Li, Qingyong, Wang, Wen, Yan, Zhicheng

原文摘要

Unsupervised point cloud segmentation is critical for embodied artificial intelligence and autonomous driving, as it mitigates the prohibitive cost of dense point-level annotations required by fully supervised methods. While integrating 2D pre-trained models such as the Segment Anything Model (SAM) to supplement semantic information is a natural choice, yet this approach faces a fundamental mismatch between discrete 3D points and continuous 2D images. This mismatch leads to inevitable projection overlap and complex modality alignment, resulting in compromised semantic consistency across 2D-3D transfer. To address these limitations, this paper proposes PointGS, a simple yet effective pipeline for unsupervised 3D point cloud segmentation. PointGS leverages 3D Gaussian Splatting as a unified intermediate representation to bridge the discrete-continuous domain gap. Input sparse point clouds are first reconstructed into dense 3D Gaussian spaces via multi-view observations, filling spatial gaps and encoding occlusion relationships to eliminate projection-induced semantic conflation. Multi-view dense images are rendered from the Gaussian space, with 2D semantic masks extracted via SAM, and semantics are distilled to 3D Gaussian primitives through contrastive learning to ensure consistent semantic assignments across different views. The Gaussian space is aligned with the original point cloud via two-step registration, and point semantics are assigned through nearest-neighbor search on labeled Gaussians. Experiments demonstrate that PointGS outperforms state-of-the-art unsupervised methods, achieving +0.9% mIoU on ScanNet-V2 and +2.8% mIoU on S3DIS.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection

Feng, Huidong, Chen, Wentao, Chen, Jie, Cai, Xinqi, Ma, Ruolong, Zheng, Yinglin, Lin, Yuxin, Zeng, Ming

原文摘要

With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security. Despite remarkable progress in existing deepfake detection methods, AIGC forgery detection remains challenging, as existing datasets mainly rely on open-source video generation models with quality far below commercial AIGC systems. Even datasets containing a few commercial samples often retain visible watermarks, compromising authenticity and hindering model generalization to high-fidelity AIGC videos. To address these issues, we introduce CoCoVideo-26K, a contrastive, commercial-model-based AIGC video dataset covering 13 mainstream commercial generators and providing semantically aligned real-fake video pairs. This dataset enables deeper exploration of the differences between authentic and high-quality synthetic videos, establishes a new benchmark for highly realistic video forgery detection. Building on this dataset, we propose CoCoDetect, a detection framework integrating contrastive learning with confidence-gated multimodal large language model (MLLM) inference. An R3D-18 backbone extracts spatio-temporal representations, while a confidence gate routes uncertain cases to an MLLM for reasoning about physical plausibility and scene consistency. Extensive experiments on CoCoVideo-26K and public benchmarks demonstrate state-of-the-art performance, validating the framework's robustness and generality. Our code and dataset are available at https://github.com/DonoToT/CoCoVideo.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Red-teaming Retrieval-Augmented Diffusion Models via Poisoning Knowledge Bases

Lyu, Xinqi, Liu, Yihao, Wang, Dong, Xiao, Bin

原文摘要

Retrieval-augmented diffusion models (RAG-DMs) have been increasingly deployed across applications, reflecting a broader trend of adopting retrieval-augmented pipelines in AI agent systems and the emerging OpenClaw framework. Despite the success, their trustworthiness remains underexplored. Existing backdoor attacks focus on either manipulating the generation phase or the retrieval phase under the white-box setting, which suffer from knowledge conflicts between retrieved images and user prompts. To bridge this gap, we propose a novel red-teaming approach JOB, which is the first jointly optimized backdoor attack tailored to black-box RAG-DMs. Specifically, JOB poisons the knowledge base with a small number of target class images and learns a trigger through multi-objective optimization, steering retrieval toward poisoned images and aligning the generated outputs with the target class, while preserving benign performance. Experiments show that JOB effectively attacks black-box RAG-DMs, achieving high success rates and outperforming state-of-the-art baselines.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion

Lu, Keyang, Zhou, Sifan, Xu, Hongbin, Xu, Gang, Yang, Zhifei, Wang, Yikai, Xiao, Zhen, Long, Jieyi 等 9 位作者

原文摘要

Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a novel agentic framework that enables user-customized and infinitely expandable 3D city generation by leveraging the reasoning and compositional capabilities of off-the-shelf large models. Specifically, Yo'City first conceptualizes the city through a top-down planning strategy that defines a hierarchical "City-District-Grid" structure. The Global Planner determines the overall layout and potential functional districts, while the Local Designer further refines each district with detailed grid-level descriptions. Subsequently, the grid-level 3D generation is achieved through a produce-refine-evaluate isometric image synthesis loop, followed by image-to-3D generation. To simulate continuous city evolution, Yo'City further introduces a user-interactive, relationship-guided expansion mechanism, which performs scene graph-based distance- and semantics-aware layout optimization, ensuring spatially coherent city growth. To comprehensively evaluate our method, we construct a diverse benchmark dataset and design six multi-dimensional metrics that assess generation quality from the perspectives of semantics, geometry, texture, and layout. Extensive experiments demonstrate that Yo'City consistently outperforms existing state-of-the-art methods across all evaluation aspects.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning

Ding, Shengyuan, Fang, Xinyu, Liu, Ziyu, Zang, Yuhang, Cao, Yuhang, Zhao, Xiangyu, Duan, Haodong, Dong, Xiaoyi 等 13 位作者

原文摘要

Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks.We present **ARM-Thinker**, an **A**gentic multimodal **R**eward **M**odel that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring.This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models.We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy.To evaluate agentic reward modeling, we introduce **ARMBench-VL**, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification).ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks.Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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GR-Gauge: Cost-efficient Training Configuration By Gauging the Gradient Redundancy

Wang, Guanjie, Chen, Chen

原文摘要

The recent success of artificial intelligence motivates many non-professional users to train their own models. Those users often resort to cloud training services, seeking to obtain a sufficiently accurate model at a modest cost, for which properly setting up the learning rate and batch size is crucial. While various Hyper-parameter Optimization (HPO) methods have been proposed in that regard, they largely act based on heavy-weight validation signals, being inefficient in the overall cost. We find that the model training process can be viewed as a two-dimensional voting process---with gradients for different iterations and from different samples; moreover, to attain cost-efficient training is to ensure that the gradient redundancy is within a proper range which is similar across diverse models. Based on that insight, we further introduce GR-Gauge, a general method that gauges the gradient redundancy to instruct HPO decisions like configuration searching and trial termination. Extensive experiments demonstrate that GR-Gauge can help attain near-optimal accuracy in much less time than existing methods.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling

Yang, Zuhao, Wang, Sudong, Zhang, Kaichen, Wu, Keming, Leng, Sicong, Zhang, Yifan, Li, Bo, Qin, Chengwei 等 11 位作者

原文摘要

Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought. However, they remain vulnerable to hallucinations, especially when processing long-form videos where evidence is sparse and temporally dispersed. Inspired by how humans comprehend long videos--by first skimming globally and then examining relevant clips for details--we introduce LongVT, an end-to-end agentic framework that enables "Thinking with Long Videos" via interleaved Multimodal Chain-of-Tool-Thought. Specifically, we exploit LMMs' inherent temporal grounding ability as a native video cropping tool to zoom in on a specific video clip and resample finer-grained video frames. This global-to-local reasoning loop continues until answers are grounded in retrieved visual evidence. Given the scarcity of fine-grained question-answering (QA) data for the long video reasoning task, we curate and release a data suite named VideoSIAH to facilitate both training and evaluation. Specifically, our training dataset consists of 247.9K samples for tool-integrated cold-start supervised fine-tuning, 1.6K samples for agentic reinforcement learning, and 15.4K samples for agentic reinforcement fine-tuning. Our evaluation benchmark consists of 652 QA pairs that are carefully curated through a semi-automatic data pipeline with human-in-the-loop validation. With a meticulously designed three-stage training strategy and extensive empirical validation, LongVT consistently outperforms existing strong baselines across four challenging long-video understanding and reasoning benchmarks. Our code, data, and model weights are publicly available at https://github.com/EvolvingLMMs-Lab/LongVT.

cs.CV · cs.AI

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SAGE: Scalable Agentic 3D Scene Generation for Embodied AI

Xia, Hongchi, Li, Xuan, Li, Zhaoshuo, Ma, Qianli, Xu, Jiashu, Liu, Ming-Yu, Cui, Yin, Lin, Tsung-Yi 等 12 位作者

原文摘要

Real-world data collection for embodied agents remains costly and unsafe, calling for scalable, realistic, and simulator-ready 3D environments. However, existing scene-generation systems often rely on rule-based or task-specific pipelines, yielding artifacts and physically invalid scenes. We present SAGE, an agentic framework that, given a user-specified embodied task (e.g., "pick up a bowl and place it on the table"), understands the intent and automatically generates simulation-ready environments at scale. The agent couples multiple generators for layout and object composition with critics that evaluate semantic plausibility, visual realism, and physical stability. Through iterative reasoning and adaptive tool selection, it self-refines the scenes until meeting user intent and physical validity. The resulting environments are realistic, diverse, and directly deployable in modern simulators for policy training. Policies trained purely on this data exhibit clear scaling trends and generalize to unseen objects and layouts, demonstrating the promise of simulation-driven scaling for embodied AI. Code, demos, and the SAGE-10k dataset can be found on the project page: https://research.nvidia.com/labs/dir/sage/.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos

Yu, Shoubin, Shu, Lei, Yang, Antoine, Fu, Yao, Sunkara, Srinivas, Wang, Maria, Chen, Jindong, Bansal, Mohit 等 9 位作者

原文摘要

Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limitation: they focus entirely on web-based interaction and perception, lacking grounding in the user's real-world physical surroundings. This limitation prevents evaluation in crucial scenarios, such as when an agent must use egocentric visual perception (e.g., via AR glasses) to recognize an object in the user's surroundings and then complete a related task online (e.g., making a purchase related to that object). To address this gap, we introduce Ego2Web, the first benchmark designed to bridge egocentric video perception and web agent execution. Ego2Web pairs real-world first-person video recordings with web tasks that require visual understanding, web task planning, and interaction in an online environment for successful completion. We utilize an automatic data-generation pipeline combined with human verification and refinement to curate well- constructed, high-quality video-task pairs across diverse task types, including e-commerce, media retrieval, knowledge lookup, etc. To facilitate accurate and scalable evaluation for our benchmark, we also develop a novel LLM-as-a-Judge automatic evaluation method, Ego2WebJudge, which achieves approximately 84% agreement with human judgment, substantially higher than existing evaluation methods. Experiments with diverse SoTA agents on our Ego2Web benchmark show that their performance is still weak, with substantial headroom across all task categories. We also conduct a comprehensive ablation study on task design, highlighting the necessity of accurate video understanding in Ego2Web and the limitations of current agents. We hope Ego2Web can be a critical new resource for developing capable AI assistants that can seamlessly see, understand, and act across the physical and digital worlds.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos

Ge, Mengmeng, Isobe, Takashi, Jia, Xu, Sun, Yanan, Yang, Zetong, Wang, Weinong, Zhou, Dong, Li, Dong 等 10 位作者

原文摘要

Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric perspective, which serves as a key bridge between humans and machines in action modeling. We define this modeling process as Egocentric Instructed Visual State Transition (EIVST), which involves generating intermediate frames that depict object transformations between initial and target states under a brief action instruction. EIVST poses two challenges for current generative models: (1) understanding the visual scenes of the initial and target states and reasoning about transformation steps from an egocentric view, and (2) generating a consistent intermediate transition that follows the given instruction while preserving object appearance across the two visual states. To address these challenges, we propose the EgoIn framework. It first infers the multi-step transition process between two given states using TransitionVLM, fine-tuned on our curated dataset to better adapt to this task and reduce hallucinated information. It then generates a sequence of frames based on transition conditions produced by the proposed Transition Conditioning module. Additionally, we introduce Object-aware Auxiliary Supervision to preserve consistent object appearance throughout the transition. Extensive experiments on human-object and robot-object interaction datasets demonstrate EgoIn's superior performance in generating semantically meaningful and visually coherent transformation sequences.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking

Lin, Jingyang, Wu, Jialian, Liu, Jiang, Sun, Ximeng, Wang, Ze, Yu, Xiaodong, Luo, Jiebo, Liu, Zicheng 等 9 位作者

原文摘要

Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively parsing the full video. This insight allows the model to use far fewer frames while maintaining, or even improving, its video understanding capability. VideoSeek operates in a think-act-observe loop with a well-designed toolkit for collecting multi-granular video observations. This design enables query-aware exploration over accumulated observations and supports practical video understanding and reasoning. Experiments on four challenging video understanding and reasoning benchmarks demonstrate that VideoSeek achieves strong accuracy while using far fewer frames than prior video agents and standalone LMMs. Notably, VideoSeek achieves a 10.2 absolute points improvement on LVBench over its base model, GPT-5, while using 93% fewer frames. Further analysis highlights the significance of leveraging video logic flow, strong reasoning capability, and the complementary roles of toolkit design.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL

Chen, Siyi, Uy, Mikaela Angelina, Song, Chan Hee, Ladhak, Faisal, Murali, Adithyavairavan, Qu, Qing, Birchfield, Stan, Blukis, Valts 等 9 位作者

原文摘要

Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth estimators, segmentation models, and pose estimators. Yet it remains an open challenge how to realize this vision without solely relying on handcrafted prompting strategies or enforcing fixed, predefined tool pipelines that limit VLMs' ability to discover optimal tool-use patterns. Reinforcement Learning could overcome this gap, but has so far been limited to reasoning with a single visual tool due to the large search space in multi-tool reasoning. We introduce Double Interactive Reinforcement Learning (DIRL), a two-phase training framework where VLMs learn to coordinate multiple tools through interactive exploration and feedback. In the teaching phase, we combine demonstrations from a single tool specialist trained via interactive RL with traces from a frontier model using all tools. In the exploration phase, the model further refines multi-tool coordination through continued RL. Our model, SpaceTools, with tool-augmented spatial reasoning ability, achieves state-of-the-art performance on spatial understanding benchmarks (RoboSpatial-Home, BLINK, Internal Benchmark) and demonstrates reliable real-world manipulation using a 7-DOF robot as a tool. DIRL provides substantial improvements over the vanilla SFT (+12% on RoboSpatial) and RL (+16% on RoboSpatial) baselines. Project page: https://spacetools.github.io/.

cs.CV · cs.CL · cs.AI

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OctoT2I: A Self-Evolving Agentic Text-to-Image Router

Jiang, Xu, Chen, Bin, Li, Gehui, Duan, Yule, Wang, Ronggang, Zhang, Jian

原文摘要

The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns from single-model scaling. Agentic T2I methods emerged to alleviate this bottleneck by using multiple models. However, existing agentic T2I methods suffer from three key challenges: reliance on expensive handcrafted priors or human annotations, rigid single-path decision mechanisms, and a neglect of inference efficiency. To address these challenges, we introduce OctoT2I, a novel agentic framework that reformulates the T2I task as a joint optimization of generation quality and inference efficiency. OctoT2I implements a stateful, multi-round routing strategy that adaptively selects the most suitable tool based on its knowledge and memory. This strategy is enabled by a knowledge base built from scratch by our novel Self-Evolving Mechanism. This mechanism, which requires no human supervision, first autonomously defines foundational Conceptual Dimensions (e.g., style, color, count) and then intelligently explores their combinations via an iterative "Propose--Solve--Evaluate--Learn" (PSEL) loop. The PSEL loop efficiently discovers each tool's capability frontier, driving continuous improvement without external guidance. Extensive experiments demonstrate that OctoT2I achieves competitive performance (0.96) on GenEval while delivering a 90.3% inference speedup and a 56.6% energy-efficiency gain over the leading baseline (Flow-GRPO), striking an exceptional balance between performance and efficiency. Code and models will be made available.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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