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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1cb0464ccf01cc2614b9d2a50944d9f3CVF · CVPR 2026

DRAMA: Next-Gen Dynamic Orchestration for Resilient Multi-Agent Ecosystems in Flux

Zhao, Xinkui, Zhang, Yifan, Liu, Sai, Wang, Naibo, Cheng, Guanjie, Xu, Yueshen, Liu, Chang, Deng, Shuiguang 等 9 位作者

原文摘要

Embodied Multi-Agent Systems have proven highly effective in addressing complex tasks through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, exhibiting frequent changes, uncertainty, and variability. Despite these characteristics, most existing frameworks employ static architectures with fixed agent capabilities and rigid task allocation strategies, which substantially constrain their adaptability to evolving conditions. This inflexibility presents significant challenges to maintaining robust and efficient multi-agent cooperation in dynamic and unpredictable settings.To address these limitations, we propose DRAMA, short for Dynamic Orchestration for Resilient Multi-Agent Ecosystems, tailored for rapidly changing environments. DRAMA adopts a multilayer architecture that incorporates three principal mechanisms: adaptive scheduling through an affinity-driven mechanism, fault-tolerant continuity via hierarchical trust-chain task takeover, and collective spatial intelligence that consolidates distributed observations for predictive reasoning. Together, these components enable event-triggered rescheduling and decentralized fault recovery, ensuring uninterrupted task execution amid agent arrivals, dropouts, or recoveries. Extensive experiments in the embodied VirtualHome-Social environment demonstrate that DRAMA achieves a 7% improvement in runtime efficiency and a 10% increase in throughput compared with state-of-the-art baselines, while maintaining superior stability and robustness under dynamic agent populations.

cs.CV · cs.MA

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:2233453cd3bd5bbf1e28008855ee3871CVF · CVPR 2026

Towards Human-Like Robot Handwriting via Contour-Aware Generation

Qin, Yutao, Dai, Gang, Zhang, Yifan, Han, Youwei, He, Qisheng, Huang, Shuangping

原文摘要

Empowering machines to simulate human handwriting is a promising research direction. Most existing methods, however, primarily focus on reproducing the writing trajectory to capture the overall character structure, while neglecting the critical aspect of stroke contour modeling. Consequently, these methods struggle to generate visually realistic, human-like handwriting, limiting their applicability in scenarios such as calligraphy robots. To address this issue, we propose a new task, called Contour-aware Handwriting Trajectory Reconstruction (CHTR). This task presents two major challenges: 1) Existing handwriting datasets lack stroke contour annotations, making supervised learning difficult; 2) Previous methods are unable to recover stroke contour and preserve the overall character structure jointly. To address the dataset limitation, we present CHTR-110K, a large-scale character dataset with refined stroke contour annotations. To tackle the technical challenge, we propose Graph-based Handwriting Trajectory Reconstruction (G-HTR), a novel method using contour-aware graphs to jointly model stroke contour and character structure. We use a Graph Neural Network to capture structural relationships among nodes and introduce a multi-scale graph learning strategy to encode both fine-grained stroke details and global character structure. Extensive experiments verify the effectiveness of G-HTR, outperforming previous state-of-the-art methods on both our CHTR-110K and the widely-used CASIA-OLHWDB dataset. G-HTR further shows strong real-world results when deployed on robots, confirming its practical value. To support future research, we will release source code and dataset.

cs.CV

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:5c856bdc1f66d8bd8839654071e28a6cCVF · CVPR 2026

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

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:9e1bedcf899bdd6743e7a2cf46abb1a5CVF · CVPR 2026

PersonaVLM: Long-Term Personalized Multimodal LLMs

Nie, Chang, Fu, Chaoyou, Zhang, Yifan, Yang, Haihua, Shan, Caifeng

原文摘要

Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.img1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.

cs.CV · cs.CL

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 ↗