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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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:374335e7f1f817157dd8f8bb46e1a604CVF · CVPR 2026

EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models

Fang, Yiyang, Huang, Wenke, Fu, Pei, Yang, Yihao, Su, Kehua, Luo, Zhenbo, Luan, Jian, Ye, Mang

原文摘要

Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture the complexity and subjectivity of human emotions. Existing approaches based on supervised fine-tuning often suffer from limited generalization and poor interpretability, while reinforcement learning methods such as Group Relative Policy Optimization fail to align with the intrinsic characteristics of emotional cognition.To address these challenges, we propose Reflective Reinforcement Learning for Emotional Reasoning (EMO-R3), a framework designed to enhance the emotional reasoning ability of MLLMs. Specifically, we introduce Structured Emotional Thinking to guide the model to perform step-by-step emotional reasoning in a structured and interpretable manner, and design a Reflective Emotional Reward that enables the model to re-evaluate its reasoning based on visual-text consistency and emotional coherence. Extensive experiments demonstrate that EMO-R3 significantly improves both the interpretability and emotional intelligence of MLLMs, achieving superior performance across multiple visual emotional understanding benchmarks.

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 ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:5394d2f60c5d4c40181d542ca700b3c8CVF · CVPR 2026

FedSDR: Federated Graph Learning with Structural Noise Detection and Reconstruction

Liu, Jiaqi, Tan, Zihan, Wan, Guancheng, Huang, Wenke, Li, He, Ye, Mang

原文摘要

Federated Graph Learning (FGL) has emerged as a principled framework for decentralized training of Graph Neural Networks (GNNs) while preserving data privacy. In subgraph-FL scenarios, however, structural noise arising from data collection and storage can damage the GNN message-passing scheme of clients, leading to conflicts in collaboration. Existing approaches exhibit two critical limitations: 1) Globally, they fail to identify corrupted clients, causing destructive knowledge inconsistencies. 2) Locally, the global GNN performs poorly on these clients due to structural noise, limiting their ability to benefit from federated collaboration. To address these challenges, we propose FedSDR, a spectra-based FGL framework against high-structural-noise scenarios. Specifically, Structural Noise-Aware Aggregation (SNAA) introduces a structural fidelity evaluation metric to detect corrupted clients and reduce their contributions, thereby mitigating the impact of noise on the global GNN. Furthermore, Robust Local Structure Reconstruction (RLSR) leverages the knowledge from the healthy global model to repair locally corrupted graph structures. Extensive experiments demonstrate that FedSDR outperforms state-of-the-art methods across various scenarios under structural noise. The code is available at https://github.com/Subtleazure/FedSDR.

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:659497ff7130a592f1ab2589561e2b42CVF · CVPR 2026

SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization

Rong, Xuankun, Huang, Wenke, Wang, Tingfeng, Zhou, Daiguo, Du, Bo, Ye, Mang

原文摘要

Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new compositional safety risks that emerge from complex text-image interactions.Such cross-modal couplings can produce unsafe semantics even when individual inputs are benign, exposing the fragile safety awareness of current MLLMs.While recent works enhance safety by guiding models to reason about potential risks, unregulated reasoning traces may compromise alignment; although Group Relative Policy Optimization (GRPO) offers self-rewarded refinement without human supervision, it lacks verifiable signals for reasoning safety.To address this, we propose **SafeGRPO** a self-rewarded multimodal safety alignment framework that integrates rule-governed reward construction into GRPO, enabling interpretable and verifiable optimization of reasoning safety. Built upon the constructed **SafeTag-VL-3K** dataset with explicit visual, textual, and combined safety tags, SafeGRPO performs **step-guided safety thinking** to enforce structured reasoning and behavior alignment, substantially improving multimodal safety awareness, compositional robustness, and reasoning stability across diverse benchmarks without sacrificing general capabilities.

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 ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9389c8679934971bbfa1fb0d87963aa4CVF · CVPR 2026

WHU-MARS: A Multispectral Aerial-Ground Benchmark Towards Any-Scenario Person Re-Identification

Zhao, Yuxuan, Zhou, Zhongao, Yang, Bin, Li, He, Liang, Jian, Chen, Jun, Du, Bo, Ye, Mang

原文摘要

Recent person re-identification (ReID) leverages heterogeneous sensing with multiple modalities and viewpoints to improve robustness across diverse conditions. However, most approaches target predefined scenario pairs (e.g., visible-infrared or aerial-ground) and train separate task-specific models. In contrast, real-world systems often query against galleries that cover all scenarios, making such designs inefficient and complex to deploy. To bridge this gap, we introduce Any-Scenario ReID (AS-ReID): given a query from any (modality, viewpoint) scenario, a single model retrieves the same identity from a heterogeneous gallery spanning all scenarios. Progress toward AS-ReID is limited by two factors: (i) the lack of a real-world-aligned benchmark with broad scenario coverage, and (ii) the challenge of learning representations with strong intra-identity cohesion and inter-identity discrimination under diverse scenarios. To this end, we construct WHU-MARS, a multispectral aerial-ground benchmark with 2,337 identities and 434,620 images captured by RGB, near-infrared, and thermal infrared cameras on both ground and UAV platforms. WHU-MARS spans day-night, multiple seasons, and diverse weather, enabling AS-ReID as well as conventional ReID tasks. We further propose the Unified Alignment and Discrimination (UAD) framework. Progressive Center Alignment (ProCA) aggregates multi-view features into modality centers and then aligns them toward identity centers to reduce scenario bias. Global Prototype Discrimination (GPD) contrasts samples against global identity prototypes to enforce large-margin discrimination. Extensive experiments highlight the challenges of WHU-MARS and demonstrate the effectiveness of UAD on AS-ReID.

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:bee58a8216a6adf4cc503fbdf3762b9dCVF · CVPR 2026

Object-Generalized Re-Identification: A Step Towards Universal Instance Perception

Chen, Shuoyi, Wu, Yurui, Ye, Mang

原文摘要

The object re-identification (ReID) task aims to recognize the same individual object across diverse viewpoints and sensing conditions.Although person and vehicle ReID have achieved remarkable success, most existing methods are built on the assumption that training and testing data come from the same object category.This constraint requires separate models for each category, which limits scalability and generalization.To address this limitation, we introduce Object-Generalized Re-Identification (OG-ReID), a new paradigm that learns unified identity representations transferable across different object categories.Unlike conventional domain generalization that focuses on appearance variations within a single category, OG-ReID deals with category shifts caused by intrinsic structural differences in identity cues. To achieve this goal, we introduce the Meta-Generalized Object Re-Identification (MGOR) framework, which treats meta-learning as semantic distributional regularization, exposing the model to controlled category shifts so that invariance emerges as an equilibrium between semantic diversity and identity discrimination.Extensive evaluations on more than 100 unseen object categories from multiple domains show that MGOR outperforms existing ReID approaches without any target-domain adaptation, advancing toward universal identity perception beyond domain and category boundaries.

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:c260c602db9c98e945970ed0059f5636CVF · CVPR 2026

Batman: Benign Knowledge Alignment Through Malicious Null Space in Federated Backdoor Attack

He, Wenwen, Huang, Wenke, Fang, Yiyang, Qu, Wenjie, Zhang, Jiaheng, Ye, Mang

原文摘要

Federated Learning (FL), a distributed learning paradigm that enables local training on user-held data across decentralized devices, is vulnerable to backdoor attacks due to limited visibility into client updates. Exploiting this opacity, adversaries induce targeted misbehavior on trigger inputs without affecting overall performance, thereby compromising the trust and integrity of collaborative training in federated learning systems. Existing federated backdoor attacks mainly concentrate on benign knowledge alignment on trigger-surface design or representation guidance to evade defense mechanisms. However, trigger-surface attacks suffer from insufficient alignment, leaving malicious knowledge distinguishable from benign updates. In contrast, representation-guided attacks attempt to obscure the boundary between benign and malicious behaviors. Nevertheless, excessive incorporation of benign knowledge within a shared parameter space leads to over-alignment, ultimately degrading attack effectiveness. To overcome shared parameter space dilemma in backdoor attack, we propose Batman, a novel backdoor attack that aligns benign knowledge within the malicious null space, which effectively decouples malicious space from shared parameter space and enables benign alignment in an orthogonal direction of this space that does not interfere with the attack effectiveness. To further enhance stealthiness, we combine both clean and global models to guide the alignment perturbation within this null space to evade detection. Experiments on four benchmark datasets demonstrate that Batman consistently achieves strong backdoor performance while remaining stealthy under various defenses.

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:ca852b19178e54603d7efe0afe61e11cCVF · CVPR 2026

Towards Cross-Modal Preservation, Consistency and Alignment for Privacy-Preserving Visible-Infrared Person Re-Identification

Xie, Yudi, Zhou, Zhongao, Yang, Bin, Chen, Zhenghan, Ye, Mang

原文摘要

Privacy-preserving Person Re-Identification (PP-ReID) addresses the core privacy-utility trade-off in Re-ID by retrieving a person across multiple non-overlapping cameras while applying anonymization techniques to protect sensitive information. However, prior PP-ReID studies are confined to single-modality visible scenarios, as 24-hour surveillance systems require robust cross-modal visible-infrared (VI) capabilities. Extending PP-ReID to the cross-modal VI setting is therefore crucial. Accordingly, we introduce a new task: Privacy-Preserving Visible-Infrared Person Re-Identification (PP-VI-ReID). This task presents two severe challenges: 1) Crude anonymization strategies destroy identity-critical information and disrupt cross-modal alignment 2) The anonymization process creates inconsistent distortions across modalities. It disrupts color-based textures in visible images while obscuring thermal contours in infrared images. This inconsistency with modality gap forms a Mixed Gap. To overcome these challenges, we propose a framework, the Precise Privacy-preserving and Alignment Network (PPA) with two components: 1) A Keypoint-Preserving Regularization (KPR) module leverages human pose as a prior to guide structure-aware anonymization, preserving essential body features. 2) A Differential Consistency-guided Modality Alignment (DCMA) treats anonymization perturbations not as varying noise, but as a stable, learnable offset, facilitating robust alignment between raw and anonymized features across modalities. Experiments on SYSU-MM01 and RegDB validate our framework, establishing a strong baseline.

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 ↗