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新论文 · v1arXiv:2611.16493

ReA-Bench: Benchmarking Responsible Agent Auditing

Zipeng Ye, Shenyu Dai, Yong Xie, Kun Wang, Jiyang Guan, Zhikai Chen, Jiaqi Weng, Jialing Tao 等 9 位作者

原文摘要

As agents perform tasks autonomously in local and cloud environments, auditing their execution trajectories can help service providers identify risks and improve agent harnesses and foundation models. Our analysis of production and red-team trajectories shows that some risks are repeatedly missed across auditing configurations. Overall recall obscures which risks are missed and whether higher recall comes with more false positives on legitimate behavior. Existing benchmarks offer limited insight into these failures. Generated trajectories can contain inconsistent tool calls and responses, conspicuous risky actions, or repetitive attack patterns. Grouping results by risk type also makes it difficult to tell whether auditors overlooked evidence, accepted an unsupported conclusion, or lacked information needed to make a judgment. We introduce ReA-Bench, a diagnostic benchmark for auditing responsible agent behavior. It organizes auditing challenges into six tiers: internal safety knowledge, contradictions within trajectories, evidence retrieval in long contexts, verification of sources and reasoning, indirect steering, and missing information. It includes challenging benign cases in which actions resemble risky ones but are consistent with the task and its authorization. It also provides paired trajectory versions to test whether auditors detect the same risks more reliably when the trajectory is shortened or missing information is supplied. Most samples are drawn from real production trajectories, with scenarios manually selected and adapted to preserve realistic tasks and risks that could plausibly arise during execution.

cs.AI

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.90ACL Anthology · ACL 2026

RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services

Fei zhao, Chonggang Lu, Haofu Qian, Fangcheng Shi, Zijie Meng, Jianzhao Huang, Zheyong Xie, Shaosheng Cao

原文摘要

As a primary medium for human interaction and information exchange, social networking services (SNS) present distinct challenges for large language models (LLMs): rapidly evolving norms and slang, and culturally diverse content that causes knowledge distribution shift. While supervised fine-tuning (SFT) can improve in-domain performance, it often induces a ”seesaw” trade-off with out-of-domain robustness, especially for smaller models. To address these challenges, we present RedOne 2.0, an SNS-oriented LLM developed with a progressive, RL-prioritized post-training paradigm for fast and stable adaptation. Our pipeline has three stages: (1) Exploratory Learning on curated SNS corpora to establish initial alignment and surface systematic weaknesses; (2) Targeted Fine-Tuning that applies SFT only to diagnosed gaps while mixing a small amount of general data to reduce forgetting; and (3) Refinement Learning that re-applies RL with SNS-centric signals to consolidate gains and balance trade-offs across tasks. Across various tasks in three categories, our 4B model improves by 2.41 on average over the prior 7B RedOne baseline. It also yields an 8.74 average gain over its Qwen3-4B base while using less than half the data required by the SFT-centric method, demonstrating superior data efficiency and stability at compact scales. Overall, RedOne 2.0 provides a competitive, cost-effective baseline for SNS-specific LLMs, improving capability without sacrificing robustness.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.204ACL Anthology · ACL 2026

Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding

Sensen Gao, Shanshan Zhao Alibaba Group, Xu Jiang, Lunhao Duan Alibaba Group, Yong Xien Chng Tsinghua University, Tsinghua University, Qing-Guo Chen Alibaba Group, Weihua Luo Alibaba International Digital Commerce Group, Kaifu Zhang Alibaba Group 等 10 位作者

原文摘要

Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs), face key limitations: the former loses structural detail, while the latter struggles with context modeling. Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents’ multimodal nature, i.e., combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG. This approach enables holistic retrieval and reasoning across all modalities, unlocking comprehensive document intelligence. Recognizing its importance, this paper presents a systematic survey of Multimodal RAG for document understanding. We propose a taxonomy based on domain, retrieval modality, and granularity, and review advances involving graph structures and agentic frameworks. We also summarize key datasets, benchmarks, and applications, and highlight open challenges in efficiency, fine-grained representation, and robustness, providing a roadmap for future progress in document AI.

cs.CL · cs.AI · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1CVF · CVPR 2026:cvf:f46342281105c0c62e6d5e6e0dc55037CVF · CVPR 2026

SenseSearch: Empowering Vision-Language Models with High-Resolution Agentic Search-Reasoning via Reinforcement Learning

Chng, Yong Xien, Hu, Tao, Tong, Wenwen, Li, Xueheng, Chen, Jiandong, Yu, Haojia, Lu, Jiefan, Guo, Hewei 等 12 位作者

原文摘要

Vision-Language Models (VLMs) are limited by static knowledge and insufficient fine-grained visual analysis, hindering their performance on knowledge-intensive and visually complex tasks. While recent research has explored VLMs that employ external tools like search or cropping to enhance model performance, they typically employ tools in isolation and lack the ability to coordinate multiple tools effectively. To address this gap, we propose SenseSearch, the first agentic VLM for search-reasoning that supports adaptive multi-tool coordination via reinforcement learning (RL). Specifically, SenseSearch dynamically integrates the image search, text search, and image crop tools to tackle fine-grained and knowledge-intensive visual understanding challenges. We first construct a high-quality cold-start dataset to instill basic tool-usage behaviors. In the subsequent RL stage, we introduce Batch-Normalized Group Sequence Policy Optimization (BN-GSPO) algorithm to enhance the tool invocation and reasoning ability. To comprehensively evaluate the agentic VLMs on complex visual tasks, we introduce the HR-MMSearch benchmark, the first search-oriented benchmark composed of high-resolution images with knowledge-intensive and search-driven questions. Experiments demonstrate that SenseSearch achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks, outperforming baselines by 19.18% on HR-MMSearch. SenseSearch provides a promising path toward agentic VLMs with effective and robust tool invocation capabilities. All code and data are released at https://github.com/OpenSenseNova/SenseNova-MARS.

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 ↗