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新论文 · v1arXiv:2611.13968

Advancing a Global Framework for AI Safety and Governance for the Well-being of Humanity

Zeng Yi, Seán Ó hÉigeartaigh, Wang Zhengqi, Lu Enmeng, Chen Yu, Cao Gongce, Guo Xiaoyang, Kang Yanrong 等 76 位作者

原文摘要

The Report "Advancing a Global Framework for AI Safety and Governance for the Well-being of Humanity" is an important outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on Artificial Intelligence in 2025. This report provides a systematic review of current global explorations and practices in AI safety and governance, identifies key issues that need to be addressed, and draws upon multilateral governance experiences from other domains. Centered on mechanism design, it explores how to build a global AI safety and governance system that ensures safety and security, promotes inclusiveness, clarifies responsibilities and accountabilities, strengthens effective coordination, and ensures authoritative and efficient governance. Guided by the principles of multilateralism and the vision of building a community with a shared future for humankind, the report proposes institutional measures and policy recommendations to establish a United Nations-centered global AI safety and governance framework. It aims to serve as a reference for governments, international organizations, enterprises, and research institutions. The report is to contribute insights and momentum to advancing people-centered and beneficial AI governance worldwide, ensuring that AI technologies truly serve the well-being of all humanity.

cs.CY

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.43ACL Anthology · ACL 2026

A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting

Yu Lei, Zixuan Wang, Yiqing Feng, Junru Zhang, Yahui Li, Liu Chu, Wang Tongyao, Dongyang Li

原文摘要

Recent industrial credit scoring models remain heavily reliant on manually tuned statistical learning methods. Despite their potential, deep learning architectures have struggled to consistently outperform traditional statistical models in industrial credit scoring, largely due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. To bridge this gap, we introduce FinLangNet, a novel framework that reformulates credit scoring as a multi-scale sequential learning problem. FinLangNet processes heterogeneous financial data through a dual-module architecture that combines tabular feature extraction with temporal sequence modeling, generating probability distributions of users’ future financial behaviors across multiple time horizons. A key innovation is our dual-prompt mechanism within the sequential module, which introduces learnable prompts operating at both feature-level granularity for capturing fine-grained temporal patterns and user-level granularity for aggregating holistic risk profiles. Notably, real world deployment yielded a 6.3 pp improvement in KS, along with a 9.9% reduction in bad debt rate.

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.1142ACL Anthology · ACL 2026

Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement

Zhenxin Qin Tongji University, Qiang Li Tongji University, Qingzhuo Wang Tongji University, Ruiyang Qin, Zhihua Wei Tongji University, Wen Shen Tongji University

原文摘要

Large Vision-Language Models (LVLMs) have achieved remarkable performance on diverse vision-language tasks. However, LVLMs still suffer from hallucinations, generating text that contradicts the visual input. Existing research has primarily focused on mitigating object hallucinations, but often overlooks more complex relation hallucinations, particularly action relations involving interactions between objects. In this study, we empirically observe that the primary cause of action-relation hallucinations in LVLMs is the insufficient attention allocated to visual information. Thus, we propose a framework to locate action-relevant image regions and enhance the LVLM’s attention to those regions. Specifically, we define the Action-Relation Sensitivity (ARS) score to identify attention heads that are most sensitive to action-relation changes, thereby localizing action-relevant image regions that contain key visual cues. Then, we propose the Relation-aware Visual Enhancement (RVE) method to enhance the LVLM’s attention to these action-relevant image regions. Extensive experiments demonstrate that, compared to existing baselines, our method achieves superior performance in mitigating action-relation hallucinations with negligible additional inference cost. Furthermore, it effectively generalizes to spatial-relation hallucinations and object hallucinations.

cs.CL · 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 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1491ACL Anthology · ACL 2026

StratMem-Bench: Evaluating Strategic Memory Use in Virtual Character Conversation Beyond Factual Recall

Yerong Wu Southeast University, Tianxing Wu, Minghao Zhu Alibaba Group, Hangyu Sha Southeast University, Haofen Wang Tongji University

原文摘要

Achieving realistic human-like conversation for virtual characters requires not only a simple memorization and recall of past events, but also the strategic utilization of memory to meet factual needs and social engagement. Current memory utilization relevant (e.g., memory-augmented generation, long-term dialogue, and etc.) benchmarks overlook this nuance, treating memory primarily as a static repository of facts rather than a dynamic resource to be strategically deployed in dialogues. To address this gap, we design StratMem-Bench, a new benchmark to evaluate strategic memory use in character-centric dialogues. This dataset comprises 657 instances where virtual characters must navigate heterogeneous memory pools containing required, supportive, and irrelevant memories. We also propose a framework with different evaluation metrics including Strict Memory Compliance, Memory Integration Quality, Proactive Enrichment Score and Conditional Irrelevance Rate, and to evaluate strategic memory use capabilities of virtual characters. Experiments on StratMem-Bench which leverage the state-of-the-art large language models as virtual characters show that all models perform well at distinguishing between required and irrelevant memories, but struggle once supportive memories are introduced into the decision process.

cs.CL

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 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1617ACL Anthology · ACL 2026

CityVG: Contrastive Fine-Tuning and Reward-Based Chain-of-Thought Reasoning for Zero-Shot City-Scale 3D Visual Grounding

Jianjun Zhang, Hanli Wang Tongji University

原文摘要

3D Visual Grounding (3DVG) locates objects in 3D scenes based on natural language descriptions. However, existing methods are primarily confined to small-scale indoor data or rely on heavy supervision, failing to generalize to the complexity of large-scale urban environments. To address this limitation, we present CityVG, the first city-scale zero-shot 3D visual grounding framework capable of localizing urban objects without manual annotations. Our approach adopts a retrieval-and-reasoning paradigm comprising two key components. Specifically, we propose a contrastive fine-tuning strategy to align textual queries with urban scene graphs. By leveraging an LLM-driven graph clustering mechanism, we automatically construct high-quality positive and negative training pairs and fine-tune the text encoder via contrastive learning, resulting in a scene-adaptive text encoder that enables efficient alignment without grounding supervision. Complementing this, we introduce a multi-trajectory reward-based Chain-of-Thought (CoT) reasoning strategy for inference. This mechanism iteratively evaluates candidate objects by aggregating reward scores across diverse reasoning trajectories, selecting the target that is most consistent with both appearance and spatial constraints. Extensive experiments on city-scale 3D grounding benchmarks demonstrate that CityVG achieves strong zero-shot localization performance and generalizes effectively to unseen urban environments.

cs.CL

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