Junmyeong Lee, Chan Hur Electronics and Telecommunications Research Institute, ChangSu Choi, Sukmin Cho Korea Advanced Institute of Science and Technology, Fitsum Gaim, Eui Jun Hwang Korea Advanced Institute of Science & Technology, Hoyun Song Korea Advanced Institute of Science & Technology, KyungTae Lim Korea Advanced Institute of Science & Technology
Sign Language Retrieval (SLRet) enables efficient access to sign language content but remains fragile in fine-grained scenarios where visually similar signs must be distinguished. We show that this limitation does not stem from model capacity, but from ineffective hard negative supervision. Specifically, we formulate fine-grained retrieval failures as a negative distribution mismatch: semantically distinct yet visually confusable signs are rarely treated as hard negatives, while existing text-based mining strategies fail to capture such visual ambiguity. To address this issue, we propose Sign-Aware Hard Negative Mining (SAN), which constructs hard negatives based on visual confusability in the sign embedding space rather than linguistic similarity. Experiments on PHOENIX-2014T demonstrate that SAN substantially improves fine-grained retrieval performance while preserving coarse-grained accuracy, highlighting the importance of aligning negative supervision with visual ambiguity in sign language retrieval.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Joonhyung Park Korea Advanced Institute of Science & Technology, Jaeyun Song KRAFTON Inc., Sihwan Park Korea Advanced Institute of Science & Technology, Eunho Yang Korea Advanced Institute of Science & Technology
Recent proprietary video generation models have demonstrated remarkable proficiency in synthesizing highly realistic videos from textual instructions. Most open-source text-to-video models, however, still struggle to accurately simulate real-world physics and dynamic entity interactions. Existing approaches rely on scaling laws and large-scale, high-quality video datasets to implicitly learn physical dynamics, yet this paradigm is constrained by prohibitive costs and the burdensome demands of data curation. Motivated by this, we propose a novel framework that integrates graph-structured temporal knowledge into video latent diffusion models to enhance compositional generation and interaction fidelity. Our framework constructs video scene graphs specifically designed to capture entity relationships, temporal dynamics, and global scene context. These graph-structured representations guide the generation process through cross-attention mechanisms. Additionally, we introduce Graph-Aligned Denoising Loss (GADL), a training objective that ensures adherence to conditioned graphs by incorporating node modification tasks within the denoising process, leveraging synchronized edited video-graph pairs. Comprehensive evaluations demonstrate that incorporating graph-structured knowledge significantly enhances compositionality and the accurate portrayal of real-world interactions in generated videos.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Huije Lee Korea Advanced Institute of Science & Technology, Jisu Shin Korea Advanced Institute of Science & Technology, Hoyun Song Korea Advanced Institute of Science & Technology, Changgeon Ko, Jong C. Park Korea Advanced Institute of Science and Technology
Static benchmarks for harmful content detection face limitations in scalability and diversity, and may also be affected by contamination from web-scale pre-training corpora. To address these issues, we propose a framework for synthesizing harmful content, leveraging persona-guided large language model (LLM) agents. Our approach constructs two-dimensional user personas by integrating demographic identities and topical interests with situational harmful strategies, enabling the simulation of diverse and contextually grounded harmful interactions. We evaluate the framework along three dimensions: harmfulness, challenge level, and diversity. Both human and LLM-based evaluations confirm that our framework achieves a high harmful generation success rate. Experiments across multiple detection systems reveal that our synthetic scenarios are more challenging to detect than those in existing benchmarks. Furthermore, a multi-faceted analysis confirms that our approach achieves linguistic and topical diversity comparable to human-curated datasets, establishing our framework as an effective tool for robust stress-testing of harmful content detection systems.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Changgeon Ko, Jisu Shin Korea Advanced Institute of Science & Technology, Hoyun Song Korea Advanced Institute of Science & Technology, Huije Lee Korea Advanced Institute of Science & Technology, Eui Jun Hwang Korea Advanced Institute of Science & Technology, Jong C. Park Korea Advanced Institute of Science and Technology
Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make a final decision. Drawing inspiration from social psychology, we investigate how the reliability of this representative agent is undermined by the social context of its network. We define four key phenomena—social conformity, perceived expertise, dominant speaker effect, and rhetorical persuasion—and systematically manipulate the number of adversaries, relative intelligence, argument length, and argumentative styles. Our experiments demonstrate that the representative agent’s accuracy consistently declines as social pressure increases: larger adversarial groups, more capable peers, and longer arguments all lead to significant performance degradation. Furthermore, rhetorical strategies emphasizing credibility or logic can further sway the agent’s judgment, depending on the context. These findings reveal that multi-agent systems are sensitive not only to individual reasoning but also to the social dynamics of their configuration, highlighting critical vulnerabilities in AI delegates that mirror the psychological biases observed in human group decision-making.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zheng Jia, Shengbin Yue, Wei Chen Huazhong University of Science and Technology, Siyuan Wang The Chinese University of Hong Kong, Yidong Liu Harbin Institute of Technology and Midu Technology CO.,Ltd., Zejun Li, Yun Song, Zhongyu Wei Fudan University
The gap between existing benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. Guided by legal experts, it comprises six representative scenarios from Chinese legal practices at three levels of environmental complexity. We further introduce J1-EVAL, a dual-metric evaluation framework, designed to assess both task performance and procedural compliance across varying levels of legal proficiency. Extensive experiments on 17 LLM agents reveal that while many models demonstrate solid legal knowledge, they struggle with procedural execution in dynamic settings. Even the SOTA model is below 60% overall performance . These findings highlight persistent challenges in achieving dynamic legal intelligence and offer valuable insights to guide future research.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表