The paper evaluates Jev, a specialized decision model, on stance detection tasks using the VAST (English) and ZS-CSD (Chinese) datasets. Jev matches GPT‑5.6 and outperforms other general‑purpose LLMs on VAST, but lags behind stronger LLMs on ZS‑CSD, especially in distinguishing favor from against. The authors suggest that Jev’s limitations may stem from difficulties in understanding reply relationships and stance direction rather than conversation length.
By Xing Li, Jinzhong Ning, Yijia Zhang, Liang Yang, Hongfei Lin
arXiv:2607. 24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling.
By Heyan Chai, Xin Li, Wenjie Wang, Jianyang Qin, Chaoyang Li, Lu Wang, Hao Chen, Qing Liao
MMDS-Bench is a new diagnostic benchmark for multimodal dynamic stance classification in social media parent‑reply interactions. It contains 3,482 multimodal instances annotated with a seven‑label stance taxonomy, plus an 800‑instance subset that demands structured reasoning over parent and reply understanding and stance‑relation inference. The benchmark also tags each instance with five challenge factors—multimodal fusion, parent framing, non‑literal expression, interaction reasoning, and label‑boundary ambiguity—and evaluates 12 multimodal large language models using a reference‑grounded LLM‑judge protocol, revealing that current models still struggle with relational inference beyond separate parent and reply comprehension.
By Yuzhe Ding, Kang He, Li Zheng, Shengwu Zheng, Teng Shi, Fei Li, Chong Teng, Donghong Ji
The paper introduces VFStance, a method that uses image generation to make implicit stance cues in news articles more explicit through visual framing. It targets article-level news stance detection, a task complicated by subtle, structurally complex texts. Experiments show VFStance outperforms existing methods, and a user study with 200 participants demonstrates that the visual framing makes stance signals more noticeable in a snippet-based news consumption setting.
By Dahyun Lee, Jiyoung Han, Kunwoo Park
arXiv:2604. 00878v2 Announce Type: replace-cross Abstract: Actor-level stance detection aims to determine an author expressed position toward specific geopolitical actors mentioned or implicated in a text.
By Abdullah Al Shafi, Md. Milon Islam, Sk. Imran Hossain, K. M. Azharul Hasan
The paper introduces Mawqif-XT, a new Arabic benchmark dataset comprising 996 manually annotated tweets from three public targets: Women Driving, E-Cars, and Trimester System. Each tweet is labeled for stance, sentiment, and sarcasm following the Mawqif annotation scheme, and the dataset is intended as a held‑out evaluation set to test cross‑target generalization. Baseline results are provided using Arabic and multilingual transformer models as well as zero‑shot large language models, enabling reproducible evaluation alongside the original Mawqif dataset.
By Rasha Albalawi, Nuha Albadi, Hamzah Luqman, Maram Kurdi, Saad Ezzini, Asma Yamani, Ahmed Ashraf
MoPrO‑SD is a stance‑specific prompt optimization framework that breaks prompts into semantic modules and refines them sequentially with LLM feedback. It evaluates three refinement strategies—instruction‑only, instruction with reasoning, and instruction with per‑example reasoning—using class‑conditioned errors to improve stance detection. The authors also release VaccineStance, a new human‑annotated benchmark for vaccine stance detection, and demonstrate that MoPrO‑SD outperforms human‑written base prompts across multiple datasets and open‑weight LLMs.
By Meysam Sabbaghan, Nikesh Gyawali, Arman Zareian Jahromi, Sebastian Loera-Lozano, Doina Caragea, Cornelia Caragea
We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning.
arXiv:2607. 04690v1 Announce Type: cross Abstract: We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026.
By Md. Shakhoyat Rahman Shujon, MD Jahid Hasan Jim, Md. Milon Islam, Md Rezwanul Haque, Fakhri Karray
arXiv:2610.25463v2 Announce Type: replace
Abstract: How well can transformer-based Large Language Models (LLMs) retain and recall memories in long contexts of dialogue? LLMs are known to forget long-...
By Owen Bianchi, Samer Aslan, Ryunosuke Saito
Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.
arXiv:2607. 14957v1 Announce Type: new Abstract: Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage.
By Besim Shala, Peter Mandl, Andreas Humpe, Martin H\"ausl