arXiv:2609.37568v1 Announce Type: new
Abstract: Audio-visual large language models (AVLLMs) have made remarkable progress in multimodal understanding and reasoning through interactions among visual,...
By Yu Zhang, Pingrui Zhang, Xuefeng Bai, Pengfei Zhang, Yang Xiang, Kehai Chen
arXiv:2609.37837v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly el...
By Haotian Deng, Wenbin Xing, Gang Xu, Tao He, Jinkai Zheng, Chun Li, Zheng Zhu, Ming Li
arXiv:2609.37883v1 Announce Type: new
Abstract: Pre-trained language models (PLMs) with encoder-based architectures have shown impressive capabilities in zero-shot cross-lingual transfer for various...
By Lalita Lowphansirikul, Attapol Rutherford, Jian Gang Ngui, Sarana Nutanong, Peerat Limkonchotiwat
arXiv:2609.38111v1 Announce Type: new
Abstract: Vision-language models (VLMs) may accept false visual premises, answering questions about a target object's color, count, location, or state even when...
By Hongzhu Guo, Mohsen Fayyaz, Nanyun Peng
arXiv:2609.38149v1 Announce Type: new
Abstract: Transformer language models (LMs) are feed-forward: deep-layer representations are never fed back to shallower layers, and the only pathway for informa...
By Dor Tirosh, Ido Amos, Mor Geva
arXiv:2609.35916v1 Announce Type: cross
Abstract: Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions fac...
By Jie Yang, Jiajun Chen, Jiazheng Zhou, Mianqiu Huang, Yining Zheng, Yuxin Wang, Xipeng Qiu
arXiv:2609.36290v1 Announce Type: cross
Abstract: We investigate the extent to which the Hamas attacks on Israel of October 7, 2023, have affected German social media debates about Judaism and Israel...
By Gregor Wiedemann, Daniel Wehrend
arXiv:2609.38099v1 Announce Type: cross
Abstract: Turning decoder-only large language models (LLMs) into strong dense retrievers typically requires some form of retriever training. In this paper, we...
By Nour Jedidi, Abdul Basit Ali, Hang Li, Jimmy Lin
arXiv:2609.38106v1 Announce Type: cross
Abstract: Speech-LLMs are expensive to run, making compression important for real-world deployment. However, compressed models are usually selected using aggre...
By Ganesh Pavan Kartikeya Bharadwaj Kolluri, Michael Kampouridis, Ravi Shekhar
arXiv:2609.38177v1 Announce Type: cross
Abstract: Reasoning about the 3D world from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While modern MLLMs...
By Jaewoo Jung, Hyeonseo Yu, Honggyu An, Jisang Han, Mungyeom Kim, Minkyeong Jeon, Heeseong Shin, Wonjun Moon, Federico Tombari, Daniel Barath, Marc Pollefeys, Seungryong Kim, Sunghwan Hong
arXiv:2509.21305v4 Announce Type: replace
Abstract: Large language models (LLMs) often exhibit sycophantic behaviors -- such as excessive agreement with or flattery of the user -- but it is unclear w...
By Daniel Vennemeyer, Phan Anh Duong, Tiffany Zhan, Tianyu Jiang
arXiv:2510.12041v3 Announce Type: replace
Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecifie...
By Ruibo Chen, Jiacheng Pan, Heng Huang, Zhenheng Yang
arXiv:2602.05385v2 Announce Type: replace
Abstract: Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-...
By Tao Liu, Jiafan Lu, Bohan Yu, Pengcheng Wu, Liu Haixin, Guoyu Xu, Li Xiangheng, Lixiao Li, Jiaming Hou, Zhao Shijun, Xinglin Lyu, Kunli Zhang, Yuxiang Jia, Hongyin Zan
arXiv:2602.11328v2 Announce Type: replace
Abstract: As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral dis...
By Amir Taubenfeld, Zorik Gekhman, Lior Nezry, Omri Feldman, Natalie Harris, Shashir Reddy, Romina Stella, Ariel Goldstein, Marian Croak, Yossi Matias, Amir Feder
arXiv:2603.17815v2 Announce Type: replace
Abstract: Understanding and evaluating multi-step reasoning in LLMs at the level of individual steps remains a key challenge. Process reward models (PRMs) pr...
By Corentin Royer (International Business Machines), Anna Hedstr\"om (ETH AI Center), Debarun Bhattacharjya (Lirio), Gaetano Rossiello (International Business Machines), Andrea Giovannini (International Business Machines), Mennatallah El-Assady (Department of Computer Science, ETH Zurich)
arXiv:2605.29637v2 Announce Type: replace
Abstract: Large language models often exhibit a substantial gap between their performance in English and in lower-resourced languages on equivalent knowledge...
By Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali, Aditya Joshi, Akshay Agarwal, Jasabanta Patro
arXiv:2606.29985v2 Announce Type: replace
Abstract: Diversity in LLM mathematical reasoning is critical for exploration, but common diversity metrics mostly capture surface-level variation rather tha...
By Sangmook Lee, Minbeom Kim, Jeonghye Kim, Dohyung Kim, Sojeong Rhee, Kyomin Jung
arXiv:2608.03275v2 Announce Type: replace
Abstract: Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve...
By Yiming Zeng, Lei Lu, Zexin Li, Zhuochun Li, Dehai Min, Shuoqiu Li, Shuyi Liao, Xidong Wu, Zeyu Zhang, Minmei Wang, Yu Zhao, Tingting Yu, Shangqian Gao
arXiv:2608.08477v5 Announce Type: replace
Abstract: We build VectraYX-Vision-1B, a sub-2B Spanish/LATAM cybersecurity vision-language model for offline use, and measure what limits its visual groundi...
By Juan S. Santillana
arXiv:2606.28249v2 Announce Type: replace-cross
Abstract: Recently, Large Language Model (LLM)-based Text-to-Speech (TTS) models have achieved remarkable naturalness. However, the standard Supervised...
By Sihang Nie, Xiaofen Xing, Rui Xing, Haoming Li, Ruitong Xiao, Jingyuan Xing, Baiji Liu, Xiangmin Xu