Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Computation and Language

Oct 1

Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears

arXiv:2504.05008v3 Announce Type: replace Abstract: The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their...

By Anastasiia Ivanova, Natalia Fedorova, Ekaterina Artemova

llms

arXiv Computation and Language

Oct 1

Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response

arXiv:2601.19918v2 Announce Type: replace Abstract: Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in...

By Yitong Qiao, Licheng Pan, Yu Mi, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu, Fei Sun, Zhixuan Chu

llms benchmarks safety

arXiv Computation and Language

Oct 1

DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

arXiv:2603.26164v2 Announce Type: replace-cross Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters...

By Hao Liang, Zhengyang Zhao, Mingrui Chen, Meiyi Qiang, Lu Ma, Rongyi Yu, Hengyi Feng, Shixuan Sun, Zimo Meng, Xiaochen Ma, Xuanlin Yang, Qifeng Cai, Ruichuan An, Bohan Zeng, Zhen Hao Wong, Chengyu Shen, Runming He, Zhaoyang Han, Yaowei Zheng, Fangcheng Fu, Conghui He, Bin Cui, Zhiyu Li, Weinan E, Wentao Zhang

llms rag benchmarks

arXiv Computer Vision

Oct 1

TrackFish3D: Self-Supervised 3D Tracking of Schooling Fish from Multi-view Videos

arXiv:2609.38347v1 Announce Type: new Abstract: Quantifying collective fish behavior requires accurate trajectories, yet multi-view 3D tracking remains challenging due to frequent occlusions, visuall...

By Patt Phurtivilai, Zhiyang Dou, Yifan Wu, Kinfung Chu, Yuan Liu, Lei Yang, Wenping Wang, Taku Komura

llms rag benchmarks

arXiv Computer Vision

Oct 1

PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos

arXiv:2609.38377v1 Announce Type: new Abstract: Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language mode...

By Max Ku, Jiaojiao Fan, Zekun Hao, Francesco Ferroni, Heng Wang, Wenhu Chen, Ming-Yu Liu, Prithvijit Chattopadhyay

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 1

Team MSU GenText-Forensics Challenge 2026 Technical Report

arXiv:2609.38391v1 Announce Type: new Abstract: Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meani...

By Kirill Koltsov, Aleksandr Gushchin, Dmitriy Vatolin, Anastasia Antsiferova

llms computer-vision robotics fine-tuning efficiency multimodal

arXiv Computer Vision

Oct 1

MOBA-VL: Event-Localized Multi-Turn Reinforcement Learning for Real-Time MOBA Commentary

arXiv:2609.38428v1 Announce Type: new Abstract: Real-time commentary for Multiplayer Online Battle Arena (MOBA) esports requires a vision-language model (VLM) to narrate a live match second by second...

By Shengyun Zhong, Xinkang Zhao, Ziyuan Chu, Linchao Zhu

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Oct 1

Multi-LLM Collaborative Alignment via Stackelberg Games

The paper introduces Stackelberg Alignment, a leader‑follower framework that lets a pool of language models collaborate and improve by learning from each other’s responses. An EXP3 bandit leader adaptively selects instructions based on difficulty and discriminability, while the models act as followers, evaluating peers and learning via DPO or GRPO with Elo‑style reputation weighting and opponent matching. Experiments on diverse benchmarks show that this adaptive curriculum outperforms static baselines by up to 12‑25% and improves multi‑LLM evolution.

By Christina Hahn, Shangbin Feng, Dean Light, Swastik Roy, Hila Gonen, Yulia Tsvetkov

llms reinforcement-learning benchmarks safety

arXiv Computer Vision

Oct 1

From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning

arXiv:2609.38924v1 Announce Type: new Abstract: Major adverse cardiovascular events (MACE) remain the leading cause of mortality worldwide. Opportunistic screening using routinely acquired clinical d...

By Jialu Pi, Yanan Ma, Weijie Chen, Owen Crystal, Shubham Trivedi, Stephen Xie, Anna Silverman, Matthew Stib, Chadi Ayoub, Reza Arsanjani, Imon Banerjee

llms reinforcement-learning efficiency multimodal benchmarks

arXiv Computer Vision

Oct 1

MEMO: Multi-Level Entity-Aware Memory for Streaming Video Understanding

arXiv:2609.38900v1 Announce Type: new Abstract: Streaming video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons,...

By Yinying Li, Yuqian Fu, Yulin Dai, Jingyu Gong, Tianwen Qian, Xiaoling Wang

llms multimodal benchmarks

arXiv Computer Vision

Oct 1

PARK: Accurate Block Retrieval for Sparse Attention in Video Diffusion Transformers

arXiv:2609.38978v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have become a dominant architecture for video generation, but their efficiency is limited by the quadratic complexity of...

By Yun Dai, Jiarui Wen, Huiping Zhuang, Cen Chen, Ziqian Zeng

llms diffusion

arXiv Computer Vision

Oct 1

Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection

arXiv:2609.39066v1 Announce Type: new Abstract: Conventional image forgery detection methods produce binary scores or pixel-level masks without interpretable evidence, while recent multimodal large l...

By Zhiya Tan, Jing Huang, Changtao Miao, Lin Tan, Xin Zhang, Weiwei Feng, Jianshu Li, Joey Tianyi Zhou

llms agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 1

Feature-Aware Token Attack for Compression-Triggered Stealthy Failures in Large Vision-Language Models

arXiv:2609.39134v1 Announce Type: new Abstract: Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study a...

By Shilinlu Yan, Bowen Chen, Yuechen Zhang, Zhenhong Zhou, Li Sun, Sen Su

llms multimodal safety

arXiv Computer Vision

Oct 1

Asking the World: Generalist Physical Reasoning through Agentic World Modeling and Probing

arXiv:2609.39135v1 Announce Type: new Abstract: Physical reasoning from video requires inferring latent physical properties and dynamics beyond direct observation. Direct VLM inference remains unreli...

By Shenxiang Zeng, Chen Yang, Peiyao Chen, Guohui Zhang, Jiansheng Fan, Chen Wang

llms agents multimodal

arXiv Computer Vision

Oct 1

When Can Text Replace Vision? Structural Bottlenecks in Diagram Reasoning

arXiv:2609.39142v1 Announce Type: new Abstract: Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information th...

By Yunbei Zhang, Janet Wang, Jihun Hamm, Chandan K Reddy

llms multimodal

arXiv Computer Vision

Oct 1

Uruqi: Learning Spatial Cognition from Visual Experience

arXiv:2609.39195v1 Announce Type: new Abstract: Spatial intelligence requires maintaining a coherent understanding of the world as the embodied agent moves. Like humans, the agent must use its own mo...

By Shichao Li, Meiqi Wang, Fei Su, Zhicheng Zhao

llms agents robotics multimodal benchmarks

arXiv Computer Vision

Oct 1

Comparative study of adapting pre-trained models for driving behavior video captioning

arXiv:2609.39542v1 Announce Type: new Abstract: This report examines and compares some of the many fine tuning and prompting methods existing, applying them within the domain of autonomous driving. T...

By Sayak Mallick, Philipp Geiger, Augustin Kelava

llms agents robotics fine-tuning

arXiv Computer Vision

Oct 1

From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning

arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...

By Minye Shao, Chaohui Yu, Yixuan Wu, Fan Wang, Ling Shao, Yang Long

llms agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Computer Vision

Oct 1

SAGE: Salient Factor Discovery and Generation with Visual Foundation Representations

arXiv:2609.39635v1 Announce Type: new Abstract: Given a target dataset, such as faces with eyeglasses, and a background dataset, such as faces without, contrastive analysis separates \textit{salient}...

By Shuang Liang, Lejun Liao, Shiyuan Zhang, Max C. Zhang, Xiaolong Luo, Han Wang, Stefano Anzellotti, Yuan Yuan

llms diffusion

arXiv Computer Vision

Oct 1

Typographic Attack Against VLM-based AI-generated Image Detection

arXiv:2609.39662v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used for AI-generated image (AIGI) detection, providing natural-language explanations for authenticity j...

By Eunmin Lee, Jungwoo Kim, Jong-Seok Lee

llms multimodal

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