Vision & Multimodal

Amin Bigdeli, Radin Hamidi Rad, Hai Son Le et al.

Large Language Models (LLMs) are now widely used for query reformulation and expansion in Information Retrieval, with many studies reporting substantial effectiveness gains. However, these results are…

Bowen Sun, Chaozhuo Li, Yaodong Yang et al.

Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a sequence of individually benign queries that collecti…

Thibault Bañeras-Roux, Mickaël Rouvier, Jane Wottawa et al.

Evaluating automatic speech recognition (ASR) systems is a classical but difficult and still open problem, which often boils down to focusing only on the word error rate (WER). However, this metric su…

Sudong Wang, Weiquan Huang, Xiaomin Yu et al.

The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). H…

Ce Chen, Yi Ren, Yuanming Li et al.

Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently yielding corrupted video shots. We address this f…

Zihao Li, Jiaru Zou, Feihao Fang et al.

Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability to many real-world p…

Junan Hu, Jian Liu, Jingxiang Lai et al.

Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces visually. Yet supervised fine-tuning alone canno…

Shipeng Liu, Liang Zhao, Dengfeng Chen et al.

Tunnel inspection requires outputs that can support defect localization, measurement, severity grading, and engineering documentation. Existing training-free foundation-model pipelines usually stop at…

Tao Ge, Baolin Peng, Hao Cheng et al.

Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and organized through directory structures and content…

Guang Yang, Xing Hu, Xiang Chen et al.

Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots into Python scripts. A circuit diagram can be view…

Sihong Wu, Owen Jiang, Yilun Zhao et al.

Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances in large language models (LLMs) have motivated me…

Alex Petrov, Alexander Gusak, Denis Mukha et al.

Persistent AI memory is often reduced to a retrieval problem: store prior interactions as text, embed them, and ask the model to recover relevant context later. This design is useful for thematic reca…

Kenneth J. K. Ong

As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence their behavior. This paper investigates the effe…

Lauren Cadwallader, Iain Hrynaszkiewicz, parth sarin et al.

Numerous metascience studies and other initiatives have begun to monitor the prevalence of open science practices when it is more important to understand the 'downstream' effects or impacts of open sc…

Guy Lutsker, Gal Sapir, Jordi Merino et al.

Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central challenge in medicine. Here we present HealthFormer, a decoder-only tra…

Sidi Chang, Peiying Zhu, Yuxiao Chen et al.

As LLMs become credible readers of earnings calls, investor-relations Q\&A, guidance, and disclosure language, supervised financial NLP benchmarks increasingly function as decision evidence for model …

Lifan Zheng, Xue Yang, Jiawei Chen et al.

With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel paradigm in Personality Editing, most existing m…

Happy Syahrul Ramadhan, Ahmad Sahidin Akbar, Karin Yehezkiel Sinaga et al.

This study analyzes Indonesian student opinions on the adoption of artificial intelligence in higher education using two approaches: TF-IDF-based machine learning and Transformer-based deep learning. …

Dorottya Demszky, Edith Bouton, Alison Twiner et al.

Research on classroom interaction has long been divided between large-scale observation and in-depth ethnographic work. We propose a framework mapping this methodological space along three dimensions-…

Jon-Paul Cacioli

We adapted the Reliable Change Index (RCI; Jacobson and Truax, 1991) from clinical psychology to item-level LLM version comparison on 2,000 MMLU-Pro items (K=10 samples at T=0.7). Two within-family pa…

Neemias B da Silva, Rodrigo Minetto, Daniel Silver et al.

Large Language Models (LLMs) are increasingly used as proxies for human perception in urban analysis, yet it remains unclear whether persona prompting produces meaningful and reproducible behavioral d…

Junbo Cui, Bokai Xu, Chongyi Wang et al.

Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming interaction, yet they still remain far from human-lev…

Samuel Kiegeland, Vésteinn Snæbjarnarson, Tim Vieira et al.

Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a unit underspecified. In practice, experimental stim…

Wei Cheng, Yongchang Cao, Chen Shen et al.

Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges for interactive codin…

Ganesh Bagler, Gopal Krishna Tewari, Aditya Raj Yadav et al.

Cooking is a cultural expression of human creativity that transcends geography and time through the orchestration of ingredients and techniques, much like languages do through words and syntax. Yet, b…

Keito Inoshita, Xiaokang Zhou, Akira Kawai et al.

Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments into a single gold standard, discarding the distr…

Jiasheng Zheng, Xin Zheng, Boxi Cao et al.

Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and evaluation. However, exi…

Alejandro R. Jadad

What shapes a consequential decision when human and artificial intelligence work on it together? The answer is becoming harder to see. A decision may look human-led after AI has set the frame, or appe…

Eyon Jang, Damon Falck, Joschka Braun et al.

Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignment. Successful RL relies on sufficient exploration …

Thibault Bañeras Roux, Jane Wottawa, Mickael Rouvier et al.

Conventionally, Automatic Speech Recognition (ASR) systems are evaluated on their ability to correctly recognize each word contained in a speech signal. In this context, the word error rate (WER) metr…