9 papers
VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
Xiangbo Gao, Sicong Jiang, Bangya Liu +12
As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured footage to meet professional…
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Jiatong Li, Zheng Chen, Kai Liu +91
This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…
Zero-Shot Contextual Embeddings via Offline Synthetic Corpus Generation
Philip Lippmann, Jie Yang
Context-aware embedding methods boost retrieval accuracy by conditioning on corpus statistics (e.g., term co-occurrence and topical patterns) extracted from neighboring documents.…
Style over Substance: Distilled Language Models Reason Via Stylistic Replication
Philip Lippmann, Jie Yang
Specialized reasoning language models (RLMs) have demonstrated that scaling test-time computation through detailed reasoning traces significantly enhances performance. Although the…
Context-Informed Machine Translation of Manga using Multimodal Large Language Models
Philip Lippmann, Konrad Skublicki, Joshua Tanner +2
Due to the significant time and effort required for handcrafting translations, most manga never leave the domestic Japanese market. Automatic manga translation is a promising poten…
Positive Experience Reflection for Agents in Interactive Text Environments
Philip Lippmann, Matthijs T. J. Spaan, Jie Yang
Intelligent agents designed for interactive environments face significant challenges in text-based games, a domain that demands complex reasoning and adaptability. While agents bas…
Illuminating Blind Spots of Language Models with Targeted Agent-in-the-Loop Synthetic Data
Philip Lippmann, Matthijs T. J. Spaan, Jie Yang
Language models (LMs) have achieved impressive accuracy across a variety of tasks but remain vulnerable to high-confidence misclassifications, also referred to as unknown unknowns…
Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks
Aleksander Buszydlik, Karol Dobiczek, Michał Teodor Okoń +3
We consider the problem of red teaming LLMs on elementary calculations and algebraic tasks to evaluate how various prompting techniques affect the quality of outputs. We present a…