4 citations · 12 across the 20 of their papers we have counts for
21 papers
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
Atsuyuki Miyai, Kiyoharu Aizawa, Toshihiko Yamasaki
We present a novel approach to efficient LLM harness optimization through adaptive validation task selection. Harness optimization iteratively rewrites the harness code based on va…
360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents
Kenta Watanabe, Atsuyuki Miyai, Mizuki Takenawa +2
We present 360CityArena, a benchmark for evaluating the urban exploration capabilities of embodied agents within a photorealistic environment constructed from 360-degree videos. Ex…
Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding
Jeonghun Baek, Atsuyuki Miyai, Shota Onohara +2
Manga is a culturally distinctive multimodal medium and one of the most influential forms of Japanese popular culture. As AI systems increasingly target manga understanding, OCR, a…
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers
Atsuyuki Miyai, Mashiro Toyooka, Zaiying Zhao +3
This paper introduces the first systematic evaluation framework for quantifying the quality and risks of papers written by modern coding agents. While AI-driven paper writing has b…
JMMMU-Pro: Image-based Japanese Multi-discipline Multimodal Understanding Benchmark via Vibe Benchmark Construction
Atsuyuki Miyai, Shota Onohara, Jeonghun Baek +1
This paper introduces JMMMU-Pro, an image-based Japanese Multi-discipline Multimodal Understanding Benchmark, and Vibe Benchmark Construction, a scalable construction method. Follo…
Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration from a Baseline Paper
Atsuyuki Miyai, Mashiro Toyooka, Takashi Otonari +2
Understanding the current capabilities and risks of AI Scientist systems (autoresearch) is essential for ensuring trustworthy and sustainable AI-driven scientific progress while pr…
PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning
Tatsuki Kawakami, Kazuki Egashira, Atsuyuki Miyai +2
In recent years, unlearning techniques, which are methods for inducing a model to "forget" previously learned information, have attracted attention as a way to address privacy and…
WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Atsuyuki Miyai, Zaiying Zhao, Kazuki Egashira +9
Powered by large language models (LLMs), web browsing agents operate graphical user interfaces in a human-like manner, offering a transparent and general framework for automating w…
MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding
Jeonghun Baek, Kazuki Egashira, Shota Onohara +4
Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives…
Harnessing PDF Data for Improving Japanese Large Multimodal Models
Jeonghun Baek, Akiko Aizawa, Kiyoharu Aizawa
Large Multimodal Models (LMMs) have demonstrated strong performance in English, but their effectiveness in Japanese remains limited due to the lack of high-quality training data. C…
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models
Shiho Noda, Atsuyuki Miyai, Qing Yu +2
Out-of-distribution (OOD) detection is a task that detects OOD samples during inference to ensure the safety of deployed models. However, conventional benchmarks have reached perfo…
JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
Shota Onohara, Atsuyuki Miyai, Yuki Imajuku +5
Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations. In this paper, we introduce J…
Training-Free Sketch-Guided Diffusion with Latent Optimization
Sandra Zhang Ding, Jiafeng Mao, Kiyoharu Aizawa
Based on recent advanced diffusion models, Text-to-image (T2I) generation models have demonstrated their capabilities to generate diverse and high-quality images. However, leveragi…
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +10
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other probl…
Zero-Shot Character Identification and Speaker Prediction in Comics via Iterative Multimodal Fusion
Yingxuan Li, Ryota Hinami, Kiyoharu Aizawa +1
Recognizing characters and predicting speakers of dialogue are critical for comic processing tasks, such as voice generation or translation. However, because characters vary by com…
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +7
This paper introduces a novel task to evaluate the robust understanding capability of Large Multimodal Models (LMMs), termed . Multiple…
Cross-Lingual Learning in Multilingual Scene Text Recognition
Jeonghun Baek, Yusuke Matsui, Kiyoharu Aizawa
In this paper, we investigate cross-lingual learning (CLL) for multilingual scene text recognition (STR). CLL transfers knowledge from one language to another. We aim to find the c…
The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization
Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa
Text-to-image diffusion models allow users control over the content of generated images. Still, text-to-image generation occasionally leads to generation failure requiring users to…
Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation
Daichi Horita, Naoto Inoue, Kotaro Kikuchi +2
Content-aware graphic layout generation aims to automatically arrange visual elements along with a given content, such as an e-commerce product image. In this paper, we argue that…
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
Atsuyuki Miyai, Qing Yu, Go Irie +1
Out-of-distribution (OOD) detection is critical for safety-sensitive machine learning applications and has been extensively studied, yielding a plethora of methods developed in the…
A Structure-Guided Diffusion Model for Large-Hole Image Completion
Daichi Horita, Jiaolong Yang, Dong Chen +3
Image completion techniques have made significant progress in filling missing regions (i.e., holes) in images. However, large-hole completion remains challenging due to limited str…