1.1k citations · 1.3k across the 94 of their papers we have counts for
29 papers · 1 filter
WorldPack: Dynamic Frame Compression for Long-context Video World Modeling
Yuta Oshima, Yusuke Iwasawa, Masahiro Suzuki +2
Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on past observations and navigation action…
MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation
Yuta Oshima, Daiki Miyake, Kohsei Matsutani +4
Recent text-to-image generation models have acquired the ability of multi-reference generation and editing; that is, to inherit the appearance of subjects from multiple reference i…
Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
Ru Wang, Wei Huang, Qi Cao +3
Test-time reinforcement learning (TTRL) offers a label-free paradigm for adapting models using only synthetic signals at inference, but its success hinges on constructing reliable…
Automated Refinement of Essay Scoring Rubrics for Language Models via Reflect-and-Revise
Keno Harada, Lui Yoshida, Takeshi Kojima +2
The performance of Large Language Models (LLMs) is highly sensitive to the prompts they are given. Drawing inspiration from the field of prompt optimization, this study investigate…
AQA-TTRL: Self-Adaptation in Audio Question Answering with Test-Time Reinforcement Learning
Haoyu Zhang, Jiaxian Guo, Dong Yang +2
Large Audio Language Models (LALMs) exhibit strong capabilities in general audio understanding but remain static after deployment, limiting their adaptability to real-world data. S…
AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation
Ryosuke Takanami, Petr Khrapchenkov, Shu Morikuni +32
As robots transition from controlled settings to unstructured human environments, building generalist agents that can reliably follow natural language instructions remains a centra…
Leave No Observation Behind: Real-time Correction for VLA Action Chunks
Kohei Sendai, Maxime Alvarez, Tatsuya Matsushima +2
To improve efficiency and temporal coherence, Vision-Language-Action (VLA) models often predict action chunks; however, this action chunking harms reactivity under inference delay…
When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following
Keno Harada, Yudai Yamazaki, Masachika Taniguchi +4
As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To…
Unlocking Noise-Resistant Vision: Key Architectural Secrets for Robust Models
Bum Jun Kim, Makoto Kawano, Yusuke Iwasawa +1
While the robustness of vision models is often measured, their dependence on specific architectural design choices is rarely dissected. We investigate why certain vision architectu…
RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
Kohsei Matsutani, Shota Takashiro, Gouki Minegishi +3
Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on reasoning traces to improve the…
Enhancing Reference-based Sketch Colorization via Separating Reference Representations
Dingkun Yan, Xinrui Wang, Zhuoru Li +4
Reference-based sketch colorization methods have garnered significant attention for the potential application in animation and digital illustration production. However, most existi…
AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots
Xinjie Zhao, Moritz Blum, Fan Gao +10
AGENTiGraph is a user-friendly, agent-driven system that enables intuitive interaction and management of domain-specific data through the manipulation of knowledge graphs in natura…
Retrieve-Augmented Generation for Speeding up Diffusion Policy without Additional Training
Sodtavilan Odonchimed, Tatsuya Matsushima, Simon Holk +2
Diffusion Policies (DPs) have attracted attention for their ability to achieve significant accuracy improvements in various imitation learning tasks. However, DPs depend on Diffusi…
LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
Zhongchao Zhou, Yuxi Lu, Yaonan Zhu +5
With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level…
Dynamic Injection of Entity Knowledge into Dense Retrievers
Ikuya Yamada, Ryokan Ri, Takeshi Kojima +2
Dense retrievers often struggle with queries involving less-frequent entities due to their limited entity knowledge. We propose the Knowledgeable Passage Retriever (KPR), a BERT-ba…
SPARK: Graph-Based Online Semantic Integration System for Robot Task Planning
Mimo Shirasaka, Yuya Ikeda, Tatsuya Matsushima +2
The ability to update information acquired through various means online during task execution is crucial for a general-purpose service robot. This information includes geometric an…
Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence
Gouki Minegishi, Hiroki Furuta, Shohei Taniguchi +2
Transformer-based language models exhibit In-Context Learning (ICL), where predictions are made adaptively based on context. While prior work links induction heads to ICL through a…
CLIP-like Model as a Foundational Density Ratio Estimator
Fumiya Uchiyama, Rintaro Yanagi, Shohei Taniguchi +5
Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weighting, divergence estimation, an…
Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties
Gouki Minegishi, Hiroki Furuta, Takeshi Kojima +2
Recent large-scale reasoning models have achieved state-of-the-art performance on challenging mathematical benchmarks, yet the internal mechanisms underlying their success remain p…
A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics
Takeshi Kojima, Yaonan Zhu, Yusuke Iwasawa +8
Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to han…
Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar
Andrew Gambardella, Takeshi Kojima, Yusuke Iwasawa +1
Typical methods for evaluating the performance of language models evaluate their ability to answer questions accurately. These evaluation metrics are acceptable for determining the…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
ColorizeDiffusion v2: Enhancing Reference-based Sketch Colorization Through Separating Utilities
Dingkun Yan, Xinrui Wang, Yusuke Iwasawa +3
Reference-based sketch colorization methods have garnered significant attention due to their potential applications in the animation production industry. However, most existing met…
MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering
Feiyang Li, Yingjian Chen, Haoran Liu +10
Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced m…