activity
20202026
most citedOpen X-Embodiment: Robotic Learning Datasets and RT-X Models

103 citations · 187 across the 25 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.CV2025

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…

cs.CV2025

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…

cs.CL2025

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…

cs.AI2025

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…

cs.CL2025★ 1 cited

Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks

Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim +5

Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tas…

cs.CL2025

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words

Gouki Minegishi, Hiroki Furuta, Yusuke Iwasawa +1

Sparse autoencoders (SAEs) have gained a lot of attention as a promising tool to improve the interpretability of large language models (LLMs) by mapping the complex superposition o…

cs.CV2025

Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search

Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo +1

The remarkable progress in text-to-video diffusion models enables the generation of photorealistic videos, although the content of these generated videos often includes unnatural m…