103 citations · 187 across the 25 of their papers we have counts for
31 papers
BCT: Bayes-Consistent Tempering for Flow Language Models
Daisuke Oba, Hiroki Furuta, Naoaki Okazaki
Temperature scaling is typically applied as a post-hoc transformation of model probabilities. In posterior-induced flow language models, this creates two mismatches. First, posteri…
Matching Object or Relation? Tracing Abstract Reasoning Inside VLMs
Gouki Minegishi, Hiroki Furuta, Takeshi Kojima +2
Vision Language Models (VLMs) excel on visual benchmarks but fail systematically on tasks requiring abstract reasoning. Existing benchmarks document this failure but cannot say \em…
VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation
Yuta Oshima, Masakazu Yoshimura, Masahiro Suzuki +2
Recent multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not only by text but also by visual…
AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation
Yuta Oshima, Ku Onoda, Yusuke Iwasawa +3
Recent image generation models can take multiple reference images as input and combine them into a new image. However, multi-reference image generation remains challenging: models…
Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
Ku Onoda, Paavo Parmas, Hiroki Furuta +4
Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset of visually distinct modes. Th…
Drifting Objectives for Refining Discrete Diffusion Language Models
Daisuke Oba, Hiroki Furuta, Naoaki Okazaki
Discrete diffusion language models (DDLMs) generate text by iteratively denoising categorical token sequences, while recent drifting methods for continuous generators suggest that…
Understanding Emergent Misalignment via Feature Superposition Geometry
Gouki Minegishi, Hiroki Furuta, Takeshi Kojima +2
Emergent misalignment, where fine-tuning on narrow, non-harmful tasks induces harmful behaviors, poses a key challenge for AI safety in LLMs. Despite growing empirical evidence, it…
Diffusion-State Policy Optimization for Masked Diffusion Language Models
Daisuke Oba, Hiroki Furuta, Naoaki Okazaki
Masked diffusion language models generate text through iterative masked-token filling, but terminal-only rewards on final completions provide coarse credit assignment for the inter…
Emergent Analogical Reasoning in Transformers
Gouki Minegishi, Jingyuan Feng, Hiroki Furuta +3
Analogy is a central faculty of human intelligence, enabling abstract patterns discovered in one domain to be applied to another. Despite its central role in cognition, the mechani…
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…
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…
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…
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…
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…
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…
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback
Hiroki Furuta, Heiga Zen, Dale Schuurmans +4
Large text-to-video models hold immense potential for a wide range of downstream applications. However, they struggle to accurately depict dynamic object interactions, often result…
Geometric-Averaged Preference Optimization for Soft Preference Labels
Hiroki Furuta, Kuang-Huei Lee, Shixiang Shane Gu +4
Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic. However, human preferences can vary across individuals, and the…
Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials
Hiroki Furuta, Gouki Minegishi, Yusuke Iwasawa +1
Grokking has been actively explored to reveal the mystery of delayed generalization and identifying interpretable representations and algorithms inside the grokked models is a sugg…
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta +2
Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address these limitations, we pr…
Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
Hiroki Furuta, Yutaka Matsuo, Aleksandra Faust +1
Language model agents (LMA) recently emerged as a promising paradigm on muti-step decision making tasks, often outperforming humans and other reinforcement learning agents. Despite…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
Izzeddin Gur, Hiroki Furuta, Austin Huang +4
Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world web…
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
Hiroki Furuta, Kuang-Huei Lee, Ofir Nachum +4
The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model d…