activity
20232026
most citedLLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics

3 citations · 3 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL2026

Sumi: Open Uniform Diffusion Language Model from Scratch

Mengyu Ye, Keito Kudo, Wataru Ikeda +3

Diffusion models have become a promising alternative to autoregressive models. Among these, uniform diffusion language models (UDLMs) permit any token to be updated at any step, in…

cs.CV2026

SLVMEval: Synthetic Meta Evaluation Benchmark for Text-to-Long Video Generation

Ryosuke Matsuda, Keito Kudo, Haruto Yoshida +2

This paper proposes the synthetic long-video meta-evaluation (SLVMEval), a benchmark for meta-evaluating text-to-video (T2V) evaluation systems. The proposed SLVMEval benchmark foc…

cs.CL2026

Nodes Are Early, Edges Are Late: Probing Diagram Representations in Large Vision-Language Models

Haruto Yoshida, Keito Kudo, Yoichi Aoki +4

Large vision-language models (LVLMs) demonstrate strong performance on diagram understanding benchmarks, yet they still struggle with understanding relationships between elements,…

cs.CL2026

Reconsidering Positional Supervision in Masked Diffusion Language Model Training

Mengyu Ye, Keito Kudo, Ryosuke Takahashi +1

Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive language models. They can be view…

cs.CL2025

Weight-based Analysis of Detokenization in Language Models: Understanding the First Stage of Inference Without Inference

Go Kamoda, Benjamin Heinzerling, Tatsuro Inaba +3

According to the stages-of-inference hypothesis, early layers of language models map their subword-tokenized input, which does not necessarily correspond to a linguistically meanin…

cs.CL2024★ 3 cited

LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics

Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +5

This study investigates the internal information flow of large language models (LLMs) while performing chain-of-thought (CoT) style reasoning. Specifically, with a particular inter…

cs.CL2024

First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning

Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi +4

Multi-step reasoning instruction, such as chain-of-thought prompting, is widely adopted to explore better language models (LMs) performance. We report on the systematic strategy th…

cs.CL2024

ACORN: Aspect-wise Commonsense Reasoning Explanation Evaluation

Ana Brassard, Benjamin Heinzerling, Keito Kudo +2

Evaluating the quality of free-text explanations is a multifaceted, subjective, and labor-intensive task. Large language models (LLMs) present an appealing alternative due to their…

cs.CL2023

A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video

Keito Kudo, Haruki Nagasawa, Jun Suzuki +1

This paper proposes a practical multimodal video summarization task setting and a dataset to train and evaluate the task. The target task involves summarizing a given video into a…

cs.AI2023

Empirical Investigation of Neural Symbolic Reasoning Strategies

Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi +4

Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the bene…

cs.CL2023

Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?

Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +4

Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks.…