Author

Akiko Aizawa

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Preprint Aug 2026

Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Novelty

Automated novelty judgment can accelerate scientific discovery by enabling efficient evaluation, refinement, and comparison of research ideas. While large language models are increasingly adopted for this task, we investigate a previously overlooked limitation in their judgment capabilities: despite generating reasonin...

Tim Schopf, Tobias Schreieder, Akiko Aizawa · 0 citations

#artificial intelligence May 2026

Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks

Three findings emerge: joint depth-complexity coverage outperforms single-axis recipes; reasoning families respond non-uniformly, with abductive reasoning degrading outside the RL-covered region and task correlations clustering into deductive-abductive and inductive-analogy pairs; and uniform mixing outperforms staged...

Yi-Hua Zhu, Qian-Ying Liu, Fei Cheng et al. · arXiv.org · 0 citations

#computer vision Jun 2026

Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning

It is shown that a lightweight LLM-only DPO update on tiny single-object-pair synthetic data mitigates the bias, lifting four-way robust accuracy by up to 100 points on synthetic data, and by 68.1 points on broader evaluation datasets WhatsUp, SpatialMQA-Direct, and VSR.

Chuan Ma, Qianying Liu, Tomoyuki Obuchi et al. · arXiv.org · 0 citations