6 papers
Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Novelty
Tim Schopf, Tobias Schreieder, Akiko Aizawa
Automated novelty judgment can accelerate scientific discovery by enabling efficient evaluation, refinement, and comparison of research ideas. While large language models are incre…
Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning
Chuang Ma, Qianying Liu, Tomoyuki Obuchi +6
Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly attended visual information. We i…
Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification
Sunisth Kumar, Xanh Ho, Tim Schopf +3
Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by its evidence. Prior work ha…
Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks
Yihua Zhu, Qianying Liu, Fei Cheng +4
Reinforcement learning with verifiable rewards (RLVR) has become central to post-training reasoning models, yet a key limitation of existing studies is their narrow view of the rea…
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs
Yihua Zhu, Qianying Liu, Jiaxin Wang +5
Autoregressive LLMs perform well on relational tasks that require linking entities via relational words (e.g., father/son, friend), but it is unclear whether they learn the logical…
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering
Yihua Zhu, Qianying Liu, Akiko Aizawa +1
Knowledge Base Question Answering (KBQA) aims to answer natural language questions using structured knowledge from KBs. While LLM-only approaches offer generalization, they suffer…