4 papers
Are Your Agents Upward Deceivers?
Dadi Guo, Qingyu Liu, Dongrui Liu +13
Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users. This raises the question of whether they may also engage in…
MATP-BENCH: Can MLLM Be a Good Automated Theorem Prover for Multimodal Problems?
Zhitao He, Zongwei Lyu, Dazhong Chen +2
Numerous theorems, such as those in geometry, are often presented in multimodal forms (e.g., diagrams). Humans benefit from visual reasoning in such settings, using diagrams to gai…
Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models
Dadi Guo, Jiayu Liu, Zhiyuan Fan +5
Large reasoning models (e.g., R1, o3) have demonstrated remarkable mathematical problem-solving abilities. However, the high reported accuracy of these advanced models on popular d…
AIDBench: A benchmark for evaluating the authorship identification capability of large language models
Zichen Wen, Dadi Guo, Huishuai Zhang
As large language models (LLMs) rapidly advance and integrate into daily life, the privacy risks they pose are attracting increasing attention. We focus on a specific privacy risk…