5 papers
HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
Yucheng Wu, Jundong Xu, Mingzhen Ju +4
Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction over fixed predicates. This misses…
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervision
Jundong Xu, Hao Fei, Huichi Zhou +6
Although LLMs exhibit strong reasoning capabilities, existing training methods largely depend on outcome-based feedback, which can produce correct answers with flawed reasoning. Pr…
MuSLR: Multimodal Symbolic Logical Reasoning
Jundong Xu, Hao Fei, Yuhui Zhang +8
Multimodal symbolic logical reasoning, which aims to deduce new facts from multimodal input via formal logic, is critical in high-stakes applications such as autonomous driving and…
Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework
Jundong Xu, Hao Fei, Meng Luo +6
In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks. However, when it comes to logical reason…