1 citations · 1 across the 8 of their papers we have counts for
9 papers
Rethinking Circuit Evaluation: Do Circuits Explain Model Errors?
Li Zhang, Chuqin Geng, Mark Zhang +4
Mechanistic interpretability (MI) aims to explain a model's behaviour through analyzing its internal computations; circuit-based explanations aim to isolate these computations with…
OmniRouting: A Semantic-Coupled Multimodal Benchmark for Constraint-Aware Spatial Reasoning in PCB Routing
Taiting Lu, Kaiyuan Lin, Ziwei Dong +18
Recent large language models (LLMs) have demonstrated remarkable progress in constraint-aware navigation, maze reasoning, and graph reasoning. However, their ability to reason abou…
OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout
Taiting Lu, Kaiyuan Lin, Mingjia Wang +12
Recent large language models (LLMs) have demonstrated remarkable progress in 3D spatial reasoning, spatial grounding, and fine-grained geometric understanding. However, their abili…
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling
Chuqin Geng, Li Zhang, Mark Zhang +4
While deep generative models excel at capturing graph data distributions, they struggle to satisfy complex, hard constraints. In unconstrained settings, these models typically prod…
Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng, Li Zhang, Haolin Ye +5
Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While…
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
Chuqin Geng, Ziyu Zhao, Zhaoyue Wang +3
Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in an intermediate, uninterpretable concept…
VisionLogic: Discovering and Grounding Decision-Relevant Visual Concepts
Chuqin Geng, Yuhe Jiang, Ziyu Zhao +7
Concept-based explanations help users understand vision models through recognizable visual patterns. However, existing methods often rely on correlational signals without directly…
SAT-DIFF: A Tree Diffing Framework Using SAT Solving
Chuqin Geng, Haolin Ye, Yihan Zhang +2
Computing differences between tree-structured data is a critical but challenging problem in software analysis. In this paper, we propose a novel tree diffing approach called SatDif…
Learning Minimal Neural Specifications
Chuqin Geng, Zhaoyue Wang, Haolin Ye +1
Formal verification is only as good as the specification of a system, which is also true for neural network verification. Existing specifications follow the paradigm of data as spe…