1 citations · 2 across the 11 of their papers we have counts for
13 papers
Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability
Chuqin Geng, Li Zhang, Haolin Ye +3
Mechanistic interpretability aims to recover the internal computations responsible for model behavior. Progress in automated circuit discovery is often framed as a search problem:…
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…
Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
Sheza Munir, Ahanaf Rodoshi, Sumin Lee +3
Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, trea…
DreamProver: Evolving Transferable Lemma Libraries via a Wake-Sleep Theorem-Proving Agent
Youyuan Zhang, Jialiang Sun, Hangrui Bi +4
We introduce DreamProver, an agentic framework that leverages a "wake-sleep" program induction paradigm to discover reusable lemmas for formal theorem proving. Existing approaches…
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…
NEUROLOGIC: From Neural Representations to Interpretable Logic Rules
Chuqin Geng, Anqi Xing, Li Zhang +3
Rule-based explanation methods offer rigorous and globally interpretable insights into neural network behavior. However, existing approaches are mostly limited to small fully conne…
Modernizing SMT-Based Type Error Localization
Max Kopinsky, Brigitte Pientka, Xujie Si
Traditional implementations of strongly-typed functional programming languages often miss the root cause of type errors. As a consequence, type error messages are often misleading…
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…
TorchProbe: Fuzzing Dynamic Deep Learning Compilers
Qidong Su, Chuqin Geng, Gennady Pekhimenko +1
Static and dynamic computational graphs represent two distinct approaches to constructing deep learning frameworks. The former prioritizes compiler-based optimizations, while the l…