3 papers
cs.HC2026
Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systems
Mengli, Duan, Yuhe +3
Multimodal Large Language Models (MLLMs) are increasingly used to interpret visualizations, yet little is known about why they fail. We present the first systematic analysis of bar…
cs.SE2025
TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Honghua Dong, Jiacheng Yang, Xun Deng +4
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, the…
cs.LG2025
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…