3 papers
cs.CR2026
RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
Tarek Elsayed, Shiping Yang, Eunsong Koh +13
LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary cla…
cs.AI2025
Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants
Vincent Huang, Dami Choi, Daniel D. Johnson +2
Interpreting the internal activations of neural networks can produce more faithful explanations of their behavior, but is difficult due to the complex structure of activation space…
cs.CL2025
Training Language Models to Explain Their Own Computations
Belinda Z. Li, Zifan Carl Guo, Vincent Huang +2
Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs'…