About me

Since July 2024, I am pursuing a Ph.D. at the Secure, Reliable, and Intelligent Systems Lab at ETH Zürich, advised by Prof. Martin Vechev. My research focuses on enhancing the reliability and security of Large Language Models, with an emphasis software and code. Previously, I worked on the formal verification of data structures and algorithms and co-founded a start-up specializing in Smart Contracts.

If you are interested in conducting a research project, BSc Thesis or MSc Thesis at SRI, I am looking for motivated students with a strong background in programming languages and formal methods. Feel free to contact me about this via e-mail. I am open to new project ideas. Some suggestions around formal methods for Large Language Models include:

Further, I believe most people are unaware of what they do obviously wrong because no one dares or cares to tell them. So please roast me anonymously via admonymous.

Publications

2026

Coding Agents Don't Know When to Act
Thibaud Gloaguen, Niels Mündler, Mark Niklas Mueller, Veselin Raychev, Martin Vechev
COLM 2026
LeanLean: Benchmarking Repository-Scale Lean Proof Compression
Kári Rögnvaldsson, Niels Mündler-Sasahara, Jasper Dekoninck, Martin Vechev
2026
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
Niels Mündler-Sasahara*, Hristo Venev*, Dawn Song, Martin Vechev, Jingxuan He
arXiv 2026 * Equal contribution
AutoBaxBuilder: Bootstrapping Code Security Benchmarking
Tobias von Arx, Niels Mündler, Mark Vero, Maximilian Baader, Martin Vechev
ICML 2026
Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation
Yu-Du Feng*, Niels Mündler-Sasahara*, Mark Vero, Martin Vechev
DEMO @ ICML 2026 * Equal contribution Oral
CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
Alex Thillen, Niels Mündler, Veselin Raychev, Martin Vechev
ICML 2026
SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization
Hao Wang, Niels Mündler, Mark Vero, Jingxuan He, Dawn Song, Martin Vechev
arXiv 2026
Constrained Decoding of Diffusion LLMs with Context-Free Grammars
Niels Mündler, Jasper Dekoninck, Martin Vechev
ICLR 2026 DL4C @ NeurIPS'25 Oral
Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?
Thibaud Gloaguen, Niels Mündler, Mark Niklas Müller, Veselin Raychev, Martin Vechev
MemAgents @ ICLR 2026 Oral & Runner-up Best Paper

2025

BaxBench: Can LLMs Generate Secure and Correct Backends?
Mark Vero, Niels Mündler, Victor Chibotaru, Veselin Raychev, Maximilian Baader, Nikola Jovanović, Jingxuan He, Martin Vechev
ICML 2025 Spotlight
Black-Box Adversarial Attacks on LLM-Based Code Completion
Slobodan Jenko*, Niels Mündler*, Jingxuan He, Mark Vero, Martin Vechev
ICML 2025 * Equal contribution
Type-Constrained Code Generation with Language Models
Niels Mündler†, Jingxuan He†, Hao Wang, Koushik Sen, Dawn Song, Martin Vechev
PLDI 2025 † Co-leadership

2024

SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin Vechev
NeurIPS 2024