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20242026
most citedType-Constrained Code Generation with Language Models

8 citations · 17 across the 16 of their papers we have counts for

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9 papers · 1 filter

cs.LG2026

Vero: Can AI Agents Build Formally Verified Software Repositories?

Zhe Ye, Hantao Lou, Yuechun Sun +8

AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an…

cs.CR2026

Agent Security Needs Redefinition through a Holistic Framework

Vincent Siu, Jingxuan He, Kyle Montgomery +3

Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious. Benchmarks ask whether an agent performs a harmful soundin…

cs.CR2026

CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities

Tianneng Shi, Robin Rheem, Dongwei Jiang +13

AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity e…

cs.PL2026

Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code

Niels Mündler-Sasahara, Hristo Venev, Dawn Song +2

Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult. Off-the-shelf compilers…

cs.CR2026★ 3 cited

ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?

Zhun Wang, Nico Schiller, Hongwei Li +13

AI agents are rapidly gaining capabilities that could significantly reshape cybersecurity, making rigorous evaluation urgent. A critical capability is exploitation: turning a vulne…

cs.CR2026

SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization

Hao Wang, Niels Mündler, Mark Vero +3

Reasoning language models (RLMs) are increasingly used in programming. Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code. Pri…

cs.CR2026

A Framework for Formalizing LLM Agent Security

Vincent Siu, Jingxuan He, Kyle Montgomery +4

Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruc…

cs.AI2026

OpenSage: Self-programming Agent Generation Engine

Hongwei Li, Zhun Wang, Qinrun Dai +11

Agent development kits (ADKs) provide effective platforms and tooling for constructing agents, and their designs are critical to the constructed agents' performance, especially the…

cs.CR2026

SecCodePRM: A Process Reward Model for Code Security

Weichen Yu, Ravi Mangal, Yinyi Luo +4

Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detectio…