arXiv AI
Jun 2
FVSpec: Real-World Property-Based Tests as Lean Challenges
arXiv:2606. 01008v1 Announce Type: cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks.
SEMBridge: Tagless-Final Program Semantics with Weakest-Precondition and Bounded-Checking Interpretations
arXiv:2606. 00220v1 Announce Type: cross Abstract: Formal methods provide rigorous accounts of program behavior, but practical software engineering often works through executable libraries, tests, and incremental design.
Related stories
arXiv AI
Jul 21
ETAS: An Effect-Typed Language for Agent Systems
arXiv:2607. 17780v1 Announce Type: cross Abstract: ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions.
arXiv AI
5d ago
Vero: Can AI Agents Build Formally Verified Software Repositories?
arXiv:2608. 13522v1 Announce Type: cross Abstract: AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code.
arXiv AI
Jul 22
Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments
arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.
arXiv AI
Jul 16
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
arXiv:2607. 13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult.
arXiv AI
Aug 11
P$^{3}$: Joint Program-and-Proof Planning for Verified Code Generation
arXiv:2608. 09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promising software that is correct by construction.
arXiv AI
Jul 13
Diversifying to Verify: When Task-Equivalent Programs Differ in Verifiability
arXiv:2607. 09366v1 Announce Type: cross Abstract: Program verification is crucial for software correctness, but producing fully verified programs remains difficult in practice.
arXiv AI
Jul 21
Composable Verification Pipelines for Multi-Agent Systems
arXiv:2607. 16266v1 Announce Type: cross Abstract: Existing approaches for reasoning about action and change provide expressive semantics for modeling dynamic systems, in most cases built on top of logic programming systems.
arXiv AI
Jun 26
An Empirical Study of LLM-Generated Specifications for VeriFast
arXiv:2606. 26490v1 Announce Type: cross Abstract: Static verification tools can assure industrial scale software, but require significant human labor to write specifications.
arXiv AI
Jul 8
Harnessing Code Agents for Automatic Software Verification
arXiv:2607. 06341v1 Announce Type: cross Abstract: Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort.
Hugging Face Trending Papers
6d ago
Vero: Can AI Agents Build Formally Verified Software Repositories?
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 implementation and a machine-checked proof of its specification, offers a stronger path toward trustworthy AI-generated software.
arXiv AI
Jun 8
Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
arXiv:2606. 06523v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence.