相关论文: How Can I Do That with ACL2? Recent Enhancements t…

Smtlink is an extension of ACL2 with Satisfiability Modulo Theories (SMT) solvers. We presented an earlier version at ACL2'2015. Smtlink 2.0 makes major improvements over the initial version with respect to soundness, extensibility,…

We present a tool, simplify-defun, that transforms the definition of a given function into a simplified definition of a new function, providing a proof checked by ACL2 that the old and new functions are equivalent. When appropriate it also…

This volume contains the proceedings of the Eighteenth International Workshop on the ACL2 Theorem Prover and Its Applications (ACL2-2023), a two-day workshop held at the University of Texas at Austin and online, on November 13-14. These…

We present an experimental, verified clause processor ctv-cp that fits into the framework used at Arm for formal verification of arithmetic hardware designs. This largely automates the ACL2 proof development effort for integer multiplier…

This volume contains the proceedings of the Eleventh International Workshop on the ACL2 Theorem Prover and its Applications, held on May 30 and 31, 2013, in Laramie, Wyoming, USA. ACL2 is an industrial-strength automated reasoning system,…

In-context learning (ICL) is a recent advancement in the capabilities of large language models (LLMs). This feature allows users to perform a new task without updating the model. Concretely, users can address tasks during the inference time…

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an…

ESBMC implements many state-of-the-art techniques for model checking. We report on new and improved features that allow us to obtain verification results for previously unsupported programs and properties. ESBMC employs a new static…

Nowadays, we are witnessing a wide adoption of Machine learning (ML) models in many safety-critical systems, thanks to recent breakthroughs in deep learning and reinforcement learning. Many people are now interacting with systems based on…

Python is a high-level interpreted language that has become an industry standard in a wide variety of applications. In this paper, we take a first step towards using ACL2s to reason about Python code by developing an embedding of a subset…

When ACL2 is used to model the operational semantics of computing machines, machine states are typically represented by terms recording the contents of the state components. When models are realistic and are stepped through thousands of…

This document presents TLS and how to make it secure enough as of 2014 Spring. Of course all the information given here will rot with time. Protocols known as secure will be cracked and will be replaced with better versions. Fortunately we…

In-context learning (ICL) has emerged as a powerful capability of large language models (LLMs), enabling them to perform new tasks based on a few provided examples without explicit fine-tuning. Despite their impressive adaptability, these…

Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large body of content upon each visit, while others are casual users…

The OpenMP language continues to evolve with every new specification release, as does the need to validate and verify the new features that have been introduced. With the release of OpenMP 5.0 and OpenMP 5.1, plenty of new target offload…

Iterative algorithms are traditionally expressed in ACL2 using recursion. On the other hand, Common Lisp provides a construct, loop, which -- like most programming languages -- provides direct support for iteration. We describe an ACL2…

We demonstrate that LLMs may learn indicators of document usefulness and modulate their updates accordingly. We introduce random strings ("tags") as indicators of usefulness in a synthetic fine-tuning dataset. Fine-tuning on this dataset…

AI-assisted code generation tools have revolutionized software development, offering unprecedented efficiency and scalability. However, multiple studies have consistently highlighted challenges such as security vulnerabilities, reliability…

Distributed software is very tricky to implement correctly as some errors only occur in peculiar situations. For such errors testing is not effective. Mathematically proving correctness is hard and time consuming, and therefore, it is…

In-context Learning (ICL) has achieved notable success in the applications of large language models (LLMs). By adding only a few input-output pairs that demonstrate a new task, the LLM can efficiently learn the task during inference without…