Jianzhu Bao, Haozhen Zhang, Kuicai Dong, Bozhi Wu, Sarthak Ketanbhai Modi, Zi Pong Lim, Yon Shin Teo, Wenya Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Abstract Requirement defects are a major source of late-stage failures in automotive systems, yet rigorous validation is rarely applied during early development. While formal methods offer strong guarantees, their adoption at the requirements level is limited by high formalization cost and expertise barriers. We present an industry-oriented, shift-left verification approach that integrates Large Language Models (LLMs) with formal methods to enable requirements-level validation. Requirements are classified and decomposed by LLMs, translated into CSP system models and assertions, and refined through a CEGAR-inspired loop using the FDR4 model checker. Validation is decomposed into requirement–assertion pairs supported by natural-language back-translations and confidence scores, preserving expert control without manual formal modeling. Domain knowledge–based validation further leverages historical defect data to identify implicit requirement gaps. We evaluate the approach on three real-world automotive case studies. Results show high automation for small-to-medium systems (80–100% synthesis success for up to $$\sim 60$$ ∼ 60 requirements), effective expert validation guided by LLM confidence estimates, and 100% detection of known historical defects alongside 22 novel gaps. The workflow completes within 10–35 min per project at negligible cost ( $$<8$$ < 8 per project), with limited expert effort. Our results demonstrate that LLM+formal method hybridization can provide scalable, rigorous, and industrially viable requirements-level verification, supporting practical shift-left adoption in automotive systems.

Reasoning is a fundamental ability towards artificial general intelligence (AGI). Fueled by the success of deep learning, the neural logic machines models (NLMs) have introduced novel neural-symbolic structures and demonstrate great performance and generalization on reasoning and decision-making tasks. However, the original training approaches of the NLMs are still far from perfect, the models would repeat similar mistakes during the training process which leads to sub-optimal performance. To mitigate this issue, we present a novel framework named Failure Reflection Guided Regularizer (FRGR). FRGR first dynamically identifies and summarizes the root cause if the model repeats similar mistakes during training. Then it penalizes the model if it makes similar mistakes in future training iterations. In this way, the model is expected to avoid repeating errors of similar root causes and converge faster to a better-performed optimum. Experimental results on multiple relational reasoning and decision-making tasks demonstrate the effectiveness of FRGR in improving performance, generalization, training efficiency, and data efficiency.

Abstract Boolean satisfiability (SAT) solving is a fundamental problem in computer science. Finding efficient algorithms for SAT solving has broad implications in many areas of computer science and beyond. Quantum SAT solvers have been proposed in the literature based on Grover’s algorithm. Although existing quantum SAT solvers can consider all possible inputs at once, they evaluate each clause in the formula one by one sequentially, making the time complexityO(m), linear to the number of clausesm,per Grover iteration. In this work, we develop aparallelquantum SAT solver, which reduces the time complexity in each iteration to constant timeO(1) by utilising extra entangled qubits. To further improve the scalability of our solution in case of extremely large problems, we develop a distributed version of the proposed parallel SAT solver based on quantum teleportation such that the total qubits required are shared and distributed among a set of quantum computers (nodes), and the quantum SAT solving is accomplished collaboratively by all the nodes. We prove the correctness of our approaches and evaluate them in simulations and real quantum computers.

The automotive industry is shifting from hardware-centric to software-centric with the emergence of various intelligent features powered by software. This poses a new challenge for software testers to ensure software reliability by designing test plans that satisfy the test objectives while abiding by the constraints like scope, time, as well as various automotive safety standards. This paper proposed an automatic test plan generation framework built on the evolutionary algorithm. A novel encoding mechanism is proposed to represent the multi-dimensional test plan, while a belief model is proposed to reveal the underlying correlations between the relevant test attributes. Experiments conducted on an actual automotive software in production environment developed by our industry partner show that our method can achieve around 50% improvements in finding defects and covering high-priority test cases as compared to typical evolutionary algorithms while abiding by multiple constraints such as the total run time and custom objectives set by users.

Exploring the underlying structure of a Human-Machine Interface (HMI) product effectively while adhering to the pre-defined test conditions and methodology is critical for validating the quality of the software. We propose an reinforcement-learning powered Automated Software Structure Exploration Framework for Testing (ASSET), which is capable of interacting with and analyzing the HMI software under testing (SUT). The main challenge is to incorporate the human instructions into the ASSET phase by using the visual feedback such as the downloaded image sequence from the HMI, which could be difficult to analyze. Our framework combines both computer vision and natural language processing techniques to understand the semantic meanings of the visual feedback. Building on the semantic understanding, we develop a rules-guided software exploration algorithm via reinforcement learning and deterministic finite automaton (DFA). We conducted experiments on HMI software in actual production phase and demonstrate that the exploration coverage and efficiency of our framework outperforms current start-of-art methods.

Discovering the underlying structure of HMI software efficiently and sufficiently for the purpose of testing without any prior knowledge on the software logic remains a difficult problem. The key challenge lies in the complexity of the HMI software and the high variance in the coverage of current methods. In this paper, we introduce the PathFinder, an effective and automatic HMI software exploration framework. PathFinder adopts a curiosity-based reinforcement learning framework to choose actions that lead to the discovery of more unknown states. Additionally, PathFinder progressively builds a navigation model during the exploration to further improve state coverage. We have conducted experiments on both simulations and real-world HMI software testing environment, which comprise a full tool chain of automobile dashboard instrument cluster. The exploration coverage outperforms manual and fuzzing methods which are the current industrial standards.