Matthew B. Dwyer | Search arXiv

arXiv2026

NeuroSTAR: Automata-guided Neuro-symbolic Specification Formalization

Automated translation of natural language (NL) descriptions into Linear Temporal Logic over finite traces (LTLf) is a prerequisite for automated formal verification of a system's dynamic behavior. Several LLM-based methods have recently shown potential for this task. However, they struggle with the nuance of natural language descriptions, which can lead LLMs to only partially capture the intended meaning. To address this limitation, we propose NeuroSTAR (Automata-guided Neuro-symbolic Specification Formalization), an NL-to-LTLf framework that builds on two insights. First, it leverages multiple generators to obtain diverse LTLf candidates. Second, it uses an automata-theoretic semantic comparison based on DFA traces to identify behavioral disagreements that guide formula refinement. We evaluate NeuroSTAR and show that it improves NL-to-LTLf translation performance by 8-18 percentage points relative to the prior state-of-the-art (SoTA) on unambiguous benchmarks. We further study its applicability to a body of driving law text, a complex, realistic, and reference-free domain critical for autonomous-vehicle specification. This study shows that NeuroSTAR can capture the necessary temporal semantics in 83.9% of the driving law sections, which demonstrates the effectiveness of automata-guided reference-free refinement in formalization.

cs.FL↗

arXiv2026

What Survives the Next Model? Benchmarking LLM-Based Techniques Against Single-Prompts

The software engineering research community has enthusiastically embraced the integration of Large Language Models (LLMs) into complex techniques to solve a wide variety of tasks. However, the extent to which this investment is strategic remains unclear, as the native capabilities of successive frontier model generations can rapidly render existing techniques obsolete. To assess this research investment, we analyze 35 LLM-based technique papers from ICSE 2026. We evaluate whether their complex tools can be outperformed by the simplest possible alternative: a single, automatically generated prompt executed on a newer generation model, without any iterative refinement. We find that for between 37% and 63% papers, a newer model with a single prompt natively outperforms the heavily engineered tooling proposed just a year prior. We identify that constructive techniques like code generation or repair are more amenable to substitution by a single-prompt. We also identify a surviving set of papers relying on strategies that provide additional insights to the model where newer LLMs will amplify the proposed technique. Our findings raise questions about the cost-benefit proposition of techniques designed as workarounds to temporary model deficits and the need to focus on enduring challenges that scale synergistically with future model generations. Our source codes and results are made publicly available at https://github.com/less-lab-uva/What-Survives-the-Next-Model.

cs.SE↗