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Preprint Sep 2026

NeuroSTAR: Automata-guided Neuro-symbolic Specification Formalization

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.

Joy Saha, Trey Woodlief, Sebastian G. Elbaum et al. · 0 citations
Preprint Aug 2026

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

Analysis of technique papers from ICSE 2026 finds that a newer model with a single prompt natively outperforms the heavily engineered tooling proposed just a year prior, and identifies that constructive techniques like code generation or repair are more amenable to substitution by a single-prompt.

Nahian Salsabil, Joy Saha, S. Dristi et al. · 0 citations

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