Abstract
\textbf{VeriTrans} is a reliability-first ML system that compiles natural-language requirements into solver-ready logic with validator-gated reliability. The pipeline integrates an instruction-tuned NL\!\to\!PL translator, round-trip reconstruction (PL\!\to\!NL) used as a high-precision acceptance gate, and canonical PL\!\to\!CNF compilation, all executed via fixed API configuration (temperature=0; fine-tuning runs use seed=42) and per-item artifact logging (prompts, outputs, hashes) to support auditability and replay-driven debugging. On \textbf{SatBench} (2{,}100 specifications), VeriTrans achieves 94.46\% SAT/UNSAT correctness and 87.73\% median round-trip similarity. Compact fine-tuning on 100--150 curated examples improves fidelity by about 1--1.5\,pp without increasing latency (mean 25.8\,s/spec on our 201-spec runtime subset). A thresholded acceptance policy on the round-trip score exposes a reliability--coverage knob: at \tau{=}75, roughly 68\% of items are retained with \sim94\% correctness on the accepted set. Validator overhead contributes <15\% of end-to-end runtime, and all prompts/responses and timing metadata are logged to enable replay-driven debugging and regression testing. By separating learned translation from symbolic verification and enforcing deterministic, validator-gated acceptance, VeriTrans turns NL\!\to\!logic front-ends into auditable, reproducible components for reliability-critical workflows.
