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arxiv: 2604.10341 · v1 · submitted 2026-04-11 · 💻 cs.AI

Recognition: unknown

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline

Dheeraj Kodakandla, Kushagra Srivastva, Mahfuza Farooque, Xuan Liu

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Pith reviewed 2026-05-10 15:23 UTC · model grok-4.3

classification 💻 cs.AI
keywords NL-to-PL translationfine-tuned LLMneuro-symbolic pipelineSAT solversround-trip validationformal verificationdeterministic translation
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The pith

VeriTrans translates natural language requirements into solver-ready logic at 94.46 percent SAT/UNSAT correctness using a validator-gated neuro-symbolic pipeline.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents VeriTrans as a system that fine-tunes an LLM to convert natural language specifications into predicate logic, then applies round-trip reconstruction from logic back to natural language as an acceptance gate before compiling to conjunctive normal form for solvers. This setup runs with fixed deterministic parameters and full logging of all artifacts to support auditing and replay. On the SatBench dataset of 2100 specifications, it reaches 94.46 percent correctness in SAT/UNSAT outcomes along with 87.73 percent median round-trip similarity. Fine-tuning on only 100 to 150 curated examples adds a 1 to 1.5 percentage point gain in fidelity while keeping mean runtime around 25.8 seconds per specification. The approach targets reliability-critical applications by separating the learned translation step from symbolic checks and enforcing reproducible, auditable execution.

Core claim

VeriTrans establishes that an instruction-tuned NL-to-PL translator combined with round-trip reconstruction as a high-precision acceptance gate and canonical PL-to-CNF compilation, all under fixed API settings and complete per-item logging, delivers 94.46 percent SAT/UNSAT correctness on SatBench while exposing a tunable reliability-coverage tradeoff through the round-trip threshold.

What carries the argument

The round-trip reconstruction from PL back to NL, used as a validator gate that decides acceptance before symbolic compilation to CNF.

Load-bearing premise

Round-trip reconstruction similarity serves as a reliable proxy for actual translation correctness and the fine-tuned model generalizes beyond the small curated training set.

What would settle it

A large collection of previously unseen natural-language specifications on which many items with round-trip similarity above 75 percent produce incorrect SAT/UNSAT results would falsify the acceptance policy.

Figures

Figures reproduced from arXiv: 2604.10341 by Dheeraj Kodakandla, Kushagra Srivastva, Mahfuza Farooque, Xuan Liu.

Figure 1
Figure 1. Figure 1: Hybrid neural–symbolic VeriTrans pipeline. The LLM performs NL→ [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Fine-tuning efficiency–fidelity trade-off. Compact [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Runtime breakdown by translation direction. Val [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correlation between runtime and round-trip simi [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original 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 $\sim$94\% 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. VeriTrans presents a neuro-symbolic pipeline for translating natural-language requirements into solver-ready predicate logic (PL). It combines an instruction-tuned LLM translator with round-trip PL-to-NL reconstruction as a high-precision acceptance gate, followed by deterministic CNF compilation and SAT solving. Fixed parameters (temperature=0, seed=42) and per-item logging ensure auditability. On SatBench (2,100 specifications), the system reports 94.46% SAT/UNSAT correctness and 87.73% median round-trip similarity; compact fine-tuning on 100-150 examples yields a 1-1.5 pp gain, with a thresholded policy allowing reliability-coverage trade-offs.

Significance. If the evaluation metrics reliably indicate semantic fidelity, VeriTrans would offer a practical advance for reliable NL-to-logic front-ends in verification workflows by separating learned translation from symbolic validation and enforcing deterministic, logged execution. The emphasis on replay-driven debugging, validator overhead analysis (<15% of runtime), and the reliability knob at different tau thresholds provides concrete engineering value. The approach also demonstrates that modest fine-tuning can improve fidelity without latency penalty, which is a positive empirical observation.

major comments (2)
  1. Abstract and evaluation description: the headline 94.46% SAT/UNSAT correctness is obtained by checking whether the solver verdict on the compiled CNF matches the specification's ground-truth label. This metric only confirms equisatisfiability and does not verify that the generated PL formula is a faithful encoding of the original NL semantics; distinct formulas can share SAT/UNSAT status while differing in constraints or edge cases. The round-trip similarity is used only as an acceptance gate, with no reported correlation to semantic equivalence (e.g., via entailment checks or ground-truth PL comparison). This assumption is load-bearing for the central correctness claim.
  2. Evaluation protocol (implied in results section): the paper provides no details on how SAT/UNSAT ground-truth labels were obtained for SatBench, the exact baseline comparisons, error analysis, or statistical significance of the 1-1.5 pp fine-tuning gain. Without these, the performance numbers and generalization claims (beyond the 100-150 training examples) remain only partially verifiable.
minor comments (3)
  1. The abstract mentions 'canonical PL-to-CNF compilation' and 'per-item artifact logging' but does not specify the exact PL syntax or CNF conversion rules used; adding a short formal description or reference would improve reproducibility.
  2. No mention of how the 201-spec runtime subset was selected or whether it is representative of the full 2,100-item SatBench; clarifying this would strengthen the latency claims.
  3. The threshold policy at tau=75 retaining ~68% of items with ~94% correctness is useful, but the paper should report the full precision-recall curve or coverage vs. correctness trade-off for other tau values.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive criticism. We have carefully considered the major comments and revised the manuscript to address them. Our point-by-point responses are provided below.

read point-by-point responses

  1. Referee: Abstract and evaluation description: the headline 94.46% SAT/UNSAT correctness is obtained by checking whether the solver verdict on the compiled CNF matches the specification's ground-truth label. This metric only confirms equisatisfiability and does not verify that the generated PL formula is a faithful encoding of the original NL semantics; distinct formulas can share SAT/UNSAT status while differing in constraints or edge cases. The round-trip similarity is used only as an acceptance gate, with no reported correlation to semantic equivalence (e.g., via entailment checks or ground-truth PL comparison). This assumption is load-bearing for the central correctness claim.

    Authors: We agree that the reported correctness metric establishes equisatisfiability between the translated formula and the ground-truth label, rather than proving full semantic equivalence of the PL formulas. This is a valid observation, and we have revised the abstract and evaluation description to explicitly label the metric as 'equisatisfiability correctness' to avoid any ambiguity. We maintain that for the intended verification workflows, preserving the SAT/UNSAT outcome is the primary objective, as it determines the result of property checking. The round-trip similarity is employed as a practical filter for translation quality, and while we did not include a formal correlation analysis with semantic equivalence (due to the lack of ground-truth PL formulas in SatBench), the combination of high median round-trip similarity and high correctness rate provides supporting evidence. We have added a limitations section acknowledging this point and outlining potential extensions using automated entailment tools. revision: yes

  2. Referee: Evaluation protocol (implied in results section): the paper provides no details on how SAT/UNSAT ground-truth labels were obtained for SatBench, the exact baseline comparisons, error analysis, or statistical significance of the 1-1.5 pp fine-tuning gain. Without these, the performance numbers and generalization claims (beyond the 100-150 training examples) remain only partially verifiable.

    Authors: We have expanded the evaluation protocol description in the revised manuscript to include how the ground-truth labels were obtained for SatBench (as provided by the benchmark authors via solver-based verification of the original specifications). We have clarified the baseline comparisons by referencing the specific systems and their performance metrics from the SatBench publication. We have added an error analysis section categorizing the failure modes observed in our experiments. Regarding the statistical significance of the fine-tuning gain, we have included variance across multiple fine-tuning seeds to demonstrate consistency of the improvement, though a formal hypothesis test was not part of the original submission; we are open to adding one if a particular method is suggested. revision: partial

Circularity Check

0 steps flagged

No significant circularity; results rest on external benchmark labels and independent solver verification

full rationale

The paper evaluates its NL-to-PL pipeline by translating specifications from SatBench, compiling to CNF, invoking an external SAT solver, and comparing the resulting SAT/UNSAT verdict directly against the benchmark's pre-existing ground-truth labels. This produces the reported 94.46% correctness figure. Round-trip similarity (PL back to NL) is applied only as a post-hoc acceptance threshold and is never substituted for or derived from the SAT/UNSAT metric. No equations, fitted parameters, or self-citations are shown to reduce the central claims to their own inputs by construction. The evaluation therefore remains externally anchored rather than self-referential.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are explicitly introduced or fitted in the abstract; the approach relies on standard LLM fine-tuning practices and existing symbolic compilation tools.

pith-pipeline@v0.9.0 · 5602 in / 1322 out tokens · 36411 ms · 2026-05-10T15:23:48.273648+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

35 extracted references · 14 canonical work pages · 1 internal anchor

  1. [1]

    Beg et al

    A. Beg et al. 2025. Leveraging LLMs for Formal Software Requirements.arXiv preprint arXiv:2507.14330(2025). https://arxiv.org/abs/2507.14330

    work page arXiv 2025

  2. [2]

    Zhenhua Duan, Cong Tian, Nan Zhang, Chaofeng Yu, Mengfei Yang, and Jia He

  3. [3]

    SAT-based bounded model checking for propositional projection temporal logic.Theoretical Computer Science1049 (2025), 115368

    2025

  4. [4]

    English, Chase Walker, Dominic Simon, Sumit K

    William H. English, Chase Walker, Dominic Simon, Sumit K. Jha, and Rickard Ewetz. 2025. Verifiable Natural Language to Linear Temporal Logic Transla- tion: A Benchmark Dataset and Evaluation Suite (VLTL-Bench).arXiv preprint arXiv:2507.00877(2025). https://arxiv.org/abs/2507.00877

    work page arXiv 2025

  5. [5]

    Alessio Ferrari et al. 2025. Formal Requirements Engineering and Large Language Models: Opportunities and Challenges.Information and Software Technology (2025). doi:10.1016/j.infsof.2025.107697

    work page doi:10.1016/j.infsof.2025.107697 2025

  6. [6]

    Francesco Fuggitti et al. 2023. NL2LTL: A Python Package for Converting Natural Language Instructions to Linear Temporal Logic. InAAAI 2023 (Demo Track). doi:10.1609/aaai.v37i13.27068

    work page doi:10.1609/aaai.v37i13.27068 2023

  7. [7]

    Shiyu Han, Yixin Liu, Tianyu Zhang, Yuxin Wang, Xiang Ren, and He He. 2024. FOLIO: Natural Language Reasoning with First-Order Logic. InProceedings of EMNLP 2024

    2024

  8. [8]

    Yuwei Hao et al. 2025. Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools. InNAACL 2025

    2025

  9. [9]

    Hazra et al

    S. Hazra et al. 2025. SATQuest: A Verifier for Logical Reasoning Evaluation and Benchmarks.arXiv preprint arXiv:2509.00930(2025). https://arxiv.org/abs/2509. 00930

    work page arXiv 2025

  10. [10]

    Wassily Hoeffding. 1963. Probability Inequalities for Sums of Bounded Random Variables.J. Amer. Statist. Assoc.58, 301 (1963), 13–30

    1963

  11. [11]

    Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, and Bing Qin. 2025. A Survey on Hallucination in Large Language Models.ACM Transactions on Information Systems43, 2 (2025), 1–55

    2025

  12. [12]

    Lok Hin Marcus Lam et al. 2024. A Closer Look at Tool-based Logical Reasoning with LLMs. InALTA 2024. https://aclanthology.org/2024.alta-1.4.pdf

    2024

  13. [13]

    Xuefeng Lin et al. 2024. FVEL: Interactive Formal Verification Environment with Large Language Models. InNeurIPS 2024

    2024

  14. [14]

    Yixin Liu, Shiyu Han, Tianyu Zhang, Xiang Ren, and He He. 2024. P-FOLIO: Evaluating and Improving Logical Reasoning with First-Order Logic Proofs.arXiv preprint arXiv:2410.09207(2024). https://arxiv.org/abs/2410.09207

    work page arXiv 2024

  15. [15]

    Ricardo Mendoza, Caroline Trippel, et al. 2024. Translating Natural Language to Temporal Logics with Large Language Models. InFormal Methods in Computer- Aided Design (FMCAD)

    2024

  16. [16]

    OpenAI. 2025. OpenAI Fine-Tuning API Documentation. https://platform.openai. com/docs/guides/fine-tuning

    2025

  17. [17]

    Liangming Pan, Alon Albalak, Xiaoman Wang, and William Yang Wang. 2023. Logic-LM: Empowering Large Language Models with Symbolic Solvers.arXiv preprint arXiv:2305.12295(2023)

    work page arXiv 2023

  18. [18]

    Chen Qi, Xinyu Zhang, Jiayi Guo, and Dongxu Han. 2025. Large Language Models Meet Symbolic Solvers for Faithful Logical Reasoning. InICLR 2025

    2025

  19. [19]

    Jianxing Qin, Alexander Du, Danfeng Zhang, Matthew Lentz, and Dapeng Zhuo

  20. [20]

    InHotOS 2025

    Can Large Language Models Verify System Software?. InHotOS 2025

    2025

  21. [21]

    Lee, and Eunho Yang

    Hyun Ryu, Gyeongman Kim, Hyemin S. Lee, and Eunho Yang. 2025. Divide and Translate: Compositional First-Order Logic Translation and Verification. InICLR 2025

    2025

  22. [22]

    Mousavi Engineering: New Ideas and Emerging Results (ICSE-NIER)

    J. Sheng et al. 2025. An LLM-Driven Framework for Efficient SAT-Solving Code Search and Optimization. InICSE NIER 2025. doi:10.1109/ICSE-NIER66352.2025. 00007

    work page doi:10.1109/icse-nier66352.2025 2025

  23. [23]

    Felix Vossel, Till Mossakowski, and Björn Gehrke. 2025. Advancing Natural Language Formalization to First Order Logic with Fine-tuned LLMs.arXiv preprint arXiv:2509.22338(2025). https://arxiv.org/abs/2509.22338

    work page arXiv 2025

  24. [24]

    Wang et al

    J. Wang et al. 2025. ConformalNL2LTL: Uncertainty-Aware Natural Language to Temporal Logic Translation with Large Language Models.arXiv preprint arXiv:2504.21022(2025). https://arxiv.org/abs/2504.21022

    work page arXiv 2025

  25. [25]

    Xiangyu Wang, Haocheng Yang, Fengxiang Cheng, and Fenrong Liu. 2025. Adap- tive Selection of Symbolic Languages for Improving LLM Logical Reasoning. arXiv preprint arXiv:2510.10703(2025). https://arxiv.org/abs/2510.10703

    work page arXiv 2025

  26. [26]

    Anjiang Wei, Yuheng Wu, Yingjia Wan, Tarun Suresh, Huanmi Tan, Zhanke Zhou, Sanmi Koyejo, Ke Wang, and Alex Aiken. 2025. SATBench: Benchmarking LLMs’ Logical Reasoning via Automated Puzzle Generation from SAT Formulas. arXiv preprint arXiv:2505.14615(2025)

    work page arXiv 2025

  27. [27]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.arXiv preprint arXiv:2201.11903(2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  28. [28]

    Jiang, Wenda Li, Markus N

    Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, and Christian Szegedy. 2022. Autoformalization with Large Language Models. InNeurIPS 2022

    2022

  29. [29]

    Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu

  30. [30]

    InProceedings of ACL 2024

    Faithful Logical Reasoning via Symbolic Chain-of-Thought. InProceedings of ACL 2024

    2024

  31. [31]

    Kexun Yang et al. 2023. LeanDojo: Theorem Proving with Retrieval-Augmented Language Models. InNeurIPS 2023

    2023

  32. [32]

    Yuan Yang, Siheng Xiong, Ali Payani, Ehsan Shareghi, and Faramarz Fekri. 2024. Harnessing the Power of Large Language Models for Natural Language to First- Order Logic Translation. InProceedings of ACL 2024

    2024

  33. [33]

    Xi Ye, Qiaochu Chen, Isil Dillig, and Greg Durrett. 2023. SatLM: Satisfiability- Aided Language Models Using Declarative Prompting. InNeurIPS 2023

    2023

  34. [34]

    Thomas McCoy, and Heng Huang

    Tong Zheng, Lichang Chen, Simeng Han, R. Thomas McCoy, and Heng Huang

  35. [35]

    arXiv:2505.15817 [cs.CL] https://arxiv.org/abs/2505.15817

    Learning to Reason via Mixture-of-Thought for Logical Reasoning. arXiv:2505.15817 [cs.CL] https://arxiv.org/abs/2505.15817

    work page arXiv