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arxiv: 2605.30054 · v1 · pith:EFADKAOD · submitted 2026-05-28 · cs.SE · cs.AI

Projectional Decoding: Towards Semantic-Aware LLM Generation

Boqi Chen , Jos\'e Antonio Hern\'andez L\'opez , Aren A. Babikian This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-29 06:17 UTCgrok-4.3pith:EFADKAODrecord.jsonopen to challenge →

classification cs.SE cs.AI
keywords projectional decodingsemantic validityLLM generationsoftware engineeringpartial graph modelconstrained decodingprogram generationsemantic validation
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The pith

Maintaining a partial graph model during LLM generation enables incremental semantic validation with provable guarantees for software artifacts.

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

The paper proposes projectional decoding to address the problem that LLMs often produce software artifacts that are syntactically correct but semantically invalid. Instead of generating text alone, the method keeps a partial graph model as the central representation while tokens are produced one by one. This graph lets the system check domain semantics at each step, record uncertainty, detect errors as they arise, and steer the output toward results that satisfy semantic rules. A reader would care because current constrained-decoding methods stop at syntax or narrow rules, whereas this framework aims to bring general, provable semantic control into the generation loop itself.

Core claim

Projectional decoding integrates domain semantics directly into the generation process by maintaining, alongside text, a partial graph model as the primary artifact representation throughout generation. This abstract representation enables incremental semantic validation by explicitly capturing uncertainty and natively supporting error detection, while guiding generation toward semantically valid outputs with provable guarantees. Preliminary results on a program generation task demonstrate the potential to improve semantic validity of LLM-generated artifacts and to support verifiable automation across software engineering activities.

What carries the argument

projectional decoding, which treats a partial graph model as the primary representation maintained token-by-token to connect generated text to domain-specific semantic reasoning.

If this is right

  • Semantic validation can occur incrementally as each token is generated rather than only after the full artifact is complete.
  • Uncertainty in the artifact is represented explicitly inside the partial graph.
  • Error detection is native to the representation instead of added as an external post-check.
  • Generation can be steered toward outputs that satisfy semantic rules under provable guarantees.
  • Verifiable automation becomes feasible for a range of software engineering tasks that require semantic correctness.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same partial-graph approach could be tested on generation tasks outside software engineering that still require logical or domain constraints.
  • Combining the graph model with existing syntactic constrained decoders might produce a single pipeline that enforces both syntax and semantics.
  • Real-time tools such as IDEs could display the evolving graph to give developers immediate visibility into semantic issues during generation.

Load-bearing premise

A partial graph model can be built and updated incrementally during token-by-token generation in a way that captures enough domain semantics to enable validation with provable guarantees.

What would settle it

An experiment applying the method to program generation that produces outputs containing semantic errors the maintained graph model neither detected nor prevented.

Figures

Figures reproduced from arXiv: 2605.30054 by Aren A. Babikian, Boqi Chen, Jos\'e Antonio Hern\'andez L\'opez.

Figure 1
Figure 1. Figure 1: Architecture of projectional decoding the LLM generation process [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An example of projectional decoding for code gen [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An example of the CLEVR program (e.g., abstract syntax, control flow, and data flow) and incremen￾tally constructs a corresponding partial model, shown in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Large language models (LLMs) are increasingly used to generate software artifacts across many software engineering (SE) tasks, yet ensuring the semantic validity of these artifacts remains a fundamental challenge. Existing constrained decoding techniques can enforce syntactic correctness and, in some cases, specific semantic rules, but lack a general representation that bridges LLM-generated text with the reasoning required for semantic validation in SE. In this paper, we propose projectional decoding, a novel conceptual framework that integrates domain semantics directly into the generation process by maintaining, alongside text, a partial graph model as the primary artifact representation throughout generation. This abstract representation enables incremental semantic validation by explicitly capturing uncertainty and natively supporting error detection, while guiding generation toward semantically valid outputs with provable guarantees. We present preliminary results on a program generation task which demonstrate the potential of this approach to improve the semantic validity of LLM-generated artifacts. We also discuss how projectional decoding can enable verifiable automation with LLMs across various SE activities.

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

3 major / 1 minor

Summary. The manuscript proposes projectional decoding, a conceptual framework for LLM generation of software engineering artifacts. It maintains a partial graph model as the primary representation alongside generated text to enable incremental semantic validation, explicit uncertainty capture, native error detection, and guidance toward semantically valid outputs with provable guarantees. Preliminary results on a program generation task are cited to show potential for improved semantic validity, with discussion of broader SE applications.

Significance. A working realization of the framework could meaningfully advance reliable LLM use in SE by supplying a general semantic representation that goes beyond syntactic constraints. However, the manuscript supplies only a high-level description with no supporting formalization or evidence, so significance cannot yet be assessed.

major comments (3)
  1. [Abstract] Abstract: the central claim that the partial graph model 'enables incremental semantic validation ... with provable guarantees' is unsupported. The manuscript provides neither a definition of the graph representation, a projection operator from token sequences, update rules that preserve invariants, nor any proof sketch or pseudocode showing how uncertainty is captured or semantic validity enforced.
  2. [Abstract] Abstract (framework paragraph): the weakest assumption—that a partial graph model can be constructed and maintained incrementally during token-by-token generation while capturing sufficient domain semantics—receives no mechanism or formalization, leaving the guarantee claim uncheckable.
  3. [Preliminary results] Preliminary results: the text states that results 'demonstrate the potential' of the approach, yet supplies no data, error analysis, baseline comparisons, or quantitative metrics, so the empirical support for improved semantic validity cannot be evaluated.
minor comments (1)
  1. [Abstract] The abstract repeats the list of benefits ('incremental semantic validation', 'explicitly capturing uncertainty', 'natively supporting error detection', 'guiding generation') in consecutive sentences; a single consolidated statement would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We agree that the current manuscript is primarily conceptual and high-level, and we will revise it to incorporate the requested formalizations, mechanisms, and empirical details to strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the partial graph model 'enables incremental semantic validation ... with provable guarantees' is unsupported. The manuscript provides neither a definition of the graph representation, a projection operator from token sequences, update rules that preserve invariants, nor any proof sketch or pseudocode showing how uncertainty is captured or semantic validity enforced.

    Authors: We acknowledge that the abstract summarizes the intended benefits of the framework without embedding the supporting formal elements. The body of the manuscript describes the overall approach at a conceptual level. To address this, the revised version will include an explicit section with the definition of the graph representation, the projection operator, update rules that preserve invariants, and a proof sketch demonstrating how uncertainty is captured and semantic validity is enforced. revision: yes

  2. Referee: [Abstract] Abstract (framework paragraph): the weakest assumption—that a partial graph model can be constructed and maintained incrementally during token-by-token generation while capturing sufficient domain semantics—receives no mechanism or formalization, leaving the guarantee claim uncheckable.

    Authors: The framework paragraph in the abstract is intentionally concise. The manuscript body provides an initial description of incremental maintenance, but we agree that the mechanisms require formalization to be verifiable. In revision, we will add explicit mechanisms, update rules, and formalization showing how the partial graph model is constructed and maintained token-by-token while capturing domain semantics. revision: yes

  3. Referee: [Preliminary results] Preliminary results: the text states that results 'demonstrate the potential' of the approach, yet supplies no data, error analysis, baseline comparisons, or quantitative metrics, so the empirical support for improved semantic validity cannot be evaluated.

    Authors: The current manuscript mentions preliminary results at a high level to indicate promise on a program generation task. We agree that this is insufficient for evaluation. The revised manuscript will expand the section to include the actual data, error analysis, baseline comparisons, and quantitative metrics supporting the claims of improved semantic validity. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual proposal with no equations, fits, or self-citation reductions

full rationale

The manuscript presents a high-level conceptual framework for projectional decoding without any equations, fitted parameters, or derivation steps. The central claim of 'provable guarantees' is asserted as a property of the proposed partial graph representation but is not derived from prior results, self-citations, or definitions that reduce to the claim itself. No load-bearing self-citations appear, and the framework is not shown to be equivalent to its inputs by construction. The absence of formalization means there are no steps that can be checked for circularity under the enumerated patterns; the proposal remains self-contained as an unproven idea rather than a circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the unproven ability of partial graph models to represent and validate domain semantics incrementally during generation.

axioms (1)
  • domain assumption Domain semantics for SE artifacts can be captured sufficiently by a partial graph model that is updated token-by-token.
    This is invoked as the basis for semantic validation and provable guarantees in the framework description.
invented entities (1)
  • partial graph model no independent evidence
    purpose: Primary artifact representation maintained alongside generated text for uncertainty capture and semantic validation.
    New representation introduced by the framework; no independent evidence or external validation provided in the abstract.

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discussion (0)

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Reference graph

Works this paper leans on

42 extracted references · 7 canonical work pages · 4 internal anchors

  1. [1]

    Thorsten Berger, Markus Völter, Hans Peter Jensen, Taweesap Dangprasert, and Janet Siegmund. 2016. Efficiency of projectional editing: A controlled experiment. InProceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, 763–774

    2016

  2. [2]

    Gábor Bergmann, Ákos Horváth, István Ráth, Dániel Varró, András Balogh, Zoltán Balogh, and András Ökrös. 2010. Incremental evaluation of model queries over EMF models. InModel Driven Engineering Languages and Systems - 13th International Conference. Springer, 76–90

    2010

  3. [3]

    2016.Implementing domain-specific languages with Xtext and Xtend

    Lorenzo Bettini. 2016.Implementing domain-specific languages with Xtext and Xtend. Packt Publishing Ltd

    2016

  4. [4]

    Luca Beurer-Kellner, Marc Fischer, and Martin Vechev. 2023. Prompting is pro- gramming: A query language for large language models.Proceedings of the ACM on Programming Languages7, PLDI (2023), 1946–1969

    2023

  5. [5]

    Boqi Chen, Ou Wei, Bingzhou Zheng, and Gunter Mussbacher. 2025. Accurate and Consistent Graph Model Generation from Text with Large Language Models. In 28th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems. IEEE, 130–141

    2025

  6. [6]

    Zhaorun Chen, Mintong Kang, and Bo Li. 2025. ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning. InForty-second International Conference on Machine Learning

    2025

  7. [7]

    Patrick Cousot and Radhia Cousot. 1977. Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. InConference Record of the Fourth ACM Symposium on Principles of Programming Languages. ACM, 238–252

    1977

  8. [8]

    Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio, Phuong T Nguyen, and Riccardo Rubei. 2025. On the use of large language models in model-driven engineering. Software and Systems Modeling24, 3 (2025), 923–948

    2025

  9. [9]

    Yixin Dong, Charlie F Ruan, Yaxing Cai, Ziyi Xu, Yilong Zhao, Ruihang Lai, and Tianqi Chen. 2025. XGrammar: Flexible and efficient structured generation engine for large language models.Proceedings of Machine Learning and Systems 7 (2025)

    2025

  10. [10]

    Michalis Famelis, Rick Salay, and Marsha Chechik. 2012. Partial models: Towards modeling and reasoning with uncertainty. In34th International Conference on Software Engineering. IEEE Computer Society, 573–583

    2012

  11. [11]

    Usman Gohar, Michael C Hunter, Robyn R Lutz, and Myra B Cohen. 2024. Code- feater: Using LLMs to find defeaters in assurance cases. InProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. ACM, 2262–2267

    2024

  12. [12]

    Guidance AI. 2024. LLGuidance: Fast Structured Generation for Large Language Models. https://github.com/guidance-ai/llguidance

    2024

  13. [13]

    Junda He, Christoph Treude, and David Lo. 2025. LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead.ACM Transactions on Software Engineering and Methodology34, 5 (2025), 124:1–124:30

    2025

  14. [14]

    Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, et al

  15. [15]

    InThe Twelfth International Conference on Learning Representations

    MetaGPT: Meta programming for a multi-agent collaborative framework. InThe Twelfth International Conference on Learning Representations

  16. [16]

    Dong Huang, Jie M Zhang, Michael Luck, Qingwen Bu, Yuhao Qing, and Heming Cui. 2023. Agentcoder: Multi-agent-based code generation with iterative testing and optimisation.arXiv preprint arXiv:2312.13010(2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  17. [17]

    JetBrains. 2026. JetBrains MPS: The Meta Programming System. https://www. jetbrains.com/mps/. Accessed: 2026-01-22

    2026

  18. [18]

    Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017. CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning. InIEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 1988–1997

    2017

  19. [19]

    Parv Kapoor, Akila Ganlath, Michael Clifford, Changliu Liu, Sebastian Scherer, and Eunsuk Kang. 2025. Constrained Decoding for Robotics Foundation Models. arXiv preprint arXiv:2509.01728(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  20. [20]

    Louis-Edouard Lafontant and Eugene Syriani. 2020. Gentleman: a light-weight web-based projectional editor generator. InACM/IEEE 23rd International Confer- ence on Model Driven Engineering Languages and Systems, Companion Proceedings. ACM, 1:1–1:5

    2020

  21. [21]

    José Antonio Hernandez López, Máté Földiák, and Dániel Varró. 2024. Text2VQL: teaching a model query language to open-source language models with ChatGPT. InProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. 13–24

    2024

  22. [22]

    Michael R Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, and Patana- mon Thongtanunam. 2025. Automatic programming: Large language models and beyond.ACM Transactions on Software Engineering and Methodology34, 5 (2025), 1–33

    2025

  23. [23]

    Franklin Ma and Alan J Hu. 2025. Logically Constrained Decoding. InProceedings of The 3rd Workshop on Mathematical Natural Language Processing. ACL, 150–167

    2025

  24. [24]

    Kristóf Marussy, Attila Ficsor, Oszkár Semeráth, and Dániel Varró. 2024. Refinery: Graph solver as a service: Refinement-based generation and analysis of consistent models. InProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings. ACM, 64–68

    2024

  25. [25]

    Babikian, and Dániel Varró

    Kristóf Marussy, Oszkár Semeráth, Aren A. Babikian, and Dániel Varró. 2020. A Specification Language for Consistent Model Generation based on Partial Models. Journal of Object Technology19, 3 (2020), 3:1–22

    2020

  26. [26]

    Niels Mündler, Jingxuan He, Hao Wang, Koushik Sen, Dawn Song, and Mar- tin Vechev. 2025. Type-Constrained Code Generation with Language Models. Proceedings of the ACM on Programming Languages9, PLDI (2025), 601–626

    2025

  27. [27]

    Shaan Nagy, Timothy Zhou, Nadia Polikarpova, and Loris D’Antoni. 2026. Chop- Chop: A Programmable Framework for Semantically Constraining the Output of Language Models.Proceedings of the ACM on Programming Languages10, POPL (2026), 1905–1932

    2026

  28. [28]

    Amir Pnueli. 1977. The temporal logic of programs. In18th annual symposium on foundations of computer science. ieee, 46–57

    1977

  29. [29]

    Mark Richters and Martin Gogolla. 2002. OCL: Syntax, semantics, and tools. InObject Modeling with the OCL: The Rationale behind the Object Constraint Language. Lecture Notes in Computer Science, Vol. 2263. Springer, 42–68

    2002

  30. [30]

    Oszkár Semeráth and Dániel Varró. 2017. Graph Constraint Evaluation over Partial Models by Constraint Rewriting. InTheory and Practice of Model Trans- formation - 10th International Conference (Lecture Notes in Computer Science, Vol. 10374). Springer, 138–154

    2017

  31. [31]

    Da Song, Yuheng Huang, Boqi Chen, Tianshuo Cong, Randy Goebel, Lei Ma, and Foutse Khomh. 2026. Evaluating Implicit Regulatory Compliance in LLM Tool Invocation via Logic-Guided Synthesis.arXiv preprint arXiv:2601.08196(2026)

    work page arXiv 2026

  32. [32]

    Bastien Sultan and Ludovic Apvrille. 2024. AI-driven consistency of SysML diagrams. InProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. ACM, 149–159

    2024

  33. [33]

    Chuyue Sun, Ying Sheng, Oded Padon, and Clark Barrett. 2024. Clover: Closed- loop verifiable code generation. InAI Verification - First International Symposium. Springer, 134–155

    2024

  34. [34]

    Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, et al. 2024. Debugbench: Evaluating debugging capability of large language models. InFindings of the Association for Computational Linguistics. ACL, 4173–4198

    2024

  35. [35]

    Shubham Ugare, Rohan Gumaste, Tarun Suresh, Gagandeep Singh, and Sasa Misailovic. 2025. IterGen: Iterative Semantic-aware Structured LLM Genera- tion with Backtracking. InThe Thirteenth International Conference on Learning Representations

    2025

  36. [36]

    Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, and Gagandeep Singh. 2025. SynCode: LLM Generation with Grammar Augmentation.Transac- tions on Machine Learning Research(2025)

    2025

  37. [37]

    Dániel Varró, Oszkár Semeráth, Gábor Szárnyas, and Ákos Horváth. 2018. To- wards the automated generation of consistent, diverse, scalable and realistic graph models. InGraph Transformation, Specifications, and Nets - In Memory of Hartmut Ehrig (Lecture Notes in Computer Science, Vol. 10800). Springer, 285–312

    2018

  38. [38]

    Markus Voelter, Tamás Szabó, Sascha Lisson, Bernd Kolb, Sebastian Erdweg, and Thorsten Berger. 2016. Efficient development of consistent projectional editors using grammar cells. InProceedings of the 2016 ACM SIGPLAN International Conference on Software Language Engineering. 28–40

    2016

  39. [39]

    Brandon T Willard and Rémi Louf. 2023. Efficient guided generation for large language models.arXiv preprint arXiv:2307.09702(2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  40. [40]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report.arXiv preprint arXiv:2505.09388(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  41. [41]

    Zhe Ye, Zhengxu Yan, Jingxuan He, Timothe Kasriel, Kaiyu Yang, and Dawn Song. 2025. VERINA: Benchmarking Verifiable Code Generation.arXiv preprint arXiv:2505.23135(2025)

    work page arXiv 2025

  42. [42]

    Quanjun Zhang, Chunrong Fang, Yang Xie, Yaxin Zhang, Yun Yang, Weisong Sun, Shengcheng Yu, and Zhenyu Chen. 2023. A survey on large language models for software engineering.arXiv preprint arXiv:2312.15223(2023)

    work page arXiv 2023