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arxiv: 2604.13100 · v1 · submitted 2026-04-10 · 💻 cs.SE · cs.AI

Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm

Yi Lin , Lujin Zhao , Yijie Shi This is my paper

Pith reviewed 2026-05-10 17:48 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords contract-codingrepo-level generationsymbolic groundingintent-driven engineeringtopological independencearchitectural parallelismsoftware engineeringsingle source of truth
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The pith

Contract-Coding projects vague intents into formal Language Contracts to achieve 47 percent functional success with near-perfect structural integrity on repository-scale tasks.

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

The paper identifies a context-fidelity trade-off where vague user intents cause architectural collapse in large codebases. It introduces Contract-Coding as a structured symbolic method that converts those intents into a formal Language Contract acting as the single source of truth. This contract enforces topological independence among modules, reduces execution depth, and enables architectural parallelism. On the Greenfield-5 benchmark the approach reaches 47 percent functional success while preserving near-perfect structure, in contrast to current agents that produce different forms of hallucination. The work aims to move intent-driven engineering toward reliable repository-level synthesis.

Core claim

Contract-Coding projects ambiguous intents into a formal Language Contract as a Single Source of Truth that enforces topological independence, isolates inter-module implementation details, decreases topological execution depth, and unlocks architectural parallelism, yielding 47 percent functional success with near-perfect structural integrity on the Greenfield-5 benchmark.

What carries the argument

The Language Contract, produced through Autonomous Symbolic Grounding, serves as the formal symbolic bridge that converts unstructured intent into an executable single source of truth.

If this is right

  • Topological independence isolates module details so changes in one module do not propagate through the entire generation chain.
  • Decreased topological execution depth shortens the reasoning path required for consistent repository-scale output.
  • Architectural parallelism allows simultaneous implementation of independent modules once the contract is fixed.
  • The method shifts generation from strict specification following toward robust intent-driven architecture synthesis.

Where Pith is reading between the lines

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

  • The same contract mechanism could be applied to multi-agent systems where each agent receives only the subset of the contract relevant to its module.
  • Reviewing or iteratively refining the generated Language Contract might become a lightweight human oversight step in production workflows.
  • If the contract format proves stable, it could serve as an intermediate artifact for verification tools that check consistency before code is emitted.

Load-bearing premise

Mapping ambiguous intents into a formal Language Contract can serve as an effective Single Source of Truth without losing critical context or introducing new failure modes in topological independence and architectural parallelism.

What would settle it

A new benchmark where Language Contract generation from ambiguous intents produces measurable loss of architectural constraints or drops functional success below the reported 47 percent level.

Figures

Figures reproduced from arXiv: 2604.13100 by Lujin Zhao, Yijie Shi, Yi Lin.

Figure 1
Figure 1. Figure 1: Linear vs. Contract-Driven Parallelism. Unlike sequential workflows that suffer from context accumulation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Contract-driven execution. The Contract de [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Contract-Guided Self-Healing Case Study. When divergent assumptions (e.g., missing attributes) occur [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Token Decoupling Analysis. As project complexity grows, the Language Contract size remains stable. Red markers show the compression efficiency. As the project complexity grows (Tproj ), the size of the Language Contract (Tcont) grows at a much slower rate. This confirms that our Constraint Pro￾jection mechanism effectively compresses the high￾dimensional intent space. For the Roguelike chal￾lenge (8,857 to… view at source ↗
Figure 5
Figure 5. Figure 5: Conflict Control via Differential Interval Anal [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualizing the Language Contract. The Contract bridges the Product Requirements Documents (PRD) [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

The shift toward intent-driven software engineering (often termed "Vibe Coding") exposes a critical Context-Fidelity Trade-off: vague user intents overwhelm linear reasoning chains, leading to architectural collapse in complex repo-level generation. We propose Contract-Coding, a structured symbolic paradigm that bridges unstructured intent and executable code via Autonomous Symbolic Grounding. By projecting ambiguous intents into a formal Language Contract, our framework serves as a Single Source of Truth (SSOT) that enforces topological independence, effectively isolating inter-module implementation details, decreasing topological execution depth and unlocking Architectural Parallelism. Empirically, while state-of-the-art agents suffer from different hallucinations on the Greenfield-5 benchmark, Contract-Coding achieves 47\% functional success while maintaining near-perfect structural integrity. Our work marks a critical step towards repository-scale autonomous engineering: transitioning from strict "specification-following" to robust, intent-driven architecture synthesis. Our code is available at https://github.com/imliinyi/Contract-Coding.

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 / 2 minor

Summary. The paper proposes Contract-Coding, a structured symbolic paradigm for repo-level code generation from vague intents. It introduces Autonomous Symbolic Grounding to project intents into a formal Language Contract that serves as a Single Source of Truth, enforcing topological independence to isolate inter-module details, reduce execution depth, and enable architectural parallelism. Empirically, on the Greenfield-5 benchmark, the approach achieves 47% functional success with near-perfect structural integrity while state-of-the-art agents exhibit different forms of hallucination. The code is released at a public GitHub repository.

Significance. If the empirical claims are supported by reproducible benchmark definitions and objective metrics, the work could offer a useful direction for mitigating context-fidelity issues in intent-driven repository-scale generation. The open-source release and focus on symbolic structure rather than pure prompting are strengths that facilitate follow-up verification.

major comments (3)
  1. [§4] §4 (Evaluation on Greenfield-5): The benchmark is introduced without a definition of its task distribution, ground-truth specifications, or precise success criteria (e.g., whether functional success requires passing a fixed test suite, semantic equivalence, or partial credit). This renders the 47% figure uninterpretable as evidence of superiority over baselines.
  2. [§3.2] §3.2 (Autonomous Symbolic Grounding): The mapping from ambiguous intent to Language Contract is described at a high level but lacks concrete mechanisms, examples of context preservation, or analysis of introduced failure modes; without these, the claim that the contract serves as an effective SSOT without losing critical information cannot be evaluated.
  3. [§4.3] §4.3 (Baseline comparisons): No implementation details, hyperparameter settings, or variance statistics are provided for the state-of-the-art agents being compared, nor are statistical significance tests reported for the 47% result versus baselines.
minor comments (2)
  1. [Abstract, §1] The abstract and §1 use the term 'topological independence' without a formal definition or reference to prior work on graph-based code representations.
  2. [Figure 2] Figure 2 (framework overview) would benefit from explicit labeling of the input/output of each stage to clarify the flow from intent to parallel code synthesis.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas for improving clarity and reproducibility, and we will revise the manuscript accordingly to address them.

read point-by-point responses

  1. Referee: [§4] §4 (Evaluation on Greenfield-5): The benchmark is introduced without a definition of its task distribution, ground-truth specifications, or precise success criteria (e.g., whether functional success requires passing a fixed test suite, semantic equivalence, or partial credit). This renders the 47% figure uninterpretable as evidence of superiority over baselines.

    Authors: We agree that the manuscript would benefit from a more explicit definition of the Greenfield-5 benchmark. In the revised version, we will expand Section 4 to include the task distribution (e.g., repository sizes and module counts), ground-truth specifications (derived from verified original repositories), and precise success criteria. Functional success is defined strictly as the generated code passing the complete fixed test suite with no errors or partial credit. This addition will make the 47% result directly interpretable and comparable. revision: yes

  2. Referee: [§3.2] §3.2 (Autonomous Symbolic Grounding): The mapping from ambiguous intent to Language Contract is described at a high level but lacks concrete mechanisms, examples of context preservation, or analysis of introduced failure modes; without these, the claim that the contract serves as an effective SSOT without losing critical information cannot be evaluated.

    Authors: We acknowledge that §3.2 currently emphasizes the conceptual framework over implementation specifics. We will revise this section to include concrete examples of the intent-to-contract mapping process, detailing how topological independence preserves essential context while abstracting implementation details. We will also add an analysis of potential failure modes, such as information loss during formalization or over-constraint, and explain the safeguards (including the SSOT enforcement) that address them. These changes will allow direct evaluation of the approach. revision: yes

  3. Referee: [§4.3] §4.3 (Baseline comparisons): No implementation details, hyperparameter settings, or variance statistics are provided for the state-of-the-art agents being compared, nor are statistical significance tests reported for the 47% result versus baselines.

    Authors: We agree that greater transparency in the experimental setup is needed. In the revision, we will augment §4.3 with full implementation details for each baseline (including models, exact hyperparameters such as temperature and token limits, and prompting strategies), variance statistics from repeated runs, and statistical significance tests (e.g., McNemar's test) for the 47% functional success rate versus baselines. This will provide a more rigorous and reproducible comparison. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive framework with no equations, derivations, or self-referential reductions.

full rationale

The paper's abstract and description contain no mathematical derivations, equations, fitted parameters, or predictions that reduce to inputs by construction. Concepts such as 'Language Contract' as SSOT, 'Autonomous Symbolic Grounding', topological independence, and architectural parallelism are introduced definitionally to describe the proposed paradigm, without any chain that equates outputs to inputs via self-definition, ansatz smuggling, or self-citation. The 47% functional success claim is presented as an empirical observation on the Greenfield-5 benchmark rather than a derived prediction from fitted data. No self-citations appear in the text to load-bear uniqueness theorems or prior results. Per the guidelines, absent any quotable reduction (e.g., Eq. X = Eq. Y by construction or a fit renamed as prediction), the score is 0 and the derivation is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the unproven assumption that formal contracts can faithfully capture intent while enforcing topological independence; no free parameters are mentioned, but the approach introduces new conceptual entities without external validation.

axioms (2)
  • domain assumption Ambiguous user intents can be projected into a formal Language Contract without critical information loss
    Invoked in the description of Autonomous Symbolic Grounding as the bridge between unstructured intent and executable code.
  • ad hoc to paper Enforcing topological independence via the contract isolates inter-module details and decreases execution depth
    Stated as the mechanism that unlocks Architectural Parallelism and prevents architectural collapse.
invented entities (2)
  • Language Contract no independent evidence
    purpose: Serves as Single Source of Truth (SSOT) to project intents and enforce structural properties
    New formal artifact introduced to mediate between vague intent and code generation
  • Autonomous Symbolic Grounding no independent evidence
    purpose: Process that converts unstructured intent into the formal contract
    Core mechanism of the proposed paradigm

pith-pipeline@v0.9.0 · 5465 in / 1461 out tokens · 31121 ms · 2026-05-10T17:48:00.273538+00:00 · methodology

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · 2 internal anchors

  1. [1]

    Evaluating Large Language Models Trained on Code

    Evaluating large language models trained on code.Preprint, arXiv:2107.03374. Qian Chen, Liu Wei, Liu Hongzhang, Chen Nuo, Dang Yufan, Li Jiahao, Yang Cheng, Chen Weize, Su Yusheng, Cong Xin, Xu Juyuan, Li Dahai, Liu Zhiyuan, and Sun Maosong. 2024. ChatDev: Com- municative agents for software development. InPro- ceedings of the 62nd Annual Meeting of the A...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  2. [2]

    MemGPT: Towards LLMs as Operating Systems

    Memgpt: Towards llms as operating systems. Preprint, arXiv:2310.08560. D. L. Parnas. 1972. On the criteria to be used in de- composing systems into modules.Commun. ACM, 15(12):1053–1058. Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. 2023. Gorilla: Large lan- guage model connected with massive apis.Preprint, arXiv:2305.15334. Baptiste ...

    work page internal anchor Pith review Pith/arXiv arXiv 1972

  3. [3]

    Qianhui Zhao, Li Zhang, Fang Liu, Junhang Cheng, Chengru Wu, Junchen Ai, Qiaoyuanhe Meng, Lichen Zhang, Xiaoli Lian, Shubin Song, and Yuanping Guo

    Codeagent: Enhancing code generation with tool-integrated agent systems for real-world repo- level coding challenges.ACL. Qianhui Zhao, Li Zhang, Fang Liu, Junhang Cheng, Chengru Wu, Junchen Ai, Qiaoyuanhe Meng, Lichen Zhang, Xiaoli Lian, Shubin Song, and Yuanping Guo

    work page

  4. [4]

    Towards realistic project-level code generation via multi-agent collaboration and semantic architecture modeling.arXiv preprint arXiv:2511.03404, 2025

    Towards realistic project-level code generation via multi-agent collaboration and semantic architec- ture modeling.Preprint, arXiv:2511.03404. A Appendix: Implementation Details of Conflict Control While the Hierarchical Execution Graph enables parallel execution, the inherent asynchrony intro- duces the risk of “lost updates.” We mitigate this via aDiffe...

    work page arXiv 2024

  5. [5]

    Executability ( Ex):Measured as a binary pass/fail. A repository is marked aspassif it satis- fies: (a) ZeroModuleNotFoundErrorafter standard pip installs; (b) Successful execution ofmain.py without runtime Traceback for at least 60 seconds of idle time

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  7. [7]

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  8. [8]

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    Static Structural Analysis.Before execu- tion, we verify the repository’s topological health to quantify the alleviation of "Architectural Col- lapse." • Architectural Fidelity (Sarch ∈[0,1] ):Mea- sures the structural alignment with the ground- truth reference architecture. Let Fgen and Fref be the sets of file paths in the generated and reference reposi...

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  9. [9]

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  10. [10]

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  11. [11]

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  12. [12]

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  13. [13]

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  14. [14]

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  15. [15]

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  16. [16]

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  17. [17]

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  18. [18]

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  19. [19]

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  20. [20]

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  23. [23]

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