arxiv: 2605.06136 · v1 · submitted 2026-05-07 · 💻 cs.SE · cs.AI

Recognition: unknown

BUILD-AND-FIND: An Effort-Aware Protocol for Evaluating Agent-Managed Codebases

Jhen-Ke Lin

Authors on Pith no claims yet

Pith reviewed 2026-05-08 08:54 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords BUILD-AND-FINDagent-managed codebasesintent recoveryinspection effortrepository evaluationspecification-traced questionsagent benchmarkscode clarity

0 comments

The pith

The BUILD-AND-FIND protocol evaluates whether downstream agents can recover intended design choices from generated code repositories and how much inspection effort that recovery requires.

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

Most coding-agent benchmarks check whether generated code behaves correctly, but repository-level work involves one agent writing code that later agents must inspect, audit, or extend. The paper introduces BUILD-AND-FIND to test how clearly a generated repository communicates its hidden specification and design decisions beyond mere functionality. A builder agent receives the hidden spec and produces the codebase; a finder agent receives only the codebase plus a bank of specification-traced multiple-choice questions. The protocol records recovery accuracy and stability as gates before interpreting inspection effort, using question-only and spec-only controls plus audits to isolate the artifact's contribution. In the released high-prior task pack accuracy is already near saturation, so effort becomes the main basis for comparing artifacts that convey the same intent.

Core claim

BUILD-AND-FIND separates behavioral correctness from artifact-side recovery by having a builder create a codebase from a hidden repository specification and a finder recover the intended choices using only the codebase and a traced question bank. It reports recovery accuracy, repeatability, implementation coverage, and inspection effort, with accuracy and stability acting as gates so effort is only interpreted when recovery succeeds reliably. Question-only and spec-only controls quantify generic priors and direct specification access, while audits separate omitted claims from finder failures and verify that correct answers cite artifact evidence.

What carries the argument

The BUILD-AND-FIND protocol, consisting of a builder who sees a hidden repository specification, a finder who sees only the generated codebase plus a specification-traced multiple-choice question bank, and metrics that gate effort behind reliable recovery accuracy.

If this is right

Repositories that pass behavioral tests can still be ranked by how clearly they expose their design choices to future agents.
Lower inspection effort for the same recovery accuracy indicates that one artifact makes the intended choices easier to locate than another.
The protocol enables comparison of agent-generated codebases on communication quality once behavioral performance is saturated.
Audits can distinguish design claims that were never implemented from claims that are present but hard to find.

Where Pith is reading between the lines

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

Agents might be prompted or trained to generate code that minimizes future inspection effort, not just passes tests.
The approach could extend to other persistent artifacts such as documentation or configuration files where clarity of intent matters.
It reframes code as a communication medium between agents rather than a one-shot executable output.

Load-bearing premise

The specification-traced multiple-choice question bank faithfully captures the hidden repository specification and intended design choices without introducing its own biases or omissions.

What would settle it

If independent finders achieve near-saturation accuracy on the question bank when given a codebase that does not implement the specification, or if audits show that correct answers cite no specific evidence from the artifact, the protocol would fail to measure recovery from the generated repository.

Figures

Figures reproduced from arXiv: 2605.06136 by Jhen-Ke Lin.

**Figure 1.** Figure 1: Conditional inspection effort, Rb, on all-correct cells. Scores use the audited scoring set; missing cells are failure signals. Under this metric in the compile-pass panel, the GPT-5.5 rows are examples of full-coverage, loweffort artifact recovery: Rb = 1.033 in the high-effort panel and Rb = 1.151 for GPT-5.5-low in the low-effort panel. GPT-5.4-mini-high remains second in the high-effort panel (Rb = 1.… view at source ↗

**Figure 2.** Figure 2: Implementation-aware builder downstream recovery diagnostic. Filled markers multiply view at source ↗

**Figure 3.** Figure 3: Control-conditioned robustness views. Values are exact-match recovery percentages over view at source ↗

**Figure 4.** Figure 4: Pairwise finder agreement on total-byte builder-effort orderings in all-correct cells over the view at source ↗

**Figure 5.** Figure 5: Pair-specific builder–finder affinity residuals for the 12-agent compile-pass panel. Scores view at source ↗

**Figure 6.** Figure 6: Low-prior analogue of Figure 1. Conditional inspection effort, view at source ↗

**Figure 7.** Figure 7: Low-prior analogue of Figure 4. Pairwise finder agreement is computed on total-byte view at source ↗

**Figure 8.** Figure 8: Low-prior analogue of Figure 5. Residuals use the same matrix normalization as the main view at source ↗

read the original abstract

Most coding-agent benchmarks ask whether generated code behaves correctly. That remains essential, but repository-level engineering is increasingly agent-managed: one agent writes a repository, and later agents inspect, audit, or extend it as working context. In that setting, a generated repository is not only an answer to a task but also a communication artifact for future work. Even when strong agents nearly satisfy the visible behavioral objective, repositories can differ in how clearly they expose the intended behavior and design choices behind that behavior. We introduce BUILD-AND-FIND, a protocol for evaluating whether downstream agents can recover those intended choices from generated repositories, and how much inspection that recovery requires. For each task, a builder sees a hidden repository specification and creates a codebase; a finder sees only the codebase and a specification-traced multiple-choice question bank. The protocol separates behavioral correctness from artifact-side recovery and reports recovery accuracy, repeatability, implementation coverage, and inspection effort. Accuracy and stability act as gates: effort is interpreted only when recovery succeeds reliably. Among artifacts from which the same intent can be recovered, lower effort by the same finder suggests that the artifact makes that intent easier to locate. Question-only and spec-only controls quantify generic priors and specification access, while audits separate omitted claims from finder failures and check whether correct answers cite artifact evidence. In the released high-prior task pack, recovery accuracy is near saturation, so inspection effort and finder-specific effects provide the main panel-local comparison.

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.

Desk Editor's Note private letter to a colleague

The paper proposes a protocol to measure how clearly generated codebases communicate design intent to future agents, with effort as the key variable once recovery succeeds.

read the letter

The core idea is to split evaluation into a builder who sees a hidden spec and writes code, then a finder who gets only the code plus a set of multiple-choice questions derived from that spec. They track how accurately and with how much inspection the finder recovers the intended choices. Accuracy and repeatability serve as gates before effort is even considered, which avoids crediting easy-to-guess artifacts. Question-only and spec-only controls are included to bound generic priors and direct spec access. Audits check whether correct answers actually point to evidence in the code rather than outside knowledge. In the released high-prior pack, recovery sits near saturation, so the comparisons shift to effort and finder effects. That setup is new relative to standard correctness benchmarks and directly targets the repository-as-communication setting that multi-agent workflows create. The controls and the accuracy gate are practical choices that reduce obvious confounds. The main soft spot is the MCQ bank itself. The protocol assumes the questions form a faithful, unbiased proxy for the hidden specification, yet the abstract gives no coverage numbers, inter-annotator stats, or explicit construction protocol beyond the audits. If question phrasing or selection favors certain patterns, the effort differences could reflect question artifacts more than code clarity. The current results are also limited to one high-prior task pack, so it is unclear how the protocol behaves when recovery is harder or when specs are less structured. This work is aimed at researchers designing or critiquing agent benchmarks in software engineering. Readers who care about multi-step agent interactions or repository maintainability will find the framing useful even if they end up modifying the measurement details. The central claim is not circular and the proposal is internally coherent, so it deserves a serious referee who can press on the question-bank validation and ask for broader task coverage. I would send it to review rather than desk-reject, with the expectation that the authors strengthen the documentation and testing of the MCQ construction process.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces BUILD-AND-FIND, a protocol for evaluating agent-managed codebases. A builder agent creates a repository from a hidden specification; a finder agent, given only the generated codebase and a specification-traced multiple-choice question bank, recovers the intended design choices. The protocol reports recovery accuracy, repeatability, implementation coverage, and inspection effort, with accuracy and stability serving as gates before interpreting effort. It includes question-only and spec-only controls to quantify priors and specification access, plus audits to separate omitted claims from finder failures and to verify that correct answers cite artifact evidence. In the released high-prior task pack, recovery accuracy is reported as near saturation.

Significance. If the protocol's assumptions hold, it would address a growing need in software engineering to evaluate not only functional correctness of agent-generated code but also how clearly repositories communicate design intent for downstream agent use. The separation of behavioral correctness from artifact-side recovery, combined with effort metrics and explicit controls, offers a structured approach to comparing repository clarity. The release of a high-prior task pack with near-saturation accuracy provides a concrete starting point for comparisons, though the protocol's value for effort-based claims hinges on validation of the question bank.

major comments (1)

[Abstract] Abstract: The protocol's central claim that lower inspection effort indicates clearer artifact communication of intent (when recovery succeeds reliably) is load-bearing on the fidelity of the specification-traced MCQ bank as a complete, unbiased proxy for the hidden repository specification. While the abstract describes question-only and spec-only controls plus audits for omitted claims, it supplies no explicit coverage metric, inter-annotator agreement, or construction protocol for the question bank, leaving effort comparisons conditional on an unverified mapping from spec to questions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful review and for highlighting the need for greater explicitness in the abstract regarding the MCQ bank. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

Referee: [Abstract] Abstract: The protocol's central claim that lower inspection effort indicates clearer artifact communication of intent (when recovery succeeds reliably) is load-bearing on the fidelity of the specification-traced MCQ bank as a complete, unbiased proxy for the hidden repository specification. While the abstract describes question-only and spec-only controls plus audits for omitted claims, it supplies no explicit coverage metric, inter-annotator agreement, or construction protocol for the question bank, leaving effort comparisons conditional on an unverified mapping from spec to questions.

Authors: We agree that the abstract would be strengthened by briefly referencing the MCQ bank's construction protocol, coverage metric, and inter-annotator agreement to better support the central claim about effort as a proxy for clarity. The full manuscript provides these details in the methods and validation sections, including how questions are directly traced from specification elements, the resulting coverage of specification content, and agreement statistics among annotators, along with the audits for omitted claims. To make this information accessible at the abstract level and remove any appearance of an unverified mapping, we will revise the abstract to include a concise clause summarizing the question-bank construction and validation steps. This change will be limited to the abstract and will not alter the underlying protocol or results. revision: yes

Circularity Check

0 steps flagged

No circularity: methodological protocol with no derivations or self-referential reductions

full rationale

The paper introduces BUILD-AND-FIND as a new evaluation protocol separating behavioral correctness from artifact-side recovery of intent via a specification-traced MCQ bank, with controls for priors and audits for omissions. No equations, fitted parameters, predictions, or derivation chains exist that could reduce to inputs by construction. The work contains no self-citations of prior uniqueness theorems or ansatzes by the same author, and the central claims rest on the protocol definition plus reported observations in the released task pack rather than tautological mappings. The MCQ bank is presented as an explicit design choice with stated controls, not a hidden assumption that forces results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The proposal rests on the assumption that multiple-choice questions derived from a hidden specification can serve as a reliable proxy for recoverability of design intent; no free parameters or invented physical entities are introduced.

axioms (1)

domain assumption Recovery of intended design choices can be measured via accuracy and effort on a specification-traced multiple-choice question bank.
Central to the protocol definition in the abstract.

invented entities (1)

BUILD-AND-FIND protocol no independent evidence
purpose: To evaluate effort-aware recovery of specifications from agent-generated codebases
Newly defined evaluation method without external validation data in the abstract.

pith-pipeline@v0.9.0 · 5558 in / 1295 out tokens · 35458 ms · 2026-05-08T08:54:05.323052+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 14 canonical work pages · 2 internal anchors

[1]

URL https://proceedings.neurips.cc/paper_files/paper/2024/file/ 9547b09b722f2948ff3ddb5d86002bc0-Paper-Datasets_and_Benchmarks_Track.pdf

doi: 10.52202/079017-2610. URL https://proceedings.neurips.cc/paper_files/paper/2024/file/ 9547b09b722f2948ff3ddb5d86002bc0-Paper-Datasets_and_Benchmarks_Track.pdf. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. Program synthesis with large lan...

work page doi:10.52202/079017-2610 2024
[2]

Raymond P

doi: 10.1007/s10664-023-10381-0. Raymond P. L. Buse and Westley Weimer. Learning a metric for code readability.IEEE Transactions on Software Engineering, 36(4):546–558,

work page doi:10.1007/s10664-023-10381-0
[3]

Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, and Lilian Weng

doi: 10.1109/TSE.2009.70. Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, and Lilian Weng. MLE- bench: Evaluating machine learning agents on machine learning engineering. InInternational Conference on Learning Representations,

work page doi:10.1109/tse.2009.70 2009
[4]

Evaluating Large Language Models Trained on Code

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374,

work page internal anchor Pith review arXiv
[5]

9 Jane Cleland-Huang, Orlena C. Z. Gotel, Jane Huffman Hayes, Patrick Mäder, and Andrea Zisman. Software traceability: Trends and future directions. InFuture of Software Engineering, FOSE 2014, pages 55–69,

2014
[6]

Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt

doi: 10.1145/2593882.2593891. Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. Measuring coding challenge competence with APPS. InAdvances in Neural Information Processing Systems,

work page doi:10.1145/2593882.2593891
[7]

Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica

doi: 10.1145/3726302.3730262. Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. LiveCodeBench: Holistic and contamination-free eval- uation of large language models for code. InInternational Conference on Learning Representations,

work page doi:10.1145/3726302.3730262
[8]

Ruthruff, John Penix, J

doi: 10.1145/1368088.1368130. Andrew J. Ko and Brad A. Myers. Extracting and answering why and why not questions about Java program output.ACM Transactions on Software Engineering and Methodology, 20(2):4:1–4:36,

work page doi:10.1145/1368088.1368130
[9]

Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu

doi: 10.1145/1824760.1824761. Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu. DS-1000: A natural and reliable benchmark for data science code generation. InProceedings of the 40th International Conference on Machine Learning,

work page doi:10.1145/1824760.1824761
[10]

Tianyang Liu, Canwen Xu, and Julian McAuley

doi: 10.52202/079017-4087. Tianyang Liu, Canwen Xu, and Julian McAuley. RepoBench: Benchmarking repository-level code auto-completion systems. InInternational Conference on Learning Representations, 2024a. Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach D. Le, and David Lo. Refining ChatGPT-generated code: Characte...

work page doi:10.52202/079017-4087
[11]

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Yeon Shin, Thomas Walshe, E. Kelly Buchanan, et al. Terminal-Bench: Benchmarking agents on hard, realistic tasks in command line interfaces.arXiv preprint arXiv:2601.11868,

work page internal anchor Pith review arXiv
[12]

Evaluating code readability and legibility: An examination of human-centric studies

Delano Oliveira, Reydne Bruno, Fernanda Madeiral, and Fernando Castor. Evaluating code readability and legibility: An examination of human-centric studies. InProceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution, pages 348–359. IEEE,

2020
[13]

In: IEEE International Confer- ence on Software Maintenance and Evolution

doi: 10.1109/ICSME46990.2020.00041. 10 OpenAI. Introducing SWE-bench verified. https://openai.com/index/ introducing-swe-bench-verified/,

work page doi:10.1109/icsme46990.2020.00041 2020
[14]

Weihan Peng, Yuling Shi, Yuhang Wang, Xinyun Zhang, Beijun Shen, and Xiaodong Gu

doi: 10.1145/361598.361623. Weihan Peng, Yuling Shi, Yuhang Wang, Xinyun Zhang, Beijun Shen, and Xiaodong Gu. SWE-QA: Can language models answer repository-level code questions?arXiv preprint arXiv:2509.14635,

work page doi:10.1145/361598.361623
[15]

doi: 10.1145/1181775.1181779. Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, Johannes Heidecke, Amelia Glaese, and Tejal Patwardhan. PaperBench: Evaluating AI’s ability to replicate AI research.arXiv preprint arXiv:2504.01848,

work page doi:10.1145/1181775.1181779
[16]

Ruiqi Wang, Jiyu Guo, Cuiyun Gao, Guodong Fan, Chun Yong Chong, and Xin Xia

doi: 10.18653/v1/2024.acl-srw.28. Ruiqi Wang, Jiyu Guo, Cuiyun Gao, Guodong Fan, Chun Yong Chong, and Xin Xia. Can LLMs replace human evaluators? an empirical study of LLM-as-a-judge in software engineering tasks. Proceedings of the ACM on Software Engineering, 2(ISSTA):1955–1977,

work page doi:10.18653/v1/2024.acl-srw.28 2024
[17]

M Resource usage diagnostics Vendor token counts are reported as diagnostics

Residuals use the same matrix normalization as the main affinity diagnostic, with recovery scores recomputed on the audited low-prior question subset. M Resource usage diagnostics Vendor token counts are reported as diagnostics. They use provider-reported token totals when available, or the observed vendor-token total in the local efficiency record when p...

1922