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ARXIV:2605.02741 · AI SOFTWARE ENGINEERING · SUBMITTED 05 MAY · 20:30 UTC · FRESHNESS STALE
ARXIV:2605.02741AI SOFTWARE ENGINEERINGSUBMITTED 05 MAY · 20:30 UTCFRESHNESS STALEYuecai Zhu · Nikolaos Tsantalis · Peter C. Rigby · arXiv
An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents.
Opportunity summary
Pain An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents. This paper presents a systematic audit of technical debt in AI-generated…
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical debt in…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Crucially, we demonstrate that neither functional correctness nor detailed prompting mitigates this decay. Code availability is flagged in the production record; the public repository…
AI Software Engineering moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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Score4.0Analysis summary
An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents.
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Paper Pack
10.48550/arXiv.2605.02741An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents.
Abstract
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI does not eliminate flaws but rather introduces a distinct machine signature of defects. Our multi-scale analysis, spanning single-file algorithmic tasks and complex, agent generated systems, identifies a fundamental Reasoning-Complexity Trade-off: as models become more capable, they generate increasingly bloated and coupled code. This architectural decay is so pronounced that we establish a Volume-Quality Inverse Law, where code volume is a near perfect predictor of structural degradation. Crucially, we demonstrate that neither functional correctness nor detailed prompting mitigates this decay. These findings challenge the current paradigm of prompt-driven generation, reframing the central problem of AI-based software engineering from one of code generation to one of architectural complexity management. We conclude that future progress depends on equipping agents with explicit architectural foresight to ensure the software they build is not just functional, but also maintainable.
Source availability
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Extraction status
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI does no...
METHOD
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI d...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Crucially, we demonstrate that neither functional correctness nor detailed prompting mitigates this decay. Code availability is flagged in the production record; the public repository link still needs pro...
WHY NOW
AI Software Engineering moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI does not eliminate flaws but rather introduces a distinct machine signature of defects.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of technical debt in AI-generated software, revealing that AI does not eliminate flaws but rather introduces a distinct machine signature of defects.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Crucially, we demonstrate that neither functional correctness nor detailed prompting mitigates this decay. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Software Engineering moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
An audit of AI-generated software revealing a trade-off between model capability and code maintainability, highlighting the need for architectural foresight in AI agents.
Segment
AI Software Engineering
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Hacker News
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Bluesky
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CITED BY
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Foundation
Extension
Commercially relevant
Conflicting
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.