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ARXIV:2601.21116 · AI-ASSISTED ENGINEERING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.21116AI-ASSISTED ENGINEERINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering.
Opportunity summary
Pain Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering. LLM coding assistants generate decisions faster than teams can validate them, yet no widely-adopted framework distinguishes conjecture from verified knowledge,…
This position paper argues that AI-assisted software engineering requires explicit mechanisms for tracking the epistemic status and temporal validity of architectural decisions. LLM coding assistants generate decisions faster than teams can validate them, yet…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We outline research directions including learnable aggregation operators, federated evidence sharing, and SMT-based claim validation.
AI-Assisted Engineering moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Score2.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering.
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Paper Pack
10.48550/arXiv.2601.21116Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering.
Abstract
This position paper argues that AI-assisted software engineering requires explicit mechanisms for tracking the epistemic status and temporal validity of architectural decisions. LLM coding assistants generate decisions faster than teams can validate them, yet no widely-adopted framework distinguishes conjecture from verified knowledge, prevents trust inflation through conservative aggregation, or detects when evidence expires. We propose three requirements for responsible AI-assisted engineering: (1) epistemic layers that separate unverified hypotheses from empirically validated claims, (2) conservative assurance aggregation grounded in the Gödel t-norm that prevents weak evidence from inflating confidence, and (3) automated evidence decay tracking that surfaces stale assumptions before they cause failures. We formalize these requirements as the First Principles Framework (FPF), ground its aggregation semantics in fuzzy logic, and define a quintet of invariants that any valid aggregation operator must satisfy. Our retrospective audit applying FPF criteria to two internal projects found that 20-25% of architectural decisions had stale evidence within two months, validating the need for temporal accountability. We outline research directions including learnable aggregation operators, federated evidence sharing, and SMT-based claim validation.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 2.0
PROBLEM
Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering. LLM coding assistants generate decisions faster than teams can validate them, yet no widely-adopted framework distinguishes conjecture from verified knowledge, prevents...
METHOD
This position paper argues that AI-assisted software engineering requires explicit mechanisms for tracking the epistemic status and temporal validity of architectural decisions. LLM coding assistants generate decisions faster than teams can validate them, yet no widely-adopted f...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We outline research directions including learnable aggregation operators, federated evidence sharing, and SMT-based claim validation.
WHY NOW
AI-Assisted Engineering moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering. LLM coding assistants generate decisions faster than teams can validate them, yet no widely-adopted framework distinguishes conjecture from verified knowledge, prevents trust inflation through conservative aggregation, or detects when evidence expires.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This position paper argues that AI-assisted software engineering requires explicit mechanisms for tracking the epistemic status and temporal validity of architectural decisions. LLM coding assistants generate decisions faster than teams can validate them, yet no widely-adopted framework distinguishes conjecture from verified knowledge, prevents trust inflation through conservative aggregation, or detects when evidence expires.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We outline research directions including learnable aggregation operators, federated evidence sharing, and SMT-based claim validation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI-Assisted Engineering moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Proposes a framework for tracking the epistemic status of architectural decisions in AI-assisted engineering.
Segment
AI-Assisted Engineering
Adoption evidence
No public code link in the paper record yet
Commercial read
2.0/10 public viability
Direct
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CITED BY
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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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
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
missing
Current read
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Defensibility
missing
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
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People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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