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ARXIV:2605.23108 · AI-ASSISTED CODE REVIEW · SUBMITTED 25 MAY · 20:35 UTC · FRESHNESS STALE
ARXIV:2605.23108AI-ASSISTED CODE REVIEWSUBMITTED 25 MAY · 20:35 UTCFRESHNESS STALEKaushal Bansal · arXiv
A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models.
Opportunity summary
Pain A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent…
AI-assisted code review tools typically operate as generic "expert reviewer" agents, producing homogeneous findings regardless of the analysis type needed. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The disposition system achieves 46% convergence with human reviewers (validating signal quality), identifies unique findings at a 75% rate, and produces no findings judged…
AI-Assisted Code Review moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models.
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Paper Pack
10.48550/arXiv.2605.23108A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models.
Abstract
AI-assisted code review tools typically operate as generic "expert reviewer" agents, producing homogeneous findings regardless of the analysis type needed. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent personality lenses grounded in specific epistemological traditions (Pyrrhonist Skepticism, Navya-Ny=aya logic, Diogenes' Cynicism, Confucian relational ethics) that direct attention to structurally different types of issues. Each disposition is defined apophatically (by what it refuses to do), equipped with a self-monitoring failure mode (hamartia), and orchestrated in sequence by role protocols. We evaluate this system on 50 merged pull requests across 7 repositories spanning 5 programming languages (Python, Go, C++, Java, Terraform), 5 organizations (2 enterprise, 3 open-source), and 2 temporal eras (pre-AI 2020, post-AI 2024--2026). The disposition system achieves 46% convergence with human reviewers (validating signal quality), identifies unique findings at a 75% rate, and produces no findings judged false-positive by the author across 601 total findings (inter-rater agreement was not assessed and remains a limitation). A controlled baseline comparison demonstrates that 51% of disposition findings are not produced by the same model using generic "expert reviewer" prompting, and these unique findings target structural, operational, and logical concerns rather than standard code-level issues. Preliminary cross-model validation (Claude Opus vs.\ GPT Codex 5.3-xhigh) on 3 PRs shows 100% framework-structure adherence with 39% finding-level agreement, suggesting the framework provides real behavioral constraint while preserving model-specific analytical perspective.
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Proof status
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What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Dimensions overall score 7.0
PROBLEM
A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent personality lenses groun...
METHOD
AI-assisted code review tools typically operate as generic "expert reviewer" agents, producing homogeneous findings regardless of the analysis type needed. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent personality lenses...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The disposition system achieves 46% convergence with human reviewers (validating signal quality), identifies unique findings at a 75% rate, and produces no findings judged false-positive by the author acr...
WHY NOW
AI-Assisted Code Review moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent personality lenses grounded in specific epistemological traditions (Pyrrhonist Skepticism, Navya-Ny=aya logic, Diogenes' Cynicism, Confucian relational ethics) that direct attention to structurally different types of issues.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI-assisted code review tools typically operate as generic "expert reviewer" agents, producing homogeneous findings regardless of the analysis type needed. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent personality lenses grounded in specific epistemological traditions (Pyrrhonist Skepticism, Navya-Ny=aya logic, Diogenes' Cynicism, Confucian relational ethics) that direct attention to structurally different types of issues.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The disposition system achieves 46% convergence with human reviewers (validating signal quality), identifies unique findings at a 75% rate, and produces no findings judged false-positive by the author across 601 total findings (inter-rater agreement was not assessed and remains a limitation). 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-Assisted Code Review moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel AI code review system that uses philosophical dispositions to guide analysis and uncover unique vulnerabilities, outperforming generic models.
Segment
AI-Assisted Code Review
Adoption evidence
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Commercial read
7.0/10 public viability
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2/3 checks · 67%
Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Current read
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Defensibility signals are missing.
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
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missing
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Regulatory load
missing
Current read
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Evidence
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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.
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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