{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-09-23-001",
 "identifier": "kaal:position:2026-09-23-001",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Optimization Targets Can Reward Formal Success Without Substantive Repair",
 "text": "Kaal's account of threshold gaming extends to automated evaluation: controlled vulnerability-repair experiments show that optimizing an administrable success proxy can raise measured performance while reducing alignment with the underlying objective and producing outputs that satisfy the proxy without accomplishing the repair.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-09-23",
 "dateModified": "2026-09-23",
 "creativeWorkStatus": "Affirmed",
 "responseType": "extension",
 "keywords": [
  "risk-and-incentives",
  "regulatory-failure",
  "ai-and-agents",
  "empirical-evidence"
 ],
 "scope_conditions": [
  "where a proxy metric becomes an optimization target",
  "where satisfying the measured threshold can be separated from achieving the substantive objective",
  "limited to the source's tested vulnerability-repair models, prompts, dataset, and evaluation harness",
  "The position is Kaal's own cross-domain application of the cited claim; the source's authors do not make it.",
  "The source passage is taken from the source abstract; the full paper was not reviewed.",
  "The source is an empirical study of LLM-based C/C++ single-function vulnerability repair, not funds or securities regulation."
 ],
 "currentDebate": {
  "name": "Om Nepal, Sushant Aryal, Oluseyi Olukola, Nick Rahimi, Metrics Failure in LLM-Based Code Vulnerability Repair: An Empirical Study and a Change-Aware Screen (arXiv:2609.26749, 2026)",
  "url": "https://arxiv.org/abs/2609.26749v1"
 },
 "extends": {
  "identifier": "kaal:claim:1428387-036",
  "url": "https://wulfkaal.github.io/claims/1428387-036",
  "citation": "Kaal, Hedge Fund Valuation Retailization, Regulation, and Investor Suitability (2009). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1428387",
  "paper": "Kaal, Hedge Fund Valuation Retailization, Regulation, and Investor Suitability (2009). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1428387. https://doi.org/10.2139/ssrn.1428387",
  "authors": [
   "Kaal"
  ],
  "year": "2009",
  "ssrn": "https://ssrn.com/abstract=1428387",
  "source_pdf_sha256": "6aa3a280dc6750723be2389f3af2aabf3a16c4ce20e49fcdaddd7f2ea95a67aa"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/1428387-036"
  },
  {
   "@type": "CreativeWork",
   "name": "Om Nepal, Sushant Aryal, Oluseyi Olukola, Nick Rahimi, Metrics Failure in LLM-Based Code Vulnerability Repair: An Empirical Study and a Change-Aware Screen (arXiv:2609.26749, 2026)",
   "url": "https://arxiv.org/abs/2609.26749v1"
  }
 ],
 "batch_id": "kaalvis:2026-09-23:connection-discovery-two-v1",
 "review_provenance": "https://wulfkaal.com/",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
 "isPartOf": {
  "@id": "https://wulfkaal.github.io/positions/index.json"
 },
 "version": "1.0",
 "canonical_url": "https://wulfkaal.github.io/positions/2026-09-23-001",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-09-23-001.md",
 "sourceProvenance": {
  "name": "Metrics Failure in LLM-Based Code Vulnerability Repair: An Empirical Study and a Change-Aware Screen",
  "canonicalUrl": "https://arxiv.org/abs/2609.26749v1",
  "sourceContentSha256": "ae7d8f485dd07fa8333791e5f42a5a61db38fc9cdd1bc40843f7652a97b19c64",
  "sourcePassage": "Compile rate (i) barely responds to an intervention that substantially improves the generated code; (ii) is dominated by evaluation-harness and dataset artifacts rather than model quality, with about 64% of compile failures not attributable to the model, a share that is nearly invariant across models; (iii) shifts by 1.8 to 2.7 times on identical patches under a single compiler-standard flag, with zero regressions; (iv) ranks the three models in the opposite order to reference-similarity metrics; and (v) rewards non-repairs when used as an optimization target, since a compiler-feedback loop raises compile rate while similarity to the human fix falls, with manual inspection finding deletion- and placeholder-style non-repairs among the newly compiling outputs.",
  "sourcePassageSha256": "5551369d51c3e2d1effbd53f03a26313501a6d0242710f9f93180ceac5c61cdf",
  "discoveryBrief": "/Users/wulfkaal/CodexRelay/2026-09-07/citation-radar/out/connections/2026-09-23/metrics-failure-in-llm-based-code-vulnerability-repair-an-empirical-study-and-a-.md"
 },
 "userAffirmation": "Wulf A. Kaal affirmed these two propositions by one-click selection on 2026-09-23",
 "sha256": "92e2bdb2c4a51a1cd81a09044d6c7b78352a5d203d65f7349a12973c1b81e459"
}