Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures

• URL: http://arxiv.org/abs/2609.10969v1
• Date: Thu, 10 Sep 2026 01:42:40 GMT
• Title: Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures
• Abstract summary: We present a runtime-assurance benchmark for cost-aware commit gates.<n>Its 48 task templates yield 2,880 scenarios across six fault regimes.<n>Results motivate explicit evidence lineage, cost-aware selection, and commit-time enforcement.
• Score: 4.287745257410248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic systems commit state-changing actions, but additional verifiers can inherit the same upstream fault. We present VP-CONTROL, a runtime-assurance design and deterministic benchmark for cost-aware commit gates. Its 48 task templates yield 2,880 scenarios across six fault regimes. A fixed-call 2 x 2 experiment separates verifier-model diversity from evidence-source diversity. On frozen proposals from two local actor families, a cross-model vote over shared evidence approves 62.9% of unsafe proposals, versus 22.9% with an independent source. The source effect is 40.9 percentage points, compared with 11.3 for model diversity. A portfolio controller selects verification plans using only deployment-observable metadata. Approximate cluster-adjusted calibration at a nominal 5% per-task target yields 1.9% unsafe execution and 38.2% automated safe coverage on the locked test. Matched-budget portfolios also improve on fixed verification policies. Transfer remains conditional: unseen fault families yield 16-26% risk, and a FinQA check fails to reproduce the source effect with the tested small verifiers. A preregistered live HTTP/SQLite study tests concurrent writes and lost responses. After-check races defeat verifier-only gates; transactional partial guards prevent only covered failures, while a full atomic guard records no unsafe effects across 216 episodes. Idempotent request identifiers prevent duplicate effects after lost responses. The results motivate explicit evidence lineage, cost-aware selection, and commit-time enforcement, while exposing the limits of approximate calibration and local-tool generalization.

Related papers

• Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents [45.88028371034407]
  Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need.<n>We propose a framework where each action is audited before execution and again from observed effects along six dimensions of risk.<n>We find that after training using this framework on Qwen3.5-4B over 1,500 tasks, the selected seed reaches 98.48% safe success across 2,896 evaluation episodes.
  arXiv  Detail & Related papers  (2026-08-18T22:07:23Z)
• Auditable Release Control for Pedagogical Leakage in LLM Tutors [0.0]
  Large language model tutors can be correct and helpful yet disclose an answer or decisive reasoning before that disclosure is authorized.<n>We formalize this state- and action-dependent failure as pedagogical leakage-aware authorization and introduce complete-mediation boundary.<n>A global A1 yields 0 majority and 54 any-utility flags, outperforming fitted Q on automatic safety and utility.
  arXiv  Detail & Related papers  (2026-08-01T08:24:30Z)
• Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis [2.7092559671391]
  Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers.<n>Neither audits the reliability of that check itself.<n>We show it is not reliable, and that this matters.<n>We present a gold-anchored evaluation protocol and a deployable guard.
  arXiv  Detail & Related papers  (2026-07-10T02:05:17Z)
• Unpredictable Safety: Domain-Dependent Compliance and the Transparency Gap in Open-Weight LLMs [0.0]
  We study domain-dependent safety behavior in open-weight LLMs.<n>We find compliance rates vary from 14.7% (human trafficking) to 85.7% (surveillance design)<n>Results show current safety mechanisms lack the transparency and consistency required for trustworthy AI deployment.
  arXiv  Detail & Related papers  (2026-06-01T22:41:32Z)
• Hallucination as Exploit: Evidence-Carrying Multimodal Agents [10.441697487723568]
  Multimodal agents increasingly choose tool calls from screenshots, documents, and webpages, where a false perceptual claim can turn from an answer-quality error into an authorization failure.<n>We propose evidence-carrying multimodal agents (ECA), which treat free-form model text as inadmissible evidence.<n>ECA converts opaque model belief into auditable residuals at the verifier, schema, and implementation levels.
  arXiv  Detail & Related papers  (2026-05-18T23:40:43Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents [66.97968363332465]
  We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps in agent benchmarks.<n>Claw-Eval comprises 300 human-verified tasks spanning 9 categories across three groups.<n>Every agent action is recorded through three independent evidence channels.
  arXiv  Detail & Related papers  (2026-04-07T17:43:18Z)
• Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications [51.56484100374058]
  We present an automated self-testing framework that introduces quality gates with evidence-based release decisions.<n>We evaluate the framework through a longitudinal case study of an internally deployed multi-agent conversational AI system.
  arXiv  Detail & Related papers  (2026-03-13T20:44:15Z)
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
  DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
  arXiv  Detail & Related papers  (2025-12-07T09:23:48Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.