Evidence-Bound Autonomous Research (EviBound): A Governance Framework for Eliminating False Claims

• URL: http://arxiv.org/abs/2511.05524v1
• Date: Tue, 28 Oct 2025 17:47:13 GMT
• Title: Evidence-Bound Autonomous Research (EviBound): A Governance Framework for Eliminating False Claims
• Abstract summary: EviBound is an evidence-bound execution framework that eliminates false claims through dual governance gates.<n>The pre-execution Approval Gate validates acceptance criteria schemas before code runs.<n>The post-execution Verification Gate validates artifacts via MLflow API queries.
• Score: 0.609170287691728
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-based autonomous research agents report false claims: tasks marked "complete" despite missing artifacts, contradictory metrics, or failed executions. EviBound is an evidence-bound execution framework that eliminates false claims through dual governance gates requiring machine-checkable evidence. Two complementary gates enforce evidence requirements. The pre-execution Approval Gate validates acceptance criteria schemas before code runs, catching structural violations proactively. The post-execution Verification Gate validates artifacts via MLflow API queries (with recursive path checking) and optionally validates metrics when specified by acceptance criteria. Claims propagate only when backed by a queryable run ID, required artifacts, and FINISHED status. Bounded, confidence-gated retries (typically 1-2 attempts) recover from transient failures without unbounded loops. The framework was evaluated on 8 benchmark tasks spanning infrastructure validation, ML capabilities, and governance stress tests. Baseline A (Prompt-Level Only) yields 100% hallucination (8/8 claimed, 0/8 verified). Baseline B (Verification-Only) reduces hallucination to 25% (2/8 fail verification). EviBound (Dual Gates) achieves 0% hallucination: 7/8 tasks verified and 1 task correctly blocked at the approval gate, all with only approximately 8.3% execution overhead. This package includes execution trajectories, MLflow run IDs for all verified tasks, and a 4-step verification protocol. Research integrity is an architectural property, achieved through governance gates rather than emergent from model scale.

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