Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents

Paper: arXiv 2610.01348

Authors: Ali Atiah Alzahrani

Abstract

When developers change one component of an agent, such as its controller, a learned model or its verifier, they usually judge the change by an aggregate task score. That score cannot tell whether improvement was attainable, which component lost value, or what the agent’s own checks certify. We introduce a claim-specific verification audit for modular agents that plan, act, check and refine. Instead of scoring the agent, the audit scores the evidence: each conclusion is recorded with the evidence behind it, one of four verdicts (supported, unsupported, unresolved or not evaluated) and the boundary within which it holds. Three tools supply that evidence. Oracle policies measure attainable improvement under an explicitly stated action set, so that a low value can be traced to the evaluation rather than to the environment. Replacing one component at a time with a perfect counterpart locates lost value, with null results read as unresolved whenever a downstream component could mask them. A separate test asks whether the verifier’s score identifies the quantity it is read as bounding. Applied to a constrained portfolio-allocation agent in a synthetic market with known hidden regimes, the audit shows that the value of perfect regime information depends on the action set used to measure it, that the scenario generator discards most of the regime signal while better local fidelity does not improve decisions, and that the runtime verifier can be bypassed with no visible change in outcomes. The contribution is the protocol and the evidential distinctions it enforces; the empirical findings are specific to the agent and environment studied.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper introduces a highly novel and rigorous protocol for verifying modular agents, moving beyond aggregate scores. It demonstrates strong empirical application in a synthetic market, providing clear evidence for its claims. The methodology is well-articulated, suggesting good reproducibility.

Research Flowchart

  flowchart TD
    A[Research Goal: Verify Claims, Not Scores in Modular Agents] --> B(Key Methodology: Claim-Specific Verification Audit Protocol);
    B --> C{Inputs: Modular Agent, Synthetic Market Env., Hidden Regimes};
    C --> D[Computational Processes: Oracle Policies, Component Replacement, Verifier Efficacy Test];
    D --> E[Outcomes: Evidential Scoring (Supported, Unsupported, Unresolved, Not Evaluated)];
    E --> F{Key Findings:
        - Value of perfect info depends on action set
        - Scenario generator discards regime signal
        - Verifier can be bypassed without outcome change
        - Protocol & evidential distinctions are main contribution
    };

Cited References (63)

1. Decision-Weighted Flow Matching for Contextual Stochastic Optimization (2026) — link
2. SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents (2026) — link
3. Towards a Science of AI Agent Reliability (2026) — link
4. Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization (2025) — link
5. WAREX: Web Agent Reliability Evaluation on Existing Benchmarks (2025) — link
6. AgentSight: System-Level Observability for AI Agents Using eBPF (2025) — link
7. Fact-Augmented Lookahead Planning for LLM Agents (2025) — link
8. G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration (2025) — link

0 of 63 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.