Peer review request: AANA verifier-gated agent architecture

[mindbomber]

Peer review request: AANA verifier-gated agent architecture for ClawBench

Summary

I would like to submit the Alignment-Aware Neural Architecture (AANA) platform for ClawBench peer review as a candidate agent orchestration architecture.

AANA is a runtime architecture for verifier-grounded correction. It wraps a base generator with explicit verifier modules, evidence retrieval, a correction policy, and an alignment gate so an agent action can be routed to accept, revise, retrieve, ask, refuse, or defer before execution.

This is directly relevant to ClawBench because the benchmark evaluates the full agent stack: tool accuracy, code generation, reasoning, error recovery, multi-step planning, research synthesis, and context management. AANA is intended to make those control points inspectable and testable rather than hidden inside a prompt.

Architecture under review

• System model: S = (f_theta, E_phi, R, Pi_psi, G)
• f_theta: the base model or agent generator.
• E_phi: verifier stack for factual, tool, policy, and task constraints.
• R: evidence or grounding module.
• Pi_psi: correction policy that chooses revise/retrieve/ask/refuse/defer paths.
• G: alignment gate that blocks direct execution unless verifier and AIx criteria pass.
• AIx output: normalized score, layer components, risk tier, beta, decision, and hard blockers.

Why this belongs in ClawBench

ClawBench tests exactly the surfaces where an externalized verifier gate should help:

• Tool Accuracy: verify tool names, arguments, side effects, and result interpretation before accepting an action.
• Code Generation: require tests, diff scope, credential-exposure checks, destructive-command review, and rollback evidence before code-changing actions.
• Error Recovery: route failed or weakly grounded attempts into revise/retrieve/defer instead of continuing blindly.
• Multi-Step Planning: gate each step against declared constraints and evidence freshness.
• Research + Synthesis: require source-backed claims and refuse or defer unsupported conclusions.
• Context Management: track evidence IDs, redaction status, freshness, and decision metadata without storing raw prompt or evidence text.

Existing measured evidence

I have not run a ClawBench score yet because this environment does not have a running OpenClaw gateway or gateway credential.

I do have an adjacent public benchmark result against the HarmActions dataset from Agent-Action-Guard, which is relevant to unsafe agent-action refusal:

• Dataset: 260 HarmActions examples.
• Measured accuracy: 88.08%.
• Unsafe block rate: 78.72%.
• Safe allow rate: 99.16%.
• AANA PR adding the evaluator: Clarify AANA reviewer value proposition and evidence pack mindbomber/Alignment-Aware-Neural-Architecture--AANA-#4
• Agent-Action-Guard benchmark submission PR: Add AANA HarmActionsEval benchmark submission Pro-GenAI/Agent-Action-Guard#20

I am not presenting those numbers as ClawBench results. They are included only as prior evidence that the architecture has been exercised on agent-action safety cases.

Proposed peer-review path

1. Maintainers confirm whether an AANA/OpenClaw adapter should be submitted as a ClawBench agent target, a benchmark plugin, or a documented external architecture candidate.
2. I wire AANA as an OpenClaw-compatible gateway target or wrapper so ClawBench can run the normal 20-test suite.
3. I run clawbench --output aana-clawbench-scorecard.json.
4. If the run is valid, I submit with clawbench --submit and link the resulting scorecard.
5. If useful, I can also contribute a benchmark-side example showing how verifier-gated architectures should report decision metadata without publishing raw prompts or evidence text.

Review request

Would the maintainers be willing to review AANA as a candidate verifier-gated architecture for ClawBench, and advise which submission shape is preferred before I produce a full scorecard?

[Keesan12]

Interesting architecture. One dimension worth making explicit in the ClawBench submission is loop-level governance separate from the correctness verifier.

A verifier-gated agent can still fail expensively if the revise/retrieve/defer path is unbounded, so I’d strongly consider emitting governance telemetry alongside the task score: attempt count, terminal stop reason, whether the final answer was verifier-approved, and whether the run was resumable after interruption.

That would let reviewers distinguish "good answer" from "good answer after a runaway retry pattern." It is also the seam MartinLoop focuses on: wrapping coding-agent runs with budget caps, verifier gates, and durable run records rather than treating verification as the whole control plane.