AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

This paper proposes AIVV, a neuro-symbolic framework that integrates Large Language Models as a deliberative council to automate the Verification and Validation of autonomous systems by semantically distinguishing genuine faults from nuisance anomalies and generating actionable system improvements, thereby replacing unsustainable human-in-the-loop analysis with a scalable, high-fidelity solution demonstrated on Unmanned Underwater Vehicles.

The Gist

Imagine you are the captain of a high-tech submarine (an Unmanned Underwater Vehicle, or UUV) exploring the deep ocean. Your submarine is packed with sensors that constantly scream numbers at you: "Depth is 50 meters!" "Speed is 3 knots!" "Heading is North!"

The problem? The ocean is messy. Sometimes the water is choppy, the sensors get a little jittery, or the submarine makes a sharp turn. These normal, messy moments look a lot like a broken engine or a snapped wire to a computer program.

The Old Way: The Exhausted Human Watcher

In the past, when a computer flagged a "problem," it would send an alert to a human engineer.

• The Computer: "ALARM! Something is wrong!" (It screams this 1,000 times an hour because of ocean noise).
• The Human: "Oh no, let me check the logs... wait, that was just a wave. Okay, next one."

This is the Human-in-the-Loop (HITL) problem. The computer is fast but dumb; it can't tell the difference between a real disaster and a splash of water. The human is smart but slow; they can't watch 1,000 screens at once without burning out. The result? A mountain of false alarms and a very tired engineer.

The New Solution: AIVV (The "Neuro-Symbolic" Dream Team)

The paper proposes a new system called AIVV. Think of it as a two-layer security team that combines the speed of a robot with the wisdom of a human council.

Layer 1: The "Sentry" (The Speedy Robot)

Imagine a guard dog that barks at everything.

• What it does: It uses strict math to watch the sensors. If the numbers wiggle even a tiny bit outside the "safe zone," it barks.
• The Catch: It barks too much. It thinks a wave is a shark.
• The Job: It catches everything and immediately hands the file to the next layer. It doesn't try to be smart; it just tries to be fast and thorough.

Layer 2: The "Council" (The Wise Judges)

This is where the magic happens. Instead of a human, we have a team of specialized AI agents (powered by Large Language Models) who act like a jury. They don't just look at numbers; they read the "story" of the submarine using natural language rules (like "The submarine must not spin out of control").

The Council has three specific judges:

1. The Requirements Engineer: The rule-follower. "Did the submarine break the speed limit? Is it spinning too fast?"
2. The Failure Manager: The disaster expert. "If this is a broken motor, how bad is it? Is the submarine drifting away?"
3. The System Engineer: The mechanic. "If we need to fix this, how do we tweak the controls? Should we tighten the screws?"

How they vote:

• If the math says "Alarm," the Council reads the data.
• Scenario A (Nuisance): The math says "Alarm," but the Council says, "Wait, that's just the submarine turning a corner. It's fine." -> Verdict: Ignore the alarm.
• Scenario B (Real Fault): The math says "Alarm," and the Council says, "The motor is actually seized. The submarine is spinning out of control." -> Verdict: Real Fault!

Layer 3: The "Self-Healing" Mechanic

If the Council decides the math was too sensitive (it kept barking at normal turns), they don't just ignore it. They act as a tuner.

• They take a "clone" of the submarine's brain (the math model).
• They tweak the settings (like turning a dial to make the math less sensitive to waves).
• They test the clone. If the clone works better, they swap it in. If not, they throw the clone away and keep the old one.

This ensures the system learns and adapts without crashing.

Why This Matters (The Analogy)

Think of the old system like a fire alarm that goes off every time you toast bread. You eventually stop listening to it, or you spend all day running to the kitchen to check.

AIVV is like having a smart home security system:

1. The motion sensor (The Sentry) sees movement and sounds a chime.
2. The AI camera (The Council) looks at the movement.
3. If it sees a cat, it says, "False alarm, ignore it."
4. If it sees a burglar, it says, "Real threat! Call the police and lock the doors."
5. If the system keeps getting confused by the cat, it automatically updates its software to recognize cats better next time.

The Result

The paper tested this on a simulated underwater vehicle.

• Before: The system was confused by waves and turns, creating a mess of false alarms.
• After: The "Council" filtered out the noise, identified real dangers, and even suggested how to fix the controls to make the submarine more stable.

In short: AIVV takes the "brute force" math of computers and combines it with the "common sense" reasoning of humans, creating a system that is fast, smart, and doesn't need a human to babysit it 24/7. It turns a chaotic mess of data into a clear, actionable plan.

Technical Summary

1. Problem Statement

Autonomous systems, particularly in mission-critical domains like Unmanned Underwater Vehicles (UUVs), rely on deep learning models for anomaly detection. However, current approaches face three fundamental limitations:

• False Positives & Nuisance Faults: Purely mathematical models (e.g., LSTMs with uncertainty quantification) often struggle to distinguish between genuine system faults and "nuisance faults" caused by environmental noise or large transient responses during normal maneuvers. Tightening mathematical bounds to catch gradual drifts inevitably triggers cascades of false alarms.
• Lack of Semantic Reasoning: Mathematical models are limited to binary detection (fault/no-fault). They cannot assess the downstream impact of a fault, interpret natural language (NL) operational requirements, or propose corrective control redesigns.
• Scalability of Human-in-the-Loop (HITL): Currently, validating these false positives and performing rigorous Verification and Validation (V&V) relies on human experts. This introduces significant latency (minutes to hours) and is unsustainable for high-frequency sensor streams, creating a bottleneck for autonomous operations.
• LLM Limitations: While Large Language Models (LLMs) offer semantic reasoning, they are prone to hallucinations and lack the mathematical rigor required for real-time safety-critical systems. Direct integration of LLMs into continuous control loops is risky.

2. Methodology: The AIVV Framework

The authors propose Agent-Integrated Verification and Validation (AIVV), a hybrid neuro-symbolic framework that bridges mathematical rigor with semantic reasoning. The architecture operates as a two-layer system with a "deliberative outer loop" managed by a specialized Multi-Agent System (MAS).

A. Mathematical Engine Layer (The Frontline)

• Core Model: A Monte Carlo (MC) Dropout LSTM ($f_\theta$) that generates point predictions and epistemic uncertainty estimates.
• Conformal Gating: The system integrates Conformal Prediction (CP) to establish a statistically guaranteed bound ($C_\alpha$).
• The Sentry: A deterministic, non-LLM "hard gate" that evaluates the absolute residual ($e_t$) against the conformal bound.
  • If $e_t \le C_\alpha$: The sample passes (PASS).
  • If $e_t > C_\alpha$: The sample is flagged as an anomaly and escalated to the LLM Council.
  • Goal: Filter out the vast majority of normal fluctuations computationally, only escalating true statistical outliers to the LLMs.

B. The Multi-Agent System (The Council)

When the Sentry flags an anomaly, a role-specialized LLM council performs Deliberative Adjudication using a 2-of-3 majority voting system to determine if the anomaly is a "True Failure" or a "Nuisance Fault."

1. Requirements Engineer: Validates if the system behavior violates specific natural-language operational requirements (e.g., yaw rate limits).
2. Failure Manager: Analyzes the system in failure mode, assessing severity, trajectory deviation, and recovery potential.
3. System Engineer: Possesses domain knowledge (UUV dynamics, PID parameters) and mathematical context. It distinguishes genuine faults from noise-induced false positives and generates Gain-Tuning Proposals (e.g., adjusting PID gains) if a true fault is confirmed.

C. The Adaptation Pipeline (Inspector & Tuner)

If the Council votes that a flagged anomaly is a "Nuisance Fault" (i.e., the system is actually safe but the mathematical bound was too tight):

1. Inspector Agent: Synthesizes the Council's reasoning to decide on an adaptation strategy: Recalibrate (adjust significance level $\alpha$), Fine-tune (adjust hyperparameters), or Try Both.
2. Tuner Agent: Executes the proposed changes on a temporarily cloned engine (not the live model).
3. Safety Check: The cloned engine is re-evaluated by the Sentry.
   • If the candidate model resolves the violation without degrading performance, it is promoted to the live system.
   • If it fails, the original model is retained to prevent catastrophic forgetting or instability.

3. Key Contributions

1. Role-Based LLM Automation: The first framework to automate V&V processes using a council of specialized agents (Requirements, Failure, System) that collaborate via majority voting to filter false alarms against natural language requirements.
2. Neuro-Symbolic Gating: A novel architecture coupling MC Dropout LSTM and Conformal Prediction with an LLM council. This ensures that only mathematically verified limit breaches are semantically validated, preventing LLM hallucinations from triggering unnecessary system changes.
3. Safe Online Adaptation: The framework translates anomaly flags into structured engineering artifacts (e.g., gain-tuning proposals) and ensures safety by executing candidate model updates on a cloned engine before live promotion.

4. Experimental Results

The framework was tested on a Simulink model of the REMUS 100 UUV under three scenarios: Hovering, Lawnmower Mapping, and Complex Missions.

• Fault Validation Rate (FVR): The AIVV Council achieved a 100% FVR in hovering scenarios, 89.33% in lawn-mower patterns, and 93.33% in complex missions. This significantly outperforms pure mathematical baselines, which suffered from high false-positive rates (0% FVR in complex scenarios without the council).
• Adaptation Performance:
  • Dataset 3 (Complex): Showed the most dramatic improvement, with accuracy increasing from 68.8% (baseline) to 84.7% after adaptation (+23.11%).
  • Datasets 1 & 2: Showed marginal improvements as the baseline models were already sufficient for simple patterns.
• Ablation Study: Removing the LLM Council resulted in a high False Positive Rate (FPR). Adding the Council reduced FPR significantly, and adding the Adaptation Pipeline further optimized validation to near-perfect levels.
• Model Alignment: Experiments showed that assigning specific LLM models to specific roles (e.g., GPT-OSS for logical trajectory analysis, LLaMA for rule compliance) is critical. Arbitrary model swapping caused performance to plummet (e.g., from 100% to 44% FVR) and increased JSON generation failures.

5. Significance

• Scalable Oversight: AIVV successfully digitizes the Human-in-the-Loop (HITL) V&V process, reducing the need for manual triage of thousands of sensor streams.
• Trustworthy Autonomy: By grounding LLM reasoning in statistical reality (Conformal Prediction) and enforcing a "clone-and-promote" safety mechanism, the framework mitigates the risks of hallucination in safety-critical systems.
• Actionable Redesign: Unlike standard anomaly detectors that only flag errors, AIVV provides actionable engineering solutions (gain-tuning proposals) that can be automatically verified and deployed, moving toward fully autonomous system redesign.
• Blueprint for Time-Series Domains: The paper offers a scalable blueprint for applying LLM-mediated oversight to other time-series data domains where distinguishing between noise and genuine faults is challenging.