Self-Evolving Multi-Agent Evaluation

• Multi-Agent Self-Evolving Evaluation is a paradigm where agents continuously monitor and repair their own policies using closed-loop feedback and introspection.
• The architecture employs a MAPE-K loop with techniques like trajectory optimization, LLM-driven error handling, and DFA-based checks to ensure runtime safety and resilience.
• Empirical studies show that such self-healing systems significantly reduce safety violations and recovery times, improving overall system robustness.

Multi-Agent Self-Evolving Evaluation refers to a class of runtime frameworks and algorithmic architectures where an agent or set of agents not only act in an environment but also evaluate, diagnose, and autonomously repair their own policies or code via closed-loop feedback. This paradigm augments traditional agent-based or learning systems with meta-level mechanisms for introspection, error detection, and iterative improvement, resulting in self-healing capabilities. Research in this area builds multilayer architectures, integrates runtime data with adaptation targets, and exploits trajectory optimization, reinforcement learning, LLMs, or formal policy automata to drive self-improvement based on observed faults or failures (Zhou et al., 2020, Cruz, 8 Dec 2025, Sun et al., 2024, Anugula et al., 4 Dec 2025, Riganelli et al., 2017, Sánchez et al., 2015, Chen et al., 2022).

1. Core Concepts and Definitions

At the foundation of multi-agent self-evolving evaluation is the concept of an agent that supervises, corrects, or repairs not only its immediate behaviors but also its underlying policy or logic based on runtime feedback. This may be formalized as a self-healing policy loop or runtime self-healing control loop: an iterative process comprising four or more architectural phases—monitoring, diagnosis or analysis, adaptive planning, and execution—typically aligned with the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) loop in autonomic computing.

Agents may act at the task level (performing external actions) or as reflective siblings, focusing solely on behavioral logs and meta-maintenance (e.g., VIGIL’s reflective runtime (Cruz, 8 Dec 2025)). Policy repair targets are naturally parameterized controllers (differentiable neural networks, controllers, or policy automata) whose parameters or code can be minimally perturbed to avoid future errors while retaining high performance (Zhou et al., 2020).

Self-evolving evaluation is thus characterized by:

• Continuous or episodic runtime diagnosis of behavioral faults or failures via monitoring/logging, model-based simulation, or formal correctness checking;
• Automated generation of remediation—policy gradient steps, code patches, or structural adaptations—targeted at the root cause;
• Systematic application, validation, and logging of the adaptation, enabling future cycles to reduce the intervention rate or error frequency.

2. Architectural Patterns and Loop Workflow

A prototypical multi-agent self-evolving evaluation system deploys a closed feedback architecture realized in several forms:

• Policy Repair Loop: A high-performance learning-based policy $\pi_\theta$ is paired with a runtime safety monitor (simulator and predicate checker) and a model-based safety controller $\pi_\mathrm{safe}$, all within a self-healing loop. Upon detecting predicted safety violation, the safety controller overrides the action, and the resulting state-action pairs are used to periodically fine-tune $\pi_\theta$ via trajectory optimization, subject to minimal deviation constraints (Zhou et al., 2020).
• Reflective Runtime Supervision: An external "reflector" agent supervises sibling agents via event logs. Logs are appraised (e.g., emotional labeling), aggregated into diagnoses (such as Roses/Buds/Thorns structures), and used to generate guarded prompt/code adaptations and repairs, all under a finite-state orchestration and strict error/guardrail handling (Cruz, 8 Dec 2025).
• LLM-Driven MAPE Loop: For software systems, each unhandled runtime exception triggers a monitor–analyze–plan–execute cycle: exception capture, diagnosis via LLM prompt engineering, synthesis of handling code by the LLM, and isolated code re-injection. Success is measured by proceeding through the error, preserving semantic correctness (Sun et al., 2024).
• Reinforcement Learning Security Agent: Proactive security for DevSecOps pipelines via an RL agent that observes current state, selects remediation actions (patch, rollback, isolate), and updates its policy based on environment response, with the aim of minimizing recovery time and maximizing resilience (Anugula et al., 4 Dec 2025).
• Proactive Libraries and DFA-Based Checkers: Low-level enforcement modules interpose on API calls, use deterministic automata or temporal logic to spot protocol violation, and, upon violation, inject the minimal corrective action to restore compliant state before returning control (Riganelli et al., 2017).

A common flow is depicted below, abstracted across domains:

Step	Function	Example Mechanism
Monitoring	Observe/log actions, events, or metrics	Forward simulation, log hooks, try/catch
Diagnosis	Predict violations/faults via models, automata, or analysis	Trajectory simulation, LLM prompt, DFA
Planning	Generate adaptation to address detected issue	QP optimization, LLM code patch
Execution	Apply adaptation, verify outcomes, store feedback/logs	Policy update, patch injection, rollback
Learning/Repair	Update policy/model/code based on current/accumulated feedback	Fine-tune, Q-learning, autoencoder

3. Formal Algorithms and Theoretical Guarantees

Formulations are algorithmically diverse, but central elements include:

• Trajectory Optimization for Policy Repair: The update step solves a constrained quadratic program (QP):

$$\Delta\theta^k = \mathop{\mathrm{argmin}}_{\Delta\theta}\; J(\theta^k + \Delta\theta) + \alpha\|\Delta\theta\|^2$$

subject to safety on predicted trajectories, with $$J(\theta) = \sum_{(x,u)\in D^k} \|\pi_\theta(x) - u\|^2$$, where $D^k$ is the dataset of intervention corrections. Dynamics and policy are linearized around nominal trajectories, typically solved via iLQR (Zhou et al., 2020).

• Reinforcement Learning for Security: The policy is optimized by deep Q-learning, with the environment formulated as a POMDP:

$$Q^*(s,a) = \mathbb{E}[r + \gamma \max_{a'} Q^*(s',a') | s, a]$$

Actions are parameterized playbooks (patch, isolate, etc.), and rewards encode uptime, mitigation success, and operational cost (Anugula et al., 4 Dec 2025).

• Runtime LLM Error Handling: The healing action $a_t$ is generated as

$S_{t+1} = f(S_t, e_t, a_t)$

where $f$ is the code execution effect of LLM-generated patch $a_t$ applied in state $S_t$ with exception $e_t$ (Sun et al., 2024).

• DFA-Driven Healing: Policy automata $A$ process event trace $\tau$; on violation, select and execute minimal healing action $h$ so that $P(\tau \cdot h) = \mathrm{true}$ (Riganelli et al., 2017).

Key theoretical guarantees include:

• Performance degradation bounds for naively switching to backup controllers can be quadratic in time horizon, but minimal-deviation repair provably bounds impact (Zhou et al., 2020).
• Convergence properties: Each adaptation step strictly reduces measured faults or violations under conditions of monotonicity and guarded pipeline execution (Cruz, 8 Dec 2025, Anugula et al., 4 Dec 2025).

4. Empirical Evaluation and Benchmarking

Empirical results validate practical efficacy and tractability:

• Policy Repair in Control: In the MountainCar environment, minimal-deviation repair maintained near-optimal steps to the goal while eliminating all safety violations. In high-fidelity simulation (CARLA), repaired policies preserved smooth trajectories, abolishing runtime interventions (Zhou et al., 2020).
• Reflective Agent Runtime: In the VIGIL reminder-latency case, meta-level diagnosis and repair reduced mean latency from 97s to ≈8s and eliminated recurrent error triggers (Cruz, 8 Dec 2025).
• LLM Self-Healing: In code benchmarks, GPT-4 achieved a 72.8% post-healing proceed rate over diverse runtime errors. Latency per patch was 1.6–3.1s. Fine-tuned models further increased recovery (Sun et al., 2024).
• Autonomous Security: AutoGuard in DevSecOps improved detection accuracy to 95.6%, reduced mean time to recovery to ≈82s, and attained a 38% reduction in false positive rate compared to conventional detection systems (Anugula et al., 4 Dec 2025).
• Android Library Enforcement: Proactive modules in Android apps healed 89% of tested resource-use misuses, at only 1.2ms average per-call overhead (Riganelli et al., 2017).

5. Limitations, Scalability, and Open Questions

Current systems exhibit several open challenges and constraints:

• Model Dependence and White-Box Constraints: Effective trajectory optimization-based repair requires differentiable, white-box policies and at least approximate models of system dynamics or correct behavior (Zhou et al., 2020).
• Computational Cost: Runtime or episodic repair may involve QP solution, code synthesis, or training episodes, impacting system latency and resource usage. Practical deployments employ batching, offline repair, or efficient iLQR variants to maintain tractability (Zhou et al., 2020, Sun et al., 2024).
• Generality and Policy Coverage: Library policy enforcement is limited by the expressiveness and coverage of hand-crafted automata or logical predicates. Complex, cross-library, or dynamically inferred policies remain challenging (Riganelli et al., 2017).
• Security and Trust: LLM-generated code patches and runtime injection frameworks must guard against secondary vulnerabilities or malicious code generation through sandboxing and vetting (Sun et al., 2024).
• Scalability of Multi-Policy Coordination: As the number of policies or intervening modules grows, state explosion and interactions may cause coverage gaps or unintended side effects (Riganelli et al., 2017).
• Meta-Failure and Self-Adaptation: Meta-level supervisory frameworks such as VIGIL demonstrate the ability to diagnose and repair their own internal toolchain upon errors, but require robust state-guarded pipelines and explicit fallback logic (Cruz, 8 Dec 2025).

6. Domain Variants and Research Directions

Instantiations of self-evolving evaluation exist across domains:

• Safety-Critical Control: Trajectory optimization-driven repair for safety-critical controllers in robotics and autonomous navigation, emphasizing minimal deviation and formal guarantees (Zhou et al., 2020).
• Agentic LLM Supervisors and Reflective Runtimes: Layered reflective agents synthesizing prompt/code repairs based on behavioral logs and structured diagnosis (Cruz, 8 Dec 2025).
• LLM-Based Software Repair: MAPE loops driven by LLM planning for code error recovery, supporting both zero-shot and fine-tuned strategies (Sun et al., 2024).
• Autonomous Security Agents: RL-based security agents for adaptive, proactive incident mitigation and resilience in cloud and DevSecOps pipelines (Anugula et al., 4 Dec 2025).
• Library Enforcement: Lightweight automata-based runtime healing in the context of API misuse, especially on resource-constrained mobile platforms (Riganelli et al., 2017).
• Fault Management in SDN: Bayesian network-informed diagnosis, ruled-based planning, and automated recovery for network controllers and services (Sánchez et al., 2015).
• Neural Network Robustness: Closed-loop optimal control embeddings to detect/correct off-manifold deviation under perturbation, increasing adversarial robustness (Chen et al., 2022).

Active research explores automated policy inference, deeper integration of continuous learning, formal verification of the self-healing loop itself, and extensions to high-dimensional, model-free, or multi-agent cooperative/competitive environments.

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References:

• "Runtime-Safety-Guided Policy Repair" (Zhou et al., 2020)
• "VIGIL: A Reflective Runtime for Self-Healing Agents" (Cruz, 8 Dec 2025)
• "LLM as Runtime Error Handler: A Promising Pathway to Adaptive Self-Healing of Software Systems" (Sun et al., 2024)
• "AutoGuard: A Self-Healing Proactive Security Layer for DevSecOps Pipelines Using Reinforcement Learning" (Anugula et al., 4 Dec 2025)
• "Policy Enforcement with Proactive Libraries" (Riganelli et al., 2017)
• "POSTER: Self-Healing Mechanisms for Software-Defined Networks" (Sánchez et al., 2015)
• "Self-Healing Robust Neural Networks via Closed-Loop Control" (Chen et al., 2022)