Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)

🕒 Published (v1): 2026-06-25 13:22 UTC · Source: Arxiv · link

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TL;DR

AURORA-AI is a closed-loop resource orchestration framework that dynamically reallocates computational budget across a heterogeneous population of AI models using Hamilton-Jacobi-Bellman (HJB) feedback control, Lyapunov stability monitoring, and a fairness-aware composite utility. It targets non-stationary deployment conditions where concept drift, demographic bias shocks, and black-swan events degrade both performance and human-centric properties. Against five baselines including PPO, it achieves near-instantaneous recovery from disruptions and improves tail-risk metrics.

Problem

Static resource allocation across heterogeneous AI model populations fails under non-stationary deployment conditions: concept drift erodes performance, demographic shifts amplify fairness gaps, and abrupt disruptions (black-swan events) cause prolonged degradation. Existing adaptive controllers (bandits, PPO) optimize scalar rewards and are blind to the Lyapunov geometry of the system state, producing slow or oscillatory recovery. Fairness and interpretability are treated as post-hoc constraints rather than first-class optimization targets.

Method

AURORA-AI formulates resource allocation as a continuous-time Markov decision process over a state space of resource vectors \(R \in \mathbb{R}^N_{\geq 0}\) and performance scores \(P \in [0,1]^N\). The optimal policy \(\pi^*\) maximizes the discounted composite utility \(J(\pi) = \mathbb{E}\left[\int_0^\infty e^{-\omega t} \sum_k (\alpha P_k(t) - \mu R_k(t))\, dt \mid \pi\right]\), solved via the HJB equation: $\(\omega V = \max_{a \in A}\left(\sum_k (\alpha P_k - \mu R_k) + \mathcal{L}^a V\right)\)$ where \(\mathcal{L}^a\) is the infinitesimal generator of the controlled diffusion governed by the Itô SDE \(dR_k = (-\lambda R_k + \beta P_k)\,dt + \sigma\,dW_k\).

Lyapunov stability is monitored via \(V(\theta, t) = \frac{1}{2}\theta^\top P(t)\theta\); the controller redistributes budget to arms where \(\dot{V} < 0\). Catastrophic forgetting is addressed by an Elastic Weight Consolidation penalty \(\mathcal{L}_{\text{EWC}} = \mathcal{L}_{\text{task}} + \frac{\lambda}{2}\sum_i F_i(\theta_i - \theta_i^*)^2\). Demographic fairness is enforced via an exponentially decaying bias term \(\dot{b}(t) = -\kappa b(t) + \nu \sum_i w_i \partial\mathcal{L}_{\text{fair}}/\partial\theta_i\). Tail risk is measured with the super-quantile \(\bar{q}_\alpha = \frac{1}{1-\alpha}\int_\alpha^1 q_X(\beta)\,d\beta\).

Evaluation uses a discrete-time simulation (\(N=5\) models, \(T=350\) steps, 500 samples/step) with three concurrent stressors: demographic bias shock (\(t \in [120,190]\)), gradual concept drift, and an abrupt black-swan degradation at \(t=160\).

Key Contributions

• HJB-based closed-loop orchestration policy that redistributes computational budget across heterogeneous AI models in real time under non-stationary conditions.
• Fairness-aware composite utility jointly optimizing predictive performance, demographic parity, cost, latency, robustness, and interpretability.
• Stress-rich simulation environment with concurrent bias shocks, concept drift, and black-swan disruptions.
• Resilience evaluation methodology using \(\alpha\)-quantile, super-quantile, recovery time, and Lyapunov-stability fraction as metrics.

Results

• Recovery time: AURORA-AI recovers within 1 time step vs. 88 steps (Static) and 22 steps (PPO).
• \(\alpha\)-quantile: 0.7666 vs. 0.5942 (Static) — +29.0%.
• Super-quantile \(\bar{q}_\alpha\): 0.6729 vs. 0.5378 (Static) — +25.1%.
• Mean demographic parity gap: 0.1246 (AURORA-AI) vs. 0.1386 (Static); maximum gap 0.3844 vs. 0.4562.
• Lyapunov-stable steps: 46.99% vs. 43.27% (Static).
• Mean explainability score: 0.7442 vs. 0.7040 (Static) — +5.7%.
• Mean performance: 0.8689 vs. 0.8559 (Static); both near 0.92 under nominal conditions.
• Lyapunov energy dissipation: post-shock energy collapses in ~20 steps (AURORA-AI) vs. ~70 steps (Static).
• All five baselines (Static, Round-Robin, Greedy, LinUCB, PPO) dominated across the full trajectory.

Limitations

• Validation is purely simulation-based; no hardware-in-the-loop or real deployment experiments.
• The simulation uses only \(N=5\) models in a single-run setup; statistical significance across seeds is not reported.
• The HJB solution assumes a compact state space with continuous transition coefficients; scalability to large model populations is unaddressed.
• Fairness metric is limited to equalized odds / demographic parity gap between two groups; intersectional or multi-class fairness is not covered.
• The decentralized multi-agent extension (multiple orchestrators) is deferred to future work.
• Real-world latency, jitter, and packet-loss interactions with the HJB feedback law are not characterized.

Relevance to Agentic AI / LLM Agents

AURORA-AI addresses a core infrastructure problem for multi-agent LLM deployments: how to dynamically allocate compute budgets across a population of heterogeneous models when conditions drift and black-swan failures occur. The HJB + Lyapunov framework provides formal stability guarantees that pure RL controllers (PPO) lack, making it relevant for building resilient agent orchestration layers where model switching, fallback routing, and fairness-aware selection are needed. The bias-decay and EWC components directly target non-stationarity and forgetting — persistent challenges in long-horizon agentic settings. Practitioners building multi-model agent pipelines (router → specialized sub-agents) can draw on this as a theoretically grounded alternative to heuristic load-balancing.