[Paper] POLARIS: Is Multi-Agentic Reasoning the Next Wave in Engineering Self-Adaptive Systems?
Source: arXiv - 2512.04702v1
Overview
The paper “POLARIS: Is Multi‑Agentic Reasoning the Next Wave in Engineering Self‑Adaptive Systems?” proposes a new three‑layer architecture that blends lightweight monitoring, explainable AI reasoning, and meta‑learning to give software systems the ability to anticipate and evolve their own adaptations. By treating adaptation as a collaborative multi‑agent problem, the authors argue that we are moving from reactive “Self‑Adaptation 1.0/2.0” toward a more proactive “Self‑Adaptation 3.0” capable of handling the “unknown unknowns” that plague today’s highly interconnected ecosystems.
Key Contributions
- POLARIS framework – a three‑tier, multi‑agent architecture (Adapter, Reasoning, Meta) that unifies monitoring, planning, verification, and continual policy improvement.
- Tool‑aware, explainable agents – reasoning agents that generate adaptation plans, validate them against system models, and expose their decision rationale to developers.
- Meta‑learning layer – a knowledge‑base that records adaptation episodes and automatically refines policies using experience‑driven learning.
- Empirical validation – prototype implementations on two benchmark self‑adaptive systems (SWIM and SWITCH) showing consistent performance gains over state‑of‑the‑art baselines.
- Conceptual shift – articulation of “Self‑Adaptation 3.0” as a paradigm where AI and adaptive control co‑evolve, mirroring the transition from Software 1.0 → 2.0 → 3.0.
Methodology
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Layered decomposition
- Adapter layer: ultra‑low‑latency agents that ingest telemetry, enforce safety guards, and trigger adaptation requests.
- Reasoning layer: a suite of domain‑specific agents equipped with symbolic planners and probabilistic models; they propose candidate adaptation plans, simulate outcomes, and produce human‑readable explanations.
- Meta layer: a centralized experience repository that logs context, actions, and outcomes; reinforcement‑learning‑style algorithms mine this data to suggest policy updates for the lower layers.
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Shared knowledge graph – all agents read/write to a common ontology that captures system goals, constraints, and environmental assumptions, enabling consistent reasoning across distributed components.
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Verification‑in‑the‑loop – before a plan is enacted, a lightweight model‑checking step validates that the plan respects safety invariants, preventing catastrophic mis‑adaptations.
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Evaluation setup
- SWIM (a smart‑water‑infrastructure manager) and SWITCH (a micro‑service orchestration platform) were instrumented with POLARIS and compared against rule‑based controllers and pure reinforcement‑learning agents.
- Metrics: adaptation latency, goal‑achievement rate, resource overhead, and resilience to injected “unknown unknown” disturbances (e.g., sudden network partitions, sensor failures).
Results & Findings
| Metric | POLARIS vs. Rule‑Based | POLARIS vs. Pure RL |
|---|---|---|
| Adaptation latency | ~30 % faster (thanks to the low‑latency Adapter) | Comparable, but with higher predictability |
| Goal‑achievement | 92 % vs. 78 % (SWIM) / 89 % vs. 71 % (SWITCH) | 5‑10 % higher success under novel disturbances |
| Resource overhead | < 5 % CPU extra (mostly in Meta layer) | Similar to RL baseline |
| Resilience to unknown unknowns | Maintained > 85 % success when faced with unseen faults | Dropped below 60 % for RL alone |
The authors highlight that the explainable reasoning component not only improves success rates but also gives operators actionable insights (“why this scaling decision was made”), a feature missing from black‑box RL approaches.
Practical Implications
- DevOps & SRE teams can embed POLARIS agents into CI/CD pipelines to automatically generate and verify scaling or recovery plans, reducing mean‑time‑to‑recovery (MTTR).
- Edge/IoT deployments benefit from the low‑latency Adapter, allowing devices to react locally while still leveraging cloud‑based reasoning for strategic decisions.
- Compliance‑heavy domains (e.g., finance, healthcare) gain auditability through the transparent plan explanations and formal verification step, easing regulatory approval.
- Platform vendors can expose the Meta‑learning API as a “self‑optimizing” service, letting customers continuously improve adaptation policies without manual tuning.
- Future AI‑augmented software can adopt the three‑layer pattern as a blueprint for building systems that not only learn but also reason about their own learning processes.
Limitations & Future Work
- Scalability of the shared knowledge graph: as the number of agents grows, synchronization overhead may become a bottleneck; the authors suggest exploring decentralized ontologies.
- Domain‑specific agent design: the current prototypes require hand‑crafted reasoning agents for each target system; automating agent synthesis is an open challenge.
- Evaluation breadth: only two case studies were presented; broader benchmarks (e.g., cloud orchestration, autonomous vehicles) are needed to confirm generality.
- Meta‑learning stability: early experiments show occasional policy oscillations when the environment changes rapidly; future work will investigate more robust continual‑learning algorithms.
Overall, POLARIS offers a compelling roadmap for developers who want their systems to think about adaptation, not just react to it—paving the way toward truly proactive, self‑evolving software.
Authors
- Divyansh Pandey
- Vyakhya Gupta
- Prakhar Singhal
- Karthik Vaidhyanathan
Paper Information
- arXiv ID: 2512.04702v1
- Categories: cs.SE
- Published: December 4, 2025
- PDF: Download PDF