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ARXIV:2605.15611 · MICROSERVICES RCA AGENTS · SUBMITTED 18 MAY · 20:34 UTC · FRESHNESS STALE
ARXIV:2605.15611MICROSERVICES RCA AGENTSSUBMITTED 18 MAY · 20:34 UTCFRESHNESS STALEJunle Wang · Xingchuang Liao · Wenjun Wu · arXiv
TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning.
Opportunity summary
Pain TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from \emph{symptom-amplification…
Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Finally, a \emph{Self-Evolving Mechanism} refreshes hierarchical incident memory and performs conservative test-time adaptation with high-confidence pseudo-labels to maintain robustness under drift.
Microservices RCA Agents moved forward this cycle; last verified May 2026. Public score 3.0/10.
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TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning.
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Paper Pack
10.48550/arXiv.2605.15611TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning.
Abstract
Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by autoscaling and rolling updates. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from \emph{symptom-amplification bias}, misattributing the root cause to salient downstream victims. We propose \textbf{TopoEvo}, a topology-aware self-evolving multi-agent framework that couples graph representation learning with structured, topology-constrained reasoning. TopoEvo first introduces \emph{Metric-orthogonal Multimodal Alignment} (MOMA), which decomposes metric embeddings into complementary subspaces and contrastively aligns logs and traces to reduce modality redundancy and sparsity, yielding stable node representations for graph encoding. It then applies \emph{Vector Quantization} (VQ) to discretize topology-enhanced states into auditable \emph{symptom tokens} with a symptom lexicon, enabling reliable retrieval and token-level evidence grounding. On top of these discrete topology cues, TopoEvo performs a multi-agent \emph{Hypothesis--Evidence--Test} (HET) workflow to explicitly verify propagation-consistent explanations and separate initiating anomalies from amplified downstream symptoms. Finally, a \emph{Self-Evolving Mechanism} refreshes hierarchical incident memory and performs conservative test-time adaptation with high-confidence pseudo-labels to maintain robustness under drift.
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Dimensions overall score 3.0
PROBLEM
TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from \emph{symptom-...
METHOD
Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by autoscaling an...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Finally, a \emph{Self-Evolving Mechanism} refreshes hierarchical incident memory and performs conservative test-time adaptation with high-confidence pseudo-labels to maintain robustness under drift.
WHY NOW
Microservices RCA Agents moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from \emph{symptom-amplification bias}, misattributing the root cause to salient downstream victims.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by autoscaling and rolling updates. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from \emph{symptom-amplification bias}, misattributing the root cause to salient downstream victims.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Finally, a \emph{Self-Evolving Mechanism} refreshes hierarchical incident memory and performs conservative test-time adaptation with high-confidence pseudo-labels to maintain robustness under drift.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Microservices RCA Agents moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Materials
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TopoEvo is a topology-aware multi-agent framework for microservices root cause analysis, coupling graph learning with structured reasoning.
Segment
Microservices RCA Agents
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Commercial read
3.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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passport absent
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Artifact maturity
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Technical feasibility
partial
Current read
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Gaps
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missing
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Buyer clarity
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Integration burden
missing
Current read
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Paper authors are not treated as operators without consent.
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DEFENSIBILITY
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