STLGT encodes microservice traces as span graphs and applies a structure-aware linear graph transformer with a decoupled temporal module to forecast multi-step p95 tail latencies, reporting 8.5% average MAPE improvement over PERT-GNN and up to 12x faster CPU inference.
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QUIET is a hierarchical RVQ-based graph tokenizer with a learned level-weighting gate; it improves several benchmarks but not consistently against the strongest baselines.
citing papers explorer
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STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
STLGT encodes microservice traces as span graphs and applies a structure-aware linear graph transformer with a decoupled temporal module to forecast multi-step p95 tail latencies, reporting 8.5% average MAPE improvement over PERT-GNN and up to 12x faster CPU inference.
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A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
QUIET is a hierarchical RVQ-based graph tokenizer with a learned level-weighting gate; it improves several benchmarks but not consistently against the strongest baselines.