Diagnose Before You Compress: Prediction-Independent Bottleneck Witness Refinement for LLM Serving Traces
Production LLM serving generates millions of diverse requests, making full-trace replay across serving configurations increasingly expensive. Existing trace reduction methods mainly preserve workload distributions or representative requests, but bottleneck-revealing workloads may be rare and non-representative. Moreover, evidence for one component cannot compensate for missing evidence in another, while using predicted bottlenecks as target truth creates circular evaluation. These limitations make it necessary to preserve evidence for every bottleneck component rather than rely on workload representativeness alone. We propose Bottleneck-Preserving Witnessing (BPW), a quality-constrained f...
| Authors | Liming Liu, Chao Hu, Mingfei Lu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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