arXiv AI
TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps
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arXiv AI
Sep 15
Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
The paper introduces Continual Search, an iterative framework that guides large language models to persistently search for diagnostic evidence in long AI agent execution logs, addressing the limitations of one-shot judgments. Evaluated on four existing RCA benchmarks and a new large-scale dataset called MegaRCA-Mix, Continual Search consistently boosts attribution performance, achieving a 40% F1 improvement for GPT‑5.5 on MegaRCA‑Mix. The results show that effective search can outweigh raw model scale, enabling lower-tier models to outperform higher-tier ones in root‑cause attribution tasks.
arXiv Machine Learning
Aug 4
TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference
arXiv:2608. 01975v1 Announce Type: cross Abstract: Large language model (LLM) inference has evolved from an offline workload into a continuously operated software service, yet root-cause analysis remains difficult because a single request spans the inference engine, Python/C++ backend, host CUDA APIs, GPU kernels, and distributed communication.
arXiv Machine Learning
Sep 22
TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
TriFleetRCA is an on‑premise pipeline that performs root‑cause analysis for Kubernetes using a single GPU. It gathers evidence at pod, namespace, or cluster scope, deduplicates and ranks it with BM25, filters runbooks through an ingest guard, and returns a root cause with supporting evidence lines. In a live cluster with four injected faults, the system achieved hit rates of 0.85–0.95 across scopes, improved accuracy with deduplication, and demonstrated robust defense against poisoned runbooks.
arXiv AI
Jun 9
From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs
arXiv:2605. 09370v3 Announce Type: replace-cross Abstract: Large-scale AI training is now fundamentally a distributed systems problem, and hardware failures have become routine operating conditions rather than rare exceptions.
arXiv AI
Jun 24
Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment
arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.
arXiv AI
Sep 15
MemRiskBench: Trace-Aware Risk-Preserving Evaluation for Long-Horizon LLM Agents
arXiv:2609.14976v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate memory across sessions, creating sparse but high-impact risks: stale facts, conflicting updates, cross-user leakage,...