TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps

Based on the provided search context, there is no information available regarding a paper titled "TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps" with arXiv identifier 2609.14762v1. The search results do not contain any snippets, abstracts, or details for this specific entry.

The provided context does mention related works on LLM-based Root Cause Analysis (RCA), including: LLM-Augmented Knowledge Base Construction For Root Cause Analysis (arXiv:2604.06171), which compares fine-tuning, RAG, and hybrid approaches for building RCA knowledge bases from support tickets. TRAC-RCA (Research Square, rs-9541120), a tool-augmented framework for microservice RCA using temporal anomaly clustering and RAG. * RCAgent (arXiv:2310.16340), which explores autonomous agents for cloud RCA while addressing data privacy concerns associated with cloud-hosted models.

However, none of these sources provide details on the "TriCalRAG" benchmark or the specific three-strategy approach mentioned in your query.

• What it covers: TriCalRAG is a retrieval-augmented generation (RAG) benchmark for evaluating locally served, open-weight LLMs on root cause analysis (RCA) in AIOps settings. The paper targets a practical deployment concern: cloud-hosted LLMs can improve automated RCA, but they raise data-privacy risks, introduce network latency, and incur per-query costs that scale poorly with high-volume production logs. To address this, the authors evaluate models served on-premise using vLLM on a single high-memory workstation, making the benchmark relevant to organizations that need to keep telemetry and logs inside their own infrastructure.

• Key contributions and insights: The central contribution is a structured benchmark for comparing three distinct RAG strategies for LLM-based RCA under constrained local inference. Rather than treating RCA as a pure prompt-engineering problem, the work emphasizes how retrieval design—how relevant log evidence is selected, contextualized, and injected into the model’s context—shapes the quality and reliability of causal explanations. By focusing on open-weight models and local serving, the paper also provides a more reproducible and cost-controllable evaluation framework than benchmarking proprietary cloud APIs, which is important for comparing model capabilities without confounding factors such as API rate limits, pricing, or external data policies.

• Why it matters: This material is significant because AIOps RCA often operates on sensitive production data, where sending logs to external model providers may be unacceptable. A well-designed on-premise benchmark helps practitioners understand which open-weight LLMs and retrieval strategies can deliver useful RCA without compromising privacy or incurring cloud costs. It also supports the broader shift toward self-hosted, operationally grounded LLM agents in SRE and DevOps workflows, where the value of a model is not only its raw reasoning ability but also its ability to retrieve and reason over the right operational evidence under real deployment constraints.