ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
The article introduces ORCA, an interactive copilot designed for optimized root cause analysis. It aims to make causal analysis more accessible to domain experts by guiding them through various workflows. ORCA includes features such as causal discovery, effect estimation, and structured reporting, demonstrating effectiveness in real-world applications.
- ▪Causal analysis is important in fields like manufacturing, social science, and medicine.
- ▪ORCA helps users navigate causal analysis workflows, from automatic to user-guided processes.
- ▪The tool evaluates performance, generates metrics, and produces insights through reports.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27022 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | E-3BmbYXbMtV |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.27022 (cs) [Submitted on 26 May 2026] Title:ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis Authors:Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj, Kristian Kersting, Juergen Luettin View a PDF of the paper titled ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis, by Phi Nguyen Xuan and 4 other authors View PDF HTML (experimental) Abstract:Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptual and methodological complexity of causal methods makes them largely inaccessible to domain experts.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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