arXiv AI June 2, 2026 By Zongzong Wu, Ming Zhao, Fengxiao Tang, Nei Kato

PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis

arXiv:2606. 00582v1 Announce Type: new Abstract: Network faults propagate layer by layer along topology and protocol dependencies, yet operations systems typically observe only symptomatic alerts at the tail end of propagation chains, where distinct root-cause faults may produce highly similar end-point symptoms.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 26

OpenRCA 2.0: From Outcome Labels to Causal Process Supervision

arXiv:2606. 27154v1 Announce Type: new Abstract: Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use.

By Aoyang Fang, Yifan Yang, Jin'ao Shang, Qisheng Lu, Junjielung Xu, Rui Wang, Songhan Zhang, Yuzhong Zhang, Boxi Yu, Pinjia He

llms agents benchmarks

Hugging Face Trending Papers

Jun 25

OpenRCA 2.0: From Outcome Labels to Causal Process Supervision

Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use. However, existing datasets suffer from a fundamental gap: they label only the root cause, not the propagation path connecting it to the observed symptom, which largely simplifies the task to naive pattern matching.

llms agents benchmarks

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.

By Ruilin Xu, Junyi Li, Pengfei Chen, Zongxuan Xie

llms nlp multimodal

arXiv Machine Learning

Jul 31

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.

By Lei Zan, Keli Zhang, Shifeng Xie, Jiale Zheng, Zehao Xiao, Zhiwei Dong, Ke Zhang, Ruichu Cai, Malik Tiomoko, Lujia Pan

llms benchmarks safety

arXiv Machine Learning

Jun 16

SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles

arXiv:2606. 15559v1 Announce Type: cross Abstract: The transition toward software-defined vehicles concentrates an increasing share of vehicle functionality into distributed software services, where failures propagate through service dependencies and the surface symptom is often several causal hops away from the underlying defect.

By Matthias Wei{\ss}, Athreya Hosahalli Prakash, Falk Dettinger, Nasser Jazdi, Michael Weyrich

rag agents multimodal

arXiv AI

Jul 16

How Far Can Root Cause Analysis Go on Real-World Telemetry Data?

arXiv:2607. 13548v1 Announce Type: new Abstract: Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to both classical and LLM-based approaches.

By Athira Gopal, Ashwanth Krishnan

llms agents multimodal benchmarks

Related stories

OpenRCA 2.0: From Outcome Labels to Causal Process Supervision

OpenRCA 2.0: From Outcome Labels to Causal Process Supervision

TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles

How Far Can Root Cause Analysis Go on Real-World Telemetry Data?