COHORT automates mitigation generation for network attacks via collaborative LLMs on emulated topologies with offensive replay evaluation, reporting 46.7% success rate that is 4.4 times higher than a single-agent baseline.
Nissist: An incident mitigation copi- lot based on troubleshooting guides
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
StepFly automates TSG execution via TSG Mentor, LLM-based DAG extraction with QPPs, and a DAG-guided parallel scheduler, reaching 94% success on GPT-4.1 with 32.9-70.4% time savings on parallelizable guides.
OpsLLM's pipeline (HITL data curation, SFT, GRPO RL with a domain process reward model) improves LLM accuracy on software-operations QA and RCA, especially on in-distribution root-cause-analysis tasks.
TSGuard builds domain knowledge bases offline from historical incidents and applies online multi-agent structured reasoning to diagnose AI workload failures, delivering 19.8% higher accuracy and 63.4% lower verification time than baselines on Azure production data.
citing papers explorer
-
COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies
COHORT automates mitigation generation for network attacks via collaborative LLMs on emulated topologies with offensive replay evaluation, reporting 46.7% success rate that is 4.4 times higher than a single-agent baseline.
-
StepFly: Agentic Troubleshooting Guide Automation for Incident Diagnosis
StepFly automates TSG execution via TSG Mentor, LLM-based DAG extraction with QPPs, and a DAG-guided parallel scheduler, reaching 94% success on GPT-4.1 with 32.9-70.4% time savings on parallelizable guides.
-
OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning
OpsLLM's pipeline (HITL data curation, SFT, GRPO RL with a domain process reward model) improves LLM accuracy on software-operations QA and RCA, especially on in-distribution root-cause-analysis tasks.
-
TSGuard: Automated User-Centric Incident Diagnosis for AI Workloads in the Cloud
TSGuard builds domain knowledge bases offline from historical incidents and applies online multi-agent structured reasoning to diagnose AI workload failures, delivering 19.8% higher accuracy and 63.4% lower verification time than baselines on Azure production data.