GSAR is a grounding-evaluation framework for multi-agent LLMs that uses a four-way claim typology, evidence-weighted asymmetric scoring, and tiered recovery decisions to detect and mitigate hallucinations.
Aiopslab: A holistic framework to evaluate ai agents for enabling autonomous clouds, 2025b
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Ambig-IaC detects structural disagreements in LLM-generated IaC candidates across three hierarchical axes to produce clarification questions, improving structure and attribute accuracy by 18.4% and 25.4% on a new 300-task benchmark.
OpsLLM is a domain-specific LLM for software ops QA and RCA built with human-curated data, SFT, and RL using a domain process reward model, showing accuracy gains of 0.2-5.7% on QA and 2.7-70.3% on RCA over general LLMs.
citing papers explorer
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GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs
GSAR is a grounding-evaluation framework for multi-agent LLMs that uses a four-way claim typology, evidence-weighted asymmetric scoring, and tiered recovery decisions to detect and mitigate hallucinations.
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Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis
Ambig-IaC detects structural disagreements in LLM-generated IaC candidates across three hierarchical axes to produce clarification questions, improving structure and attribute accuracy by 18.4% and 25.4% on a new 300-task benchmark.
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An End-to-End Framework for Building Large Language Models for Software Operations
OpsLLM is a domain-specific LLM for software ops QA and RCA built with human-curated data, SFT, and RL using a domain process reward model, showing accuracy gains of 0.2-5.7% on QA and 2.7-70.3% on RCA over general LLMs.