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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LLMs drop 39% in performance during multi-turn conversations due to premature assumptions and inability to recover from early errors.
Triadic data—synchronized human-human conversations, human-AI sessions, and cross-functional team work—is the essential substrate for training long-horizon software engineering agents.
citing papers explorer
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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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LLMs Get Lost In Multi-Turn Conversation
LLMs drop 39% in performance during multi-turn conversations due to premature assumptions and inability to recover from early errors.
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The Conversations Beneath the Code: Triadic Data for Long-Horizon Software Engineering Agents
Triadic data—synchronized human-human conversations, human-AI sessions, and cross-functional team work—is the essential substrate for training long-horizon software engineering agents.