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.
Gulavani, Alexey Tumanov, and Ramachandran Ramjee
3 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
3
Pith papers citing it
5
external citations · external index
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Optimus enables elastic decoding granularity adaptation in diffusion LLMs via chunked decoding and load-based scheduling to raise throughput under dynamic conditions.
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
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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Optimus: Elastic Decoding for Efficient Diffusion LLM Serving
Optimus enables elastic decoding granularity adaptation in diffusion LLMs via chunked decoding and load-based scheduling to raise throughput under dynamic conditions.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.