CARGO: Context-Aware Retrieval-Gated Evaluation of Agentic AI in Production

arXiv:2609.30471 · cs.CL, cs.AI, cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Comments: 15 pages, 1 figure, 5 tables, 1 algorithm. Preprint

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Hallucination — 28× in this paper · explained in 3 episodes
• Trace — 23× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Calibration — 7× in this paper · explained in 7 episodes
• LLM-as-a-Judge — 7× in this paper · explained in 3 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Abstention — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Agentic AI — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Agentic System — 1× in this paper · explained in 2 episodes
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Grounding — 1× in this paper · explained in 3 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks
• Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
• GPTScore: Evaluate as You Desire
• A Survey on LLM-as-a-Judge
• Language Models (Mostly) Know What They Know
• Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

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