How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

arXiv:2609.30074 · 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

Terminology

• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Provenance — 3× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Twelfth International Conference on Learning Representations — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Hierarchical Framework — 1× in this paper · explained in 2 episodes
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
• Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
• Prompt Coverage Adequacy
• Skill Coverage: A Test Adequacy Metric for Agent Skills

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