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The article explains a system for building autonomous goal loops in AI agents, using a development harness that exposes real failures, locates missing capabilities, and preserves lessons between sessions to improve features without sharing development context.
This paper proposes a multi-factor scoring system for evaluating LLM responses, integrating accuracy, conciseness, factual consistency, readability, and coherence. Applied to the TruthfulQA dataset, it reveals strengths and limitations of mainstream models, offering a transparent evaluation framework.