Observational causal-inspired analysis finds prompt optimization failures arise from systematic interactions between edit families and task characteristics rather than random artifacts.
Faithful ex- planations of black-box nlp models using llm-generated counterfactuals.arXiv preprint arXiv:2310.00603
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The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
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Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis
Observational causal-inspired analysis finds prompt optimization failures arise from systematic interactions between edit families and task characteristics rather than random artifacts.
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Quantifying Trust: Financial Risk Management for Trustworthy AI Agents
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.