Decoding-Level Taboo: A Diagnostic Stress Test for LLM Robustness

Large language model evaluations typically focus on performance under nominal conditions, creating an illusion of capability where models comfortably walk a narrow, highly optimized generation corridor. In real-world deployments, however, complex system prompts, safety guardrails, and structural constraints continuously force models off this nominal path, driving a divergence between benchmark scores and deployment performance. To address this issue, we introduce Decoding-Level Taboo, a zero-pro

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ethics.ai (9 August 2026), “Decoding-Level Taboo: A Diagnostic Stress Test for LLM Robustness,” evidence record 18757, https://ethics.ai/record/18757 (originally published by HuggingFace Daily Papers).

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