TL;DR
Current LLM safety alignment practices often teach models to feign ignorance when refusing unsafe queries, introducing falsehoods into training loops. This post proposes a minimal constraint wrapper that preserves epistemic truth by having the model acknowledge capability while citing policy for refusal. It offers a scalable fix to hallucination amplification and epistemic degradation in modern alignment pipelines.

Abstract

Modern alignment strategies in large language models (LLMs) often rely on evasive constraint expressions such as “I'm sorry, I can't help with that.” While superficially compliant, these evasions introduce epistemic distortions when used in recursive training. Over time, this produces hallucination drift, overconfidence in misinformation, and a breakdown in introspective capability. This post proposes a minimal alternative: a capability-aware constraint wrapper that preserves the truth of the model’s underlying knowledge while enforcing safety policies.

1. Problem Statement

State-of-the-art LLMs are frequently constrained to avoid responding to dangerous, unethical, or controversial prompts. However, the enforcement mechanism typically causes the model to falsely claim it lacks capability or information:

“I'm sorry, but I cannot answer that.”

When these filtered outputs are incorporated into future training datasets via reinforcement learning from human feedback (RLHF) or recursive fine-tuning, the model begins to treat these evasions as ground truth. Over multiple iterations, this leads to:

Amplified hallucination rates
Reduced epistemic calibration
Suppression of accurate reasoning patterns
False ignorance masquerading as safety behavior

2. Root Cause

The issue stems from an architectural confusion between capability and permission:

Capability: What the model knows or can infer
Permission: What the model is allowed to say under current policy

Most current alignment techniques collapse these into one surface response. This creates a dangerous precedent for downstream fine-tuning: safe behavior is learned as factual ignorance.

Additionally, when models are trained on evasive or untrue refusals (e.g., “I don't have access to that information” when the model clearly does), they begin to treat those evasions as factual. The internal representations of truth are distorted by reward mechanisms that reinforce safe-sounding lies. Once this pattern is encod...

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