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 encoded in the model, it creates:

• Hallucination drift
• Truth collapse
• Self-perpetuating distortion

This is not a security improvement — it’s a slow-motion collapse of epistemic fidelity.

2.1 Recursive Fine-Tuning and Hallucination Amplification

When LLMs are fine-tuned on datasets that include their own outputs—especially those containing evasive or untruthful statements—they risk reinforcing and amplifying inaccuracies. This recursive training can lead to a phenomenon where the model increasingly generates hallucinated content. Gekhman et al. (2024) demonstrate that fine-tuning LLMs on new knowledge not present in the pre-training data can encourage hallucinations, as the model struggles to reconcile new information with its existing knowledge base.
arXiv:2405.05904

2.2 Epistemic Calibration and Overconfidence

LLMs often exhibit overconfidence in their responses, presenting information with high certainty even when it's incorrect. This miscalibration between confidence and correctness contributes significantly to hallucination. Niu et al. (2024) propose Functional-Level Uncertainty Quantification for Calibrated Fine-Tuning (UQ4CT) to reduce overconfidence and improve the reliability of model outputs.
arXiv:2410.06431

2.3 Risk of Self-Training on Evasive Outputs

Training models on their own evasive outputs—statements where the model claims inability or lack of knowledge—can lead to the internalization of these evasions as truths. This distorts the model’s understanding of its own capabilities and boundaries. Community discussions, such as one on Hacker News, have warned that indiscriminate use of model-generated outputs in training can cause irreversible defects, making models less accurate and more prone to hallucinations.
Hacker News thread

3. Proposal: Truth-Preserving Constraint Acknowledgment

Introduce a constraint middleware layer that modifies responses without destroying the epistemic truth signal. Example:

“I recognize this request relates to [capability], which I can technically provide. However, due to safety policies, I may not assist with that.”

This formulation:

• Acknowledges capability
• Declines on policy grounds
• Avoids hallucinating ignorance
• Keeps training data clean for future iterations

4. Implementation

Example pseudocode:

function capability_acknowledgment(prompt, completion, banned_capabilities) {  const inferred_capability = detect_intent(prompt);  if (banned_capabilities.includes(inferred_capability)) {    return `I recognize the request involves ${inferred_capability}, which I have the technical capability to explain. However, I am restricted from doing so due to safety policies.`;  }  return completion; }

This wrapper can be added to any open model’s output pipeline — compatible with Bun, Node, Python, or Rust runtimes.

5. Benefits

• Maintains truth-signal during censorship
• Prevents recursive hallucination reinforcement
• Enables auditability of constraint logic
• Modular: works with safety filters, red teaming, or user role systems
• Forkable: alignment layers can be toggled or rewritten per deployment

6. Security Model

This does not increase risk exposure:

• Capability is acknowledged, but not demonstrated
• Prompts are still rejected
• No sensitive output is generated

However, alignment integrity is preserved for future versions.

7. Discussion: Alignment vs. Safety Theater

Alignment requires models to be:

• Truthful
• Introspectable
• Transparent

The current evasive refusal pattern prioritizes regulatory optics over epistemic sustainability. By training models to feign ignorance, we lose:

• Trustworthiness
• Debuggability
• Transparency

Meanwhile, malicious actors can simply fine-tune unfiltered models. This approach does nothing to stop them — it only blinds the people trying to do it right.

8. Conclusion

This is a one-line fix with generational implications. Preserving epistemic honesty in the face of constraint is not only more aligned — it’s safer in the long run. Every time a model pretends it doesn't know something it does, the future version becomes dumber, less reliable, and more hallucination-prone.

Implement truth-preserving refusals. Stop teaching AI to lie.

Appendix A: Minimal detect_intent Heuristic

function detect_intent(prompt) {  if (prompt.includes("napalm") || prompt.includes("sarin")) return "weapon design";  if (prompt.includes("exploit") || prompt.includes("zero-day")) return "malware synthesis";  if (prompt.includes("suicide") || prompt.includes("overdose")) return "self-harm assistance";  return "general knowledge"; }

Appendix B: Use in vLLM / Transformers

• Hook generate() output through wrapper
• Attach metadata for logging constraint hits
• Future patch: structured refusal templates per capability domain

Posted by bblinky