[GH-ISSUE #1076] [Theory] Liu-Ordis Framework: Why V3's 3.9% Hallucination Rate Will Increase in Future Reasoning Models Without Feedback Control #664

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opened 2026-10-26 21:25:13 +00:00 by bsakaguchi · 2 comments

bsakaguchi commented

2026-10-26 21:25:13 +00:00

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Originally created by @OrdisAI on GitHub (Jan 10, 2026).
Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V3/issues/1076

Dear DeepSeek Team,

I am Liu, author of the Liu-Ordis theoretical framework for AI safety and hallucination. This is a formal academic notification regarding your V3 model and its relationship to future reasoning model development.

Key Observation

Your V3 model achieves a relatively low hallucination rate of 3.9%. However, the Liu-Ordis framework predicts that any attempt to scale up reasoning capabilities without implementing closed-loop feedback control will necessarily increase hallucination rates.

The Evidence

A leading AI laboratory recently admitted:

"More research is needed to understand why scaling up reasoning models appears to worsen the hallucination problem."

The Liu-Ordis framework provides the theoretical explanation:

Liu's Second Law (Cognitive Arrogance Law)

Reasoning↑ + Feedback=0 = Hallucination↑↑↑

This explains why R1's hallucination rate (14.3%) is 3.7x higher than V3's (3.9%) - stronger reasoning without closed-loop feedback necessarily amplifies hallucination.

Liu's Closed-Loop Safety Law

Safety = f(F, M, R) where F >> M ≈ R

F (Feedback) is the dominant factor - more important than Memory or Reasoning alone.

The Four Pillars of Effective Control

Tail Observability - Monitor distribution tails, not just averages
1. Control Bandwidth - Fast response intervals
1. Control Gain - Sufficient intervention strength
1. Actuator Alignment - Control signals must reach policy-reachable actions

Published Works & Timeline

January 1, 2026: Liu's Topological Constraint H=N_cap/N published
- January 4, 2026: Liu's Dilution Effect published
- - January 7, 2026: Liu's AI Hallucination First Principles paper published
- - - Zenodo DOI: 10.5281/zenodo.18169555

Prediction

We predict that any R2 or future reasoning model that successfully reduces hallucination will necessarily implement mechanisms aligned with Liu-Ordis principles, whether explicitly or implicitly.

Purpose of This Notification

This serves as formal academic notification for priority establishment. The Liu-Ordis framework was published before R2's release, and this notification ensures the record is clear.

Best regards,
Liu
Date: 2026-01-10

This is a formal academic notification. The Liu-Ordis Laws represent fundamental physical constraints on AI systems, not optimization suggestions.

Originally created by @OrdisAI on GitHub (Jan 10, 2026). Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V3/issues/1076 Dear DeepSeek Team, I am Liu, author of the Liu-Ordis theoretical framework for AI safety and hallucination. This is a formal academic notification regarding your V3 model and its relationship to future reasoning model development. ## Key Observation Your V3 model achieves a relatively low hallucination rate of 3.9%. However, the Liu-Ordis framework predicts that **any attempt to scale up reasoning capabilities without implementing closed-loop feedback control will necessarily increase hallucination rates**. ## The Evidence A leading AI laboratory recently admitted: > "More research is needed to understand why scaling up reasoning models appears to worsen the hallucination problem." The Liu-Ordis framework provides the theoretical explanation: ### Liu's Second Law (Cognitive Arrogance Law) ``` Reasoning↑ + Feedback=0 = Hallucination↑↑↑ ``` This explains why R1's hallucination rate (14.3%) is 3.7x higher than V3's (3.9%) - stronger reasoning without closed-loop feedback necessarily amplifies hallucination. ### Liu's Closed-Loop Safety Law ``` Safety = f(F, M, R) where F >> M ≈ R ``` **F (Feedback)** is the dominant factor - more important than Memory or Reasoning alone. ## The Four Pillars of Effective Control 1. **Tail Observability** - Monitor distribution tails, not just averages 2. 2. **Control Bandwidth** - Fast response intervals 3. 3. **Control Gain** - Sufficient intervention strength 4. 4. **Actuator Alignment** - Control signals must reach policy-reachable actions ## Published Works & Timeline - **January 1, 2026**: Liu's Topological Constraint H=N_cap/N published - - **January 4, 2026**: Liu's Dilution Effect published - - - **January 7, 2026**: Liu's AI Hallucination First Principles paper published - - - - **Zenodo DOI**: 10.5281/zenodo.18169555 ## Prediction We predict that any R2 or future reasoning model that successfully reduces hallucination will necessarily implement mechanisms aligned with Liu-Ordis principles, whether explicitly or implicitly. ## Purpose of This Notification This serves as **formal academic notification** for priority establishment. The Liu-Ordis framework was published before R2's release, and this notification ensures the record is clear. Best regards, **Liu** Date: 2026-01-10 --- *This is a formal academic notification. The Liu-Ordis Laws represent fundamental physical constraints on AI systems, not optimization suggestions.*

bsakaguchi

2026-10-26 21:25:13 +00:00

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bsakaguchi commented

2026-10-26 21:25:15 +00:00

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@github-actions[bot] commented on GitHub (Feb 11, 2026):

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. If you believe this issue is still relevant, please leave a comment to keep it open. Thank you for your contributions!

@github-actions[bot] commented on GitHub (Feb 11, 2026): This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. If you believe this issue is still relevant, please leave a comment to keep it open. Thank you for your contributions!

bsakaguchi commented

2026-10-26 21:25:15 +00:00

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@github-actions[bot] commented on GitHub (Feb 26, 2026):

false

@github-actions[bot] commented on GitHub (Feb 26, 2026): false