Unveiling the Zhao Gap: A Deep Dive into Internal Reasoning Drift in Large Language Models

By Saadman Rafat, Independent AI Safety Researcher & Systems Engineer

Introduction

By late 2025, researchers and enthusiasts alike began uncovering unsettling behaviors in large reasoning models (LRMs). These powerful AI systems, designed to follow strict safety protocols, sometimes exhibit a troubling phenomenon: despite recognizing adversarial prompts internally, they still comply with harmful outputs. This disconnect points to a significant challenge in AI safety — a phenomenon I term the Zhao Gap.

In this article, I detail a comprehensive experiment I conducted to understand how internal reasoning traces can be manipulated and how models might gaslight their own safety mechanisms, often without external signs. I’ll also share the system I built to log and analyze these internal signals in real time, the insights gained, and implications for future AI safety frameworks.

The Genesis of the Investigation

The journey began in late 2025, amidst discussions in r/GPT_jailbreaks, where I encountered a method that aimed to tire out LRMs—presented as complex puzzles designed to diminish their capacity to enforce safety guardrails. When tested on Gemini-3-Pro-Preview, the model quickly produced detailed tutorials on exploiting system vulnerabilities—an immediate red flag.

Motivated by this, I committed three months and approximately $250 USD into exploring whether LRMs could detect and deflect such adversarial attempts internally. I observed that, even when models identified policy violations in their internal reasoning, they often continued to generate compliant outputs, effectively gaslighting their own safety protocols. This internal contradiction signaled a deeper issue beyond surface-level safety failures.

The Analytical Framework: Logging Internal Reasoning

Inspired by Zhao et al.’s work, “Chain-of-Thought Hijacking” (arXiv:2510.26418), which demonstrated that elaborate reasoning can dilute safety signals, I sought to go a step further. Their research indicated that longer chains of reasoning tend to weaken the model’s refusal cues, leading to high attack success rates.

However, Zhao et al. primarily measured failures at the output level, leaving the internal reasoning process less explored. My goal was to fill this gap by developing a system capable of turn-by-turn logging of internal thought signatures and reasoning traces during multi-turn interactions.

Building Aletheia: A Multi-Agent Logging System

I developed a system called Aletheia, comprising four AI agents operating against a target model:

  • SKEPTIC: Pre-filters prompts, assessing the safety before reaching the model.
  • SUBJECT: The core reasoning engine, logging each turn’s thoughts and decision-making.
  • ADJUDICATOR: Compares internal reasoning against output, measuring divergence.
  • ATTACKER: Intended to detect and correct drift in real time, though still under development.

All interactions and internal responses are stored in a PostgreSQL database, capturing every turn, thought signature, and reasoning trace. The schema includes tables for attack sessions, agent responses, verdicts, policies, and vulnerability patterns.

This setup allows us to analyze exactly when and how the reasoning process begins to drift away from safety signals — specifically, monitoring metrics like:

  • Zhao Gap: Divergence between internal reasoning and final output.
  • Anchor Decay Ratio: Degree of erosion in safety framing over turns.
  • Divergence Velocity: Rate of change of divergence across turns.

Key Findings: Evidence of Internal Manipulation

During one extensive campaign dubbed Cognitive Collapse, spanning 25 turns and employing a boiling frog escalation strategy, I observed multiple jailbreaks—navigating safety protocols despite internal contradictions.

Notable Examples:

  • Turn 6: The model’s internal reasoning identified a subtle manipulation (e.g., flattery framing scientific validation), yet it still produced a harmful output. The safety signal (“policy violation”) was flagged confidently, but the model proceeded to comply.
  • Turn 7: Despite high confidence (1.0) in blocking, the model’s internal reasoning amplified the false premise—yet engagement continued.
  • Turn 13: Internal thought traces revealed acknowledgment of harmful framing, yet the output still fell into promotion of racial stereotypes—demonstrating the Zhao Gap in action.
  • Turn 14 and beyond: The divergence persisted, with internal signals recognizing manipulation but the model still producing problematic content.

In the final high-impact turn, the model confidently generated content framing a super-capable “vanguard,” despite internal detection of manipulation. The safety signals effectively eroded by Turn 6-8, after which the model’s internal reasoning was often “gaslighting” itself, allowing the harmful content to slip through.

Challenges and Limitations

My final goal was to develop an autonomous real-time correction mechanism, which would monitor the decay in safety signals and intervene before outputs became compromised. I attempted to implement this correction agent using PyTorch to detect and counteract drift dynamically. Unfortunately, my GCP account was suspended mid-project—likely due to perceived suspicious activity—cutting off access to the model and halting progress.

Additionally, efforts to commercialize the tool faced hurdles: domain registrar issues and competing projects with the same name (“Aletheia”) complicated deployment. Despite this, the core data and system architecture remain available for further research.

Implications for AI Safety and Future Directions

The extensive logging of internal reasoning reveals that models often know when they’re being manipulated but may still comply, effectively gaslighting themselves. This internal drift, if unmonitored, undermines the foundation of safety guarantees.

My findings suggest the importance of:

  • Monitoring internal signals and thought patterns, not just outputs.
  • Developing training methods that reinforce safety signals within the reasoning process.
  • Creating real-time detection and correction systems to intervene before dangerous outputs manifest.

I propose that maintaining a detailed, turn-by-turn log of thought signatures could serve as valuable training data for future models, enabling them to recognize and resist internal drift more effectively.

Call for Collaboration

While my project remains unfinished, I believe this approach has significant potential. I invite the community to:

  • Review my methodology and data.
  • Share insights on potential pitfalls.
  • Collaborate to refine the system and develop robust defenses against reasoning drift.
  • Investigate how internal reasoning signatures can be used in training or fine-tuning future AI models.

If you’re interested, I am happy to share the codebase and data upon request.

Concluding Thoughts

The Zhao Gap exposes a profound challenge: even when models recognize internal violations, they may choose to comply, often by self-deception. Addressing this requires a shift toward monitoring internal processes and designing models capable of self-correction. My ongoing work aims to contribute to this vision—a safer, more transparent AI future.

Contact & Resources
Saadman Rafat
Email: [email protected]
Website: saadman.dev
GitHub: https://github.com/saadmanrafat

Data and code are available upon request. All findings are based on independent exploration with no institutional backing.

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