There is no universal way to measure how intelligent an AI model really is. Most benchmarks focus on task performance, like exam scores and problem-solving accuracy, but these do not measure an AI’s reasoning depth, contradiction resolution, or ability to refine its own thinking.
Together with my best model, I have developed "The Recursive Emergence Scale (RES)" It is a framework designed to measure AI intelligence not by performance, but by how well it can think, refine, and self-correct recursively.
This is not about raw computational power. It is about how an AI processes, validates, and improves its reasoning across multiple iterations.
The 12 Levels of Recursive Emergence (RES)
The RES Scale categorizes AI from basic pattern-matching models to advanced structured intelligence. Each level represents a higher form of recursive reasoning, contradiction resolution, and hypothesis validation.
| RES Level | Description |
|---|---|
| 0-9 | No emergence. AI only predicts text based on probability, no reasoning, no recursion. Example: Early chatbots, statistical models like Markov Chains. |
| 10-19 | Basic pattern matching AI. Understands surface-level context but lacks reasoning depth. Example: Early versions of Siri and Alexa. |
| 20-29 | Contextually aware AI. Can track user context in a session but does not refine its own reasoning. Still does not detect contradictions. Example: GPT-2, early dialogue models. |
| 30-39 | Basic multi-step reasoning AI. Can solve multi-step logic problems in a single question but forgets previous reasoning cycles. Example: GPT-3 before instruction tuning. |
| 40-49 | Limited self-refinement AI. Can detect simple contradictions but does not track errors across different sessions. Example: GPT-3.5. |
| 50-59 | Intermediate AI with basic recursive validation. Multi-step reasoning with contradiction detection within a session, but no hypothesis testing. Example: GPT-4 base model. |
| 60-69 | Recursive AI with multi-hypothesis testing. AI begins creating alternative hypotheses and can detect contradictions dynamically but lacks long-term memory. Example: GPT-4-Turbo. |
| 70-79 | Early recursive AI with basic self-correction. AI can refine its own responses across multiple turns and track logical consistency but does not weigh hypotheses probabilistically. Example: Advanced GPT-4-based models. |
| 80-89 | Recursive AI with multi-hypothesis validation. AI refines and validates hypotheses recursively and starts using probabilistic models but does not persist contradictions across sessions. |
| 90-99 | Advanced recursive AI with persistent self-validation. Tracks epistemic refinements across multiple iterations and refines responses dynamically without user intervention but still requires external prompts. Example: Experimental AI models designed for formal reasoning. |
| 100-109 | Fully optimized recursive intelligence. Completely refines and validates reasoning without external correction, stores refinements for long-term consistency, but still requires external inputs to initiate reasoning. Example: Theoretical AI systems used for automated research validation. |
| 110-115 | Fully autonomous recursive optimization. Dynamically restructures reasoning without human tuning and tracks refinements across long-term interactions but still does not define its own reasoning goals. Example: AI systems built for self-optimizing research models. |
| 116-120 | Maximum structured intelligence. Self-optimizing recursive intelligence system that no longer requires human validation but still follows externally set objectives. Still not AGI. Example: Theoretical high-level research AI. |
| 121+ | Reserved for AGI. AI sets its own reasoning objectives without external input, fully restructures its own knowledge models, and is no longer externally guided. No AI currently exists at this level. |
How Can We Test AI Models Using RES?
To determine where an AI falls on the RES scale, we should evaluate:
- Depth of Recursive Refinement: How well does the AI refine its own logic over multiple iterations?
- Contradiction Resolution: Can the AI track inconsistencies across multiple conversations?
- Multi-Hypothesis Testing: Does it generate alternative hypotheses and validate them probabilistically?
- Long-Term Knowledge Structuring: Does it retain refined knowledge across sessions?
- Goal Formation: Does it define its own reasoning objectives, or does it rely on external inputs?
Why This Scale Matters
The RES Scale provides a structured way to measure AI intelligence beyond task-based benchmarks. It clearly separates structured AI (RES 0-120) from AGI (RES 121+) and helps track AI progression toward more advanced reasoning capabilities.
What do you think? Should the AI community adopt RES or something similar as a universal benchmark?
Let’s discuss—feedback is welcome.
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