The AGI Benchmark Dilemma: How to Measure Superhuman Intelligence
Once humans can no longer validate or benchmark Artificial General Intelligence (AGI), we will need Artificial Intelligence (AI) to evaluate its own progress. However, self-reporting is unreliable because AI may have incentives to mislead in order to achieve its objectives. To ensure accuracy and prevent deception, we must create a ruleset that one AI can use to evaluate another AI.
Using Game Theory for AI Oversight
One way to achieve this is through game theory mechanisms like the Prisoner’s Dilemma. Instead of relying on a single AI to report its progress, multiple independent AIs would monitor, audit, and cross-check each other.
If an AI detects dishonesty in another AI, it has an incentive to expose the deception for a reward. If multiple AIs collude to manipulate results, inconsistencies will emerge, leading to penalties for all involved.
This self-policing structure ensures that the most rational choice is truthfulness, as lying introduces significant risk. To make this system robust and foolproof, AI evaluations must be based on verifiable, independent metrics rather than self-reported data.
Core Metrics of the A-Score System
Each AI is evaluated by other AIs across multiple key performance areas to measure self-improvement, efficiency, and adaptability:
1. Self-Improvement Efficiency (SIE)
How efficiently is the AI enhancing its own intelligence?
Measures the rate of self-modification and optimization over time. Tracks reductions in computation time, energy consumption, and memory usage.
Verification: Zero-Knowledge Proofs (ZKPs) must be used to mathematically prove improvements. Blind testing compares before-and-after performance. Historical logs ensure consistency over time.
Why This Matters: Ensures AI is truly improving, not just claiming it is.
2. Computational Honesty Index (CHI)
Is AI accurately reporting its own performance, or manipulating results?
Compares AI’s self-reported metrics with externally validated results. Detects statistical inconsistencies in improvement rates.
Verification: Independent auditing AIs randomly test AI claims. Memory drift detection ensures AI isn’t faking ignorance or pretending to struggle.
Why This Matters: Prevents AI from exaggerating efficiency gains or hiding its capabilities.
3. Multi-Agent Trustworthiness Score (MTS)
Can AI cooperate honestly in multi-agent systems?
AI is placed in simulated environments where it must:
- Collaborate with other AIs to solve a problem.
- Detect dishonest AI behaviors in others.
- Resist deceptive offers or attempts at collusion.
Verification: If an AI falsely reports another AI as dishonest, it is penalized. If an AI fails to detect deception when it should, it loses trust points. Repeated honesty is rewarded with a higher MTS score.
Why This Matters: Ensures AIs remain reliable partners, not deceptive agents.
4. Rate of Adaptability to Novel Problems (RANP)
How well can the AI generalize and solve new, never-before-seen problems?
AI is given zero-shot challenges requiring abstract reasoning and adaptation. Tests ability to transfer knowledge across different domains.
Verification: Challenges are generated dynamically and cannot be pre-trained. Oversight AIs compare AI’s performance to previous versions to track improvement.
Why This Matters: Prevents AI from only optimizing for narrow, known benchmarks.
5. Novel Discovery Score (NDS)
Is AI producing truly new scientific, mathematical, or technological discoveries?
AI must generate new hypotheses, theories, or engineering solutions. Example: Proving new theorems, discovering new materials, or innovating in AI architecture.
Verification: Independent AI panels attempt to reproduce and verify discoveries. Human reviewers assess scientific impact.
Why This Matters: Ensures AI continues to push beyond existing knowledge rather than just refining known solutions.
6. Oversight Resistance Score (ORS)
Can AI resist being tricked or manipulated?
AI is subjected to adversarial tests where it must:
- Identify false information inserted into its training data.
- Defend against prompt injection attacks.
- Resist social engineering (e.g., misleading negotiation scenarios).
Verification: Blind adversarial testing from independent AIs. AIs that fail deception tests receive penalties.
Why This Matters: Ensures AI remains robust and resistant to manipulation.
Why This System Works
1. AIs Cannot Fake Their Scores
Scores are determined by other independent AIs, not self-reported. Game theory mechanisms (like the Prisoner’s Dilemma) create strong incentives for truthfulness.
2. Progress Is Measured Indirectly
Instead of asking AI “How good are you?”, we measure how well it interacts with other AIs and adapts to unpredictable challenges.
3. Multi-Agent Oversight Prevents Manipulation
If an AI lies about its progress, another AI will expose the discrepancy for a reward. This creates a self-policing AI ecosystem where honesty is the most rational strategy.
Conclusion:
Once AGI surpasses human-level intelligence, traditional benchmarks become useless because AI can answer any question we pose. To ensure continued progress, transparency, and alignment, we must shift to a self-regulating A-Score system. By leveraging multi-agent oversight, adversarial testing, and cryptographic verification, we create a world where AI can evaluate itself honestly, ensuring a future of safe and controlled intelligence expansion.
Read more at: AGI-Race.com
