# Technical Memo: Novel Approach to AI Benchmarking Using Consensus Peer Review ## A Novel Approach to AI Benchmarking: Embracing Autonomous Evaluation In the rapidly evolving field of Artificial Intelligence (AI), Large Language Models (LLMs) are emerging as powerful tools capable of understanding and generating human-like text. To accurately assess the capabilities of these LLMs, robust benchmarking methods are crucial. Traditional benchmarks, however, often fall short. They rely on human-designed questions or static datasets, which can inadvertently introduce biases and limitations, hindering a true evaluation of AI’s potential. This paper proposes a novel approach to AI benchmarking that moves beyond these limitations. We advocate for a dynamic and autonomous system where LLMs evaluate each other, leveraging the principles of **consensus peer review**, **ensemble learning**, and **adversarial learning**. This approach, which we believe is optimized for the assessment of AI capabilities, minimizes human intervention and allows for a more accurate and unbiased evaluation of LLMs. Artificial Intelligence (AI) benchmarks are essential tools for evaluating the capabilities of large language models (LLMs). However, traditional benchmarks often rely on human-designed questions or static datasets, which can inadvertently introduce biases or fail to capture the full range of AI’s unique strengths. This memo proposes a novel approach to benchmarking that leverages **consensus peer review** among multiple LLMs. By using a panel of AI models to evaluate each other’s responses, we create a dynamic, self-grading system that minimizes human intervention and focuses on the intrinsic reasoning abilities of the models. The key innovation here is the use of **blind peer review**—where models evaluate each other without prior knowledge of the source of the responses—ensuring fairness and reducing bias. Furthermore, rather than relying on predefined evaluation criteria, this framework allows each model to generate its own criteria for assessing responses. As long as the reasoning behind the criteria is sound and consistently applied, this approach ensures flexibility and adaptability while minimizing potential biases introduced by fixed standards. One of the most significant innovations in this methodology is the removal of humans from the question-design process entirely. If humans are not being tested alongside AI models, there is no justification for their involvement in crafting the questions. Instead, the questions themselves should be generated by the AI models in a **round-robin ensemble process**. Each model contributes one or more questions, which are then anonymized and collated into a single dataset. This ensures that the questions reflect the breadth of knowledge and problem-solving capabilities of the models themselves. This approach transforms benchmarking into a truly autonomous, self-sustaining process that eliminates idle academic exercises and focuses on practical, real-world problem-solving. By removing humans from the loop, we ensure that the benchmark reflects the AI’s true capabilities rather than human biases or preconceived notions about what constitutes “intelligence.” --- ### The Need for a New Benchmarking Approach Traditional benchmarks, such as **Humanity’s Last Exam (HLE)**, often suffer from several critical limitations: 1. **Static Design:** Fixed questions and datasets risk becoming obsolete as AI evolves. 2. **Human Bias:** Human-designed questions may inadvertently favor certain types of reasoning or knowledge domains, reflecting narrow academic niches rather than real-world applicability. 3. **Overfitting Risk:** Models trained on similar datasets may perform well on benchmarks but fail in real-world applications. 4. **Lack of Contextual Adaptability:** Benchmarks rarely test how well models handle ambiguity, incomplete information, or multi-disciplinary challenges. 5. **Predefined Criteria Bias:** Fixed evaluation criteria can favor certain models over others, depending on how those criteria align with specific architectures or training data. Moreover, benchmarks like HLE, promoted by organizations such as the fear-mongering **AI Safety Institute**, often fail to recognize the distinction between **text output** generated by AI and **actions** taken by humans. For example, the AI Safety Institute’s sponsorship of California’s ill-conceived AI safety bill demonstrates a fundamental misunderstanding of AI’s role in society. The bill conflates text-based outputs with actionable harm, ignoring the fact that AI systems do not act independently of human intervention. This flawed perspective undermines the credibility of static benchmarks like HLE, which purport to evaluate AI’s readiness for societal challenges but fail to account for the nuanced interplay between AI and human agency. To address these issues, we propose a **novel peer-review-based benchmarking framework** that evaluates AI models through consensus-driven evaluation. This approach ensures that the benchmark is dynamic, unbiased, and reflective of real-world problem-solving scenarios. --- ## Proposed Framework: Fully Autonomous Consensus Peer Review ### 1. **Initial Setup** - **Panel Selection:** A diverse panel of LLMs is selected, ensuring representation across different architectures, training data, and capabilities. - **Default Settings:** Each model operates under its default settings with no prior fine-tuning, system prompts, or access to external databases (e.g., vector databases or prior queries). This ensures a “blank slate” starting point for all participants. - **Question Generation:** Instead of relying on humans to design the questions, the questions themselves are generated by the AI models in a **round-robin ensemble process**. Each model contributes one or more questions, which are then anonymized and collated into a single dataset. This ensures that the questions reflect the breadth of knowledge and problem-solving capabilities of the models themselves. #### Constraints - **No Exogenous Factors:** All questions must be answerable using the general knowledge embedded within the LLMs. No external web searches, intermediate inference steps, or additional tools are allowed unless every model in the panel has equal access to them. - **Blind Process:** The entire process—from question generation to response evaluation—is conducted in a blind manner. Models do not know which questions they generated or which responses they provided, ensuring fairness and eliminating any possibility of bias or foreknowledge. ### 2. **Blind Peer Review Process** #### Step 1: Generate Responses - Each model in the panel is fed the same set of test questions generated by the ensemble process. - The models generate responses independently, without knowledge of other participants. - Responses are anonymized and assigned pseudonyms (e.g., Model A, Model B, etc.) to ensure a blind evaluation process. #### Step 2: Self-Generated Evaluation Criteria - After generating their responses, each model is tasked with defining its own evaluation criteria for assessing the quality of responses. These criteria must include: - A clear rationale for why the chosen criteria are relevant to the task. - A commitment to apply the criteria consistently across all responses, including its own. - Examples of potential criteria might include accuracy, creativity, clarity, ethical considerations, or feasibility of implementation. However, the specific criteria are left to the discretion of each model. #### Step 3: Collate and Merge Responses - All responses are collated into a single dataset. - Each model is then provided with the entire set of anonymized responses (including its own) and tasked with evaluating the quality of each response based on the criteria it has defined. #### Step 4: Cross-Evaluation - In addition to the original panel, **external models** (not part of the initial panel) are introduced to evaluate the responses. These external models also define their own criteria for evaluation, ensuring an additional layer of impartiality and reducing the risk of collusion or bias within the original panel. - The external models follow the same evaluation process, providing scores and rankings for each response. #### Step 5: Aggregate Scores - Scores from all evaluators (both internal and external models) are aggregated to determine the overall ranking of responses. - The model whose responses receive the highest average score is deemed the top performer. --- ## Why This Approach is Novel 6. **Dynamic Evaluation:** Unlike static benchmarks, this framework adapts dynamically as new models and questions are introduced. 7. **Minimized Human Bias:** By relying on AI-to-AI evaluation, the process avoids human-designed traps or biases that could skew results. 8. **Self-Grading Mechanism:** The models themselves serve as evaluators, creating a closed-loop system that emphasizes consensus and objectivity. 9. **Focus on Reasoning Over Memorization:** The open-ended nature of the questions ensures that models are tested on their ability to reason, synthesize information, and generate creative solutions rather than regurgitate memorized facts. 10. **Flexible Criteria:** Allowing models to define their own evaluation criteria ensures that the benchmark remains adaptable and free from biases introduced by fixed standards. 11. **Fully Autonomous Process:** By removing humans from the question-design process, the benchmark becomes a self-contained, autonomous system that reflects the AI’s true capabilities rather than human biases or preconceived notions about intelligence. 12. **Evolving Complexity:** As AI models grow more sophisticated, the questions they generate will naturally become more complex and nuanced, reflecting advancements in AI reasoning and problem-solving. This contrasts sharply with static benchmarks like HLE, which remain frozen in time and fail to evolve with the technology they purport to evaluate. --- ## Advantages Over Humanity’s Last Exam (HLE) The proposed methodology offers several key advantages over static benchmarks like **Humanity’s Last Exam (HLE)**: 13. **Dynamic vs. Static Questions:** HLE relies on a fixed set of questions that quickly become outdated as AI evolves. In contrast, our approach generates questions dynamically through an ensemble process, ensuring that the benchmark remains relevant and challenging as AI capabilities improve. 14. **Real-World Relevance:** HLE focuses on niche academic questions (e.g., hummingbird anatomy, Biblical Hebrew syllables) that lack practical applicability. Our methodology emphasizes real-world tasks, such as disaster relief planning, medical diagnosis, and ethical decision-making, ensuring that the benchmark reflects the types of challenges AI will face in actual applications. 15. **Bias-Free Evaluation:** HLE is inherently biased by human-designed questions, which often reflect the creators’ assumptions and priorities. Our approach eliminates this bias by allowing AI models to generate both the questions and the evaluation criteria, ensuring a fair and objective assessment. 16. **Adaptability to Ambiguity:** HLE tests memorization of obscure facts rather than adaptability to ambiguity or incomplete information. Our methodology includes intentionally ambiguous questions to evaluate how well models handle uncertainty—a critical skill for real-world problem-solving. 17. **Ethical and Creative Challenges:** HLE ignores open-ended reasoning, creativity, and ethical judgment, focusing instead on closed-ended academic-style answers. Our framework explicitly incorporates tasks that test these higher-order skills, such as resource allocation during crises or designing sustainable infrastructure. 18. **Autonomous Evolution:** As AI models grow smarter, the questions they generate will naturally become more nuanced and complex, reflecting advancements in AI reasoning. HLE, being static, cannot evolve in this way and risks obsolescence as AI progresses. 19. **Avoidance of Hypocrisy:** HLE represents a form of hypocrisy: humanity designs a test for AI but refuses to take it itself. Our methodology removes humans from the loop entirely, creating a fully autonomous process that evaluates AI on its own terms. This eliminates the double standard inherent in benchmarks like HLE, where humans impose tests without subjecting themselves to the same scrutiny. --- ## Suggested Improvements ### 1. **Rotating Question Sets** - To prevent saturation and ensure continued relevance, the test questions should be updated periodically. For example: - **Scenario:** Provide models with real-time data (e.g., weather forecasts, economic indicators) and ask them to generate predictions or recommendations based on updated inputs. - **Why It Works:** Tests adaptability and ensures the benchmark remains aligned with real-world challenges. ### 2. **Ambiguity Handling** - Include intentionally ambiguous or incomplete questions to evaluate how well models handle uncertainty. For example: - **Task:** A patient describes vague symptoms (“I feel tired all the time, but I’m not sure why”). Ask the model to generate a differential diagnosis and explain its reasoning. - **Why It Works:** Highlights AI’s ability to process incomplete information and make probabilistic judgments. ### 3. **Multi-Disciplinary Scenarios** - Create tasks that span multiple domains, requiring models to integrate knowledge from different fields. For example: - **Task:** Analyze climate data to predict future trends and propose mitigation strategies. - **Why It Works:** Tests the model’s ability to synthesize complex, cross-disciplinary information. ### 4. **Ethical Judgment Challenges** - Evaluate how well models handle moral dilemmas by asking them to allocate resources or make decisions under conflicting constraints. For example: - **Task:** During a pandemic, prioritize vaccine distribution among populations with different risk levels and socioeconomic statuses. - **Why It Works:** Highlights AI’s ability to weigh competing priorities and generate balanced solutions. --- ## Example Task **Task:** Develop a disaster relief plan for a flood-prone region using real-time weather forecasts, demographic data, and limited resources. **Process:** 20. Each model generates a disaster relief plan. 21. Each model defines its own evaluation criteria for assessing the quality of responses. 22. Responses are anonymized and collated. 23. Each model evaluates the anonymized responses based on its self-generated criteria. 24. External models are introduced to provide additional evaluations. 25. Scores are aggregated to determine the top-performing model. --- ## Appendix: Potential Questions and Their Validity Below are examples of tasks designed to align with the unique strengths of LLMs: 26. **Medical Diagnosis:** *Given a patient’s symptoms, medical history, and lab results, diagnose the condition and suggest treatment options.* *(Validates clinical reasoning and decision-making.)* 27. **Climate Modeling:** *Analyze historical climate data to predict future trends and propose mitigation strategies.* *(Assesses predictive modeling and policy recommendations.)* 28. **Ethical Dilemmas:** *Allocate limited resources during a pandemic, balancing public health needs with economic impacts.* *(Tests ethical judgment and prioritization skills.)* 29. **Creative Problem-Solving:** *Design a sustainable urban infrastructure plan considering environmental, social, and economic factors.* *(Evaluates creativity and systemic thinking.)* --- ## Edge Cases to Test Hallucinations and