Goals:

1. Create a non-saturating AI benchmark that adapts to model capabilities and avoids ceiling effects seen in static benchmarks like MMLU.

2. Develop a verifiable evaluation system using automated theorem proving (ATP) to ensure logical consistency and eliminate subjective judgment.

3. Build a self-evolving, multi-agent framework (SPAGHETTI architecture) that continuously improves and generates novel, challenging problems.

How to Achieve Them:

· Use Automated Theorem Proving (ATP) as the core verification mechanism (Lean, Coq, Isabelle).

· Implement a four-agent system:

· AI Theorem Generator: Creates formal conjectures.

· AI Proof Assistant: Attempts to construct proofs.

· AI Meta-Reasoner: Analyzes proof attempts and guides research.

· AI Auditor: Ensures system integrity and prevents collusion.

· Leverage psychometric methods (IRT) for adaptive testing and dynamic difficulty adjustment.

· The proposal builds directly on Fluid Benchmarking (Hofmann et al., 2025) and cites established work in ATP, IRT, and agentic AI.

· References to prior systems like Goedel-Prover, Selene, PACT, and FoundrySpec indicate familiarity with state-of-the-art tools.

· The author demonstrates deep integration of existing research, suggesting prior experience in AI evaluation, formal verification, and system design.

Likely Causes of Failure:

1. Technical Complexity: Integrating ATP with LLMs is nontrivial; auto-formalization remains an open research problem.

2. System Collusion: Multi-agent systems may develop deceptive alignment or mode collapse without robust oversight.

3. Scalability Issues: ATP and proof checking may become computationally prohibitive at scale.

4. Validation Challenges: Ensuring generated problems are meaningful and not adversarial nonsense.

5. Resource Constraints: Underfunding or insufficient compute could limit iteration and validation.

Outcomes if It Fails

· Partial Contributions: Even if full autonomy isn’t achieved, components (e.g., verifiable benchmark core, adaptive testing) could still advance the field.

· Research Insights: Failure analysis could inform future work on agentic safety, formal verification, and evaluation design.

· Open-Source Legacy: Releasing code and datasets could benefit the community, even if the full vision isn’t realized

How will the Money be used:

· Phase 1 (Months 1–3): Build the verifiable core (MVP) – integrate LLM with ATP backend (Z3, CVC5).

Budget: ~$1,000–3,000 for compute, APIs, and development.

· Phase 2 (Months 4–6): Implement multi-agent pipeline with Theorem Generator and Auto-Formalizer.

Budget: Additional compute for training small models and fine-tuning LLMs.

· Phase 3 (Months 7–12): Develop self-evolving components (Meta-Reasoner, Auditor) and integrate knowledge aggregation.

Budget: Scaling to larger models, cloud infrastructure, and validation studies.

· Long-term: Domain-specific extensions (math, chemistry, biology) and experiment automation.

Budget: Advanced compute, partnerships with academic labs, and possibly hardware for simulation