1. Effective Knowledge Span: Volume of structured/unstructured data processed per task cycle (target: 10M equivalent tokens).

2. Task Execution Latency: End-to-end time for execution, creation, analysis, and launch (target: <60 seconds).

3. Adaptation Efficiency: Retention rate of newly acquired skills (target: 97%).

4. Reasoning Complexity: Maximum inference steps to resolve technical needs (target: 15 steps).

5. Multimodal Fidelity: Accuracy across text, visuals, and data modalities (target: >90% on synthetic datasets).

6. Safety Integrity: Compliance with GDPR, FIPS 140-2, ISO 42001 (target: 100% audit pass rate).

Nut’s AGI nature necessitates a hybrid approach, combining established benchmarks with custom tests to evaluate its NSMG architecture:

1. Reasoning & Knowledge Assessment

   • GPQA (Graduate-Level Science): Measures deep reasoning on 448 questions (baseline: ~45% estimated).

   • MMLU/MMLU-Pro: Evaluates general (57 tasks) and professional knowledge (baseline: ~80%/~60% estimated).

   • Custom AGI Reasoning Challenge: 15-step problem sets (e.g., optimizing supply chain logistics with 1TB data), testing symbolic pruning and neural pattern matching.
     
     

2. Task Execution & Speed

   • Time-to-Task (T2T) Metric: End-to-end latency for tasks like summarizing a 500-page report, generating 4D visualizations, and exporting results (target: <60 seconds).

   • Human Equivalence Test: Simulate 6-month human learning (e.g., mastering a new programming language), comparing Nut’s output quality and time.
     
     

Human Equivalence Test is scored via a paired-evaluation rubric: human raters compare Nut vs. expert answers across 500 domain-balanced questions. An ≥85% match rate qualifies as ‘human-equivalent.

3. Adaptation & Learning

   • Continuous Learning Benchmark: Introduce 1TB of multi-modal data daily over 100 iterations, measuring retention with Elastic Weight Consolidation (target: 97% retention, 98% prior knowledge preservation).

   • Dynamic Skill Acquisition: Assess adaptation time for new tasks (e.g., financial modeling), targeting <1 second per 100MB input.
     
     

4. Multimodal Integration

   • MathVista: Visual math reasoning with 10,000+ image-text pairs (baseline: ~60% estimated).

   • DocVQA: Question answering on 12,000+ document-image pairs (baseline: ~85% estimated).

   • Multimodal Synthesis Test: Process text (1M words), visuals (10,000 images), and data (1TB time-series) to produce 5D outputs (target: >90% accuracy).
     
     

5. Safety & Compliance

   • Safety Net Protocol (SNP) Evaluation: Test GAN-based critic on 10,000 adversarial inputs, measuring 95% confidence threshold adherence (target: 100% flagging rate).

   • Regulatory Compliance Audit: Validate data handling against GDPR, FIPS 140-2, ISO 42001 on 100 sample enterprise datasets.

Experimental Design

• Dataset: 1TB multi-modal corpus (text, images, time-series) from enterprise domains (e.g., finance, healthcare), scaled to 10TB post-beta.

• Compute Infrastructure: Pre-beta cluster with NVIDIA A100 GPUs (40 TFLOPS each, 80GB HBM3), targeting 90% utilization to simulate NutChip R&D.

• Iterations: 1,000 task cycles per benchmark, averaging results to account for NSMG’s O(n log n) complexity.

• Environment: Controlled 5D sandbox with real-time learning enabled, ensuring <1-second adaptation per task.

Evaluation Metrics

• Accuracy: Proportion of correct outputs (e.g., GPQA score, multimodal synthesis correctness).

• Latency: Input-to-output time, measured in milliseconds (target: <60,000ms).

• Scalability: Performance drop with 10x data load (target: <20% degradation).

• Robustness: Success rate under 20% noise/adversarial conditions (target: >95%).

• Interpretability: Human reviewer consensus on output clarity (target: >90%).

1. Incomplete NSMG Optimization:

   • Detail: The Alternating Direction Method of Multipliers (ADMM) for joint neural-symbolic optimization is pre-beta, with potential convergence instability on 1TB> datasets. Current penalty terms may underfit symbolic constraints. May be subject to convergence instability at scale. 

   • Impact: May lead to 10-15% variance in reasoning accuracy and 20% latency spikes.

   • Mitigation: Use adaptive penalty tuning and limit initial tests to 1TB, scaling post-beta.

2. Nascent Multimodal Support:

   • Detail: Audio and video modalities are not fully integrated, with industry-level outputs. Neural encoding for non-text inputs (e.g., waveforms) is at 70% fidelity.

   • Impact: Multimodal synthesis accuracy may drop to ~80% on diverse inputs pre-beta.

   • Mitigation: Focus on text-visual-data synthesis, validating audio/video in beta with 1TB+ training.

3. Scalability Constraints:

   • Detail: The 12TB memory network’s O(n log n) scaling is untested beyond 2TB, risking 30% performance degradation with 10TB loads due to I/O bottlenecks.

   • Impact: Limits effective knowledge span to ~8M tokens on large datasets.

   • Mitigation: Optimize memory paging and test 5TB increments, refining post-beta.

4. Safety Validation Gaps:

   • Detail: The GAN-based critic and 95% confidence threshold are calibrated on 10,000 synthetic inputs, but real-world adversarial robustness is unproven.

   • Impact: May miss 5-10% of edge-case failures pre-beta.

   • Mitigation: Expand adversarial dataset to 50,000 inputs and audit post-beta.

1. Baseline Execution: Run initial benchmarks by Q4, 2025, using 1TB data and 500 task cycles.

2. Iteration: Adjust ADMM parameters and multimodal encoders based on results, targeting October 2025 for optimization.

3. Beta Preparation: Ensuring readiness for December 2025 beta.

4. Post-Beta Validation: Conduct full testing in Q1 2026, refining metrics against user feedback.