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Recursive Micro-Networks for Efficient Reasoning

Engineer lightweight neural architectures that iterate on their own outputs to rival larger models in structured reasoning tasks.

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5. Evaluation and Benchmarking

Assessing recursive reasoning requires more than final accuracy.

Metrics Portfolio#

  • Task Accuracy: Percentage of correct final answers on benchmarks like ARC-AGI, GSM8K, or structured planning suites.
  • Depth Efficiency: Average number of steps to converge. Monitor variance to detect instability.
  • Progress Gain: Improvement in confidence or solution quality per step, visualized as convergence curves.
  • Self-Critique Quality: Alignment between verifier critiques and actual errors.
  • Resource Usage: Compute cost per solution, memory footprint, and latency.

Dashboard Visualization#

Create dashboards showing reasoning trajectories. Plot each iteration’s proposed solution, critique, and confidence. Highlight loops or regressions for debugging.

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