The project explores whether introducing an explicit governance layer on top of multiple specialists can change system behavior under adverse conditions. Instead of focusing solely on prediction aggregation, the governance layer evaluates specialists, aggregates diagnostic signals, adjusts trust, performs bounded mitigation, and produces an auditable decision trace.
So far I've completed three benchmarks covering: a- Predictive Performance. b- Robustness under multiple corruption families. c- Reproducibility and stability.
From these benchmarks, we see that MAVS-GC has competitive predictive performance.
However,the more interesting behavior appeared under severe corruption. Across high-corruption conditions (corruption level ≥ 0.6), MAVS-GC achieved 85.30% accuracy with 0.45% unsafe acceptance, while the ensemble baselines averaged 43.24% accuracy with 67.61% unsafe acceptance.
The reproducibility benchmarks also showed stronger prediction, decision, consensus, and trace stability under corruption, although these improvements came with increased rejection rates.
So for here atleast this is it, I'd genuinely accept any suggestions or critisicms of the architecture. The link of pdf is provided in it there are also repos of the in-respect benchmarks.
However, for those of you who want to get straight to the code, here it is.