Neural Convergence: Scaling Transformer Architectures

This paper examines how layered routing, head pruning, and sparse attention can preserve accuracy while lowering compute cost across enterprise multimodal workloads.

• Up to 28% lower inference cost with no measurable loss in benchmark accuracy
• Sparse routing reduced token latency on long-context tasks
• Measured on document synthesis, code generation, and multimodal retrieval workloads

1. 1Benchmarked a family of transformer variants across 24 enterprise task sets
2. 2Applied token-level pruning with a capped residual routing strategy
3. 3Validated results using latency, throughput, and quality regression baselines

• Long-context summarization benefited most from adaptive sparse attention
• Quality degradation stayed below the 1% threshold in all evaluated tasks
• The approach scaled best when paired with caching-aware inference scheduling

The findings suggest that transformer scaling can remain economically viable when sparsity, routing, and caching are designed together rather than added as isolated optimizations.