RIS-Kernel: A Model-Agnostic Architecture for Long-Context LLM Inference via Sparse Attention

Full self-attention in large language models scales as O(N^2), limiting long-context analysis to 65k tokens and requiring costly GPU clusters. The Reduced Interaction Sampling (RIS) inference engine addresses this as a model-agnostic architecture. Without modifying weights, RIS introduces sparse stochastic attention geometries that operate under extreme pruning (up to 99% under 1% density) while preserving factual retrieval. While foundational graph-theoretic RIS formulations establish an asymptotic O(N log N) bound, this work evaluates a practical fixed-density regime on unaccelerated commodity CPU systems. We validate RIS on Qwen2-1.5B-Instruct across two regimes. At 32,768 tokens (where native dense attention serves as the upper bound), RIS-Stochastic at 1% density and 70 ensemble seeds achieves 75.00% accuracy, outperforming the native dense baseline (71.88%), while 5% density at 10 seeds matches it (71.88%), demonstrating that multi-seed sparsity filters sequence-level noise. Crucially, a controlled ablation evaluating a single static mask at matched budget (N=1, d=0.50) yields only 53.12% accuracy, proving that multi-seed topological diversity, rather than raw edge density, drives contextual recovery. Under tight budgets, RIS-Structural reaches 65.62% accuracy at 1% density with 10 seeds (recovering 50.0% of the gap over the 59.38% zero-context floor) and 68.75% (75.0% recovery) with 30 seeds. At 65,536 tokens, where dense attention triggers out-of-memory faults, RIS yields retrieval gains of up to 14.06 percentage points over the zero-context floor (51.56%), confirmed as marginally significant under McNemar's paired test (p = 0.078 < 0.10). All evaluations run on commodity CPU servers (16-128 GB RAM), demonstrating that long-context LLM inference is feasible on standard academic hardware without GPU acceleration.