SERR-CASCADE: Hierarchical, Risk-Aware Architecture for Multi-Bottleneck LLM Inference Optimization

SERR-CASCADE introduces a novel, coordinated hierarchical architecture designed to address the multiplicative performance bottlenecks in Large Language Model (LLM) inference: repeated context overhead, per-token compute waste, and memory bandwidth limitations. By integrating risk-aware routing and multi-layer control mechanisms, this framework achieves significant simulated speedups across diverse agentic workloads.

Understanding LLM Inference Bottlenecks

Current LLM inference systems often rely on single-layer optimizations (such as entropy routing, KV quantization, or semantic-delta routing). However, the core argument presented by SERR-CASCADE is that LLM inference is constrained by three distinct, interacting bottlenecks. These bottlenecks—repeated context across turns, inefficient per-token compute, and memory bandwidth constraints—interact multiplicatively within the cost stack. The proposed solution is a holistic, coordinated hierarchical approach rather than isolated optimizations.

The SERR-CASCADE Architecture (6 Layers)

The SERR-CASCADE framework is structured across six distinct layers, each responsible for managing a specific aspect of efficiency and risk propagation. This coordination allows the system to intelligently skip processing or adjust fidelity based on the perceived state change and risk profile.

Layer Breakdown:

  • L0: Turn-level Semantic-Delta Routing: Optimizes by skipping entire conversational turns that exhibit no meaningful state change.
  • L1: Span-Coherent Kernel Batching: Focuses on optimizing kernel launches, differentiating this from span-level routing found in prior literature.
  • L2: Token-level Routing: Implements a core risk-aware mechanism, incorporating severity-weighted danger overrides and causal-correct risk propagation.
  • L3: Adaptive Evidence KV: Manages memory efficiency using an FP8/INT8 hybrid format, augmented by prefix caching and raw anchors for preserving critical factual information.
  • L4: Shadow Verification: