ASH-KV: Dynamic Attention Steering & KV-Cache Integrity Middleware

ASH-KV (Asynchronous Self-Healing KV Cache) is a high-performance middleware layer designed for Runtime Manifold Integrity Enforcement. It leverages silicon-native kernels to monitor the mathematical uncertainty (Varentropy) of the Attention Manifold and surgically prunes logical drift at the hardware level.

Technical Methodology

Varentropy-Proxy Monitoring

ASH-KV implements a deterministic uncertainty detector by analyzing the mathematical variance across the KV-Cache. This real-time analysis identifies Manifold Collapse—the mathematical state where a model's transition probability distribution becomes unstable—allowing for intervention before semantic errors materialize.

Real-Time Attention Steering

When uncertainty exceeds the threshold, ASH-KV executes a Gaussian Manifold Mutation directly within the compute graph.

• Apple Silicon: Leverages @mx.compile Fused Metal kernels for sub-millisecond mutation.
• NVIDIA: Implements synchronized PyTorch/CUDA tensor operations via the Hardware Abstraction Layer (HAL).
• Latency: Verified at < 0.9ms on Apple M4 hardware (negligible inference overhead).

Infinite Horizon (NVMe Paging)

To bypass physical VRAM constraints, ASH-KV utilizes an LRU-based paging protocol. Inactive context chunks are offloaded to NVMe storage using zero-copy memory mapping, enabling 100k+ token windows on consumer-grade hardware.

API Reference

protect(model, sensitivity=0.85, critic_model_path=None)

Initializes the ASH-KV Hypervisor for a given neural model.

Research & Reproducibility

Our benchmarks use time.perf_counter_ns() to track the exact overhead of the Fused Metal Mutations.

ash-kv install    # Platform driver verification
ash-kv benchmark  # Unified Latency & Integrity suite

Hardware Abstraction Layer (HAL)

• MLXHealer: Fused Metal backends for macOS.
• CudaHealer: Synchronized tensor backends for NVIDIA/Linux.
• UniversalTensorCritic: Pure mathematical manifold evaluation.

DISCLAIMER

ASH-KV is a probabilistic reliability layer. It is NOT a substitute for professional clinical or legal judgment. All AI-generated outputs must be verified by qualified human professionals.

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