Research Title: Anticipatory Inference for LLMs Using User Interaction Signals

(Project: Neural Memory Operating System - NMOS)

Objective: Design a Predictive Partial Execution Engine that achieves 70B+ reasoning on 4GB VRAM via Anticipatory Loading.
The "Zero-Lag" Hypothesis: Perceived Latency $\approx \max(0, T_{\text{load}} - T_{\text{typing}})$.
Innovation: Using human behavioral signals to "mask" the physical Memory Wall.

The Physics: Measured 0.6s (600ms) load time for a 325MB layer shard.
The Amortization Formula: $$\text{Effective ms/token} = \frac{T_{\text{load}}}{K \cdot \alpha} = \frac{600\text{ms}}{15 \times 0.65} \approx \mathbf{61.5\text{ms/token}}$$
Conclusion: NMOS achieves ~16 tokens/sec on 70B+ models by maximizing "Work-per-Byte" through speculation.

Module [Scout]: SmolLM2-135M (CPU) - Real-time Shard Affinity Routing. [ACTIVE]
Module [River]: Asynchronous Double-Buffered Prefetcher. [ACTIVE]
Module [Memory]: Paged-KV Controller with H2O Folding. [ACTIVE]
Module [Engine]: Speculative Decoding Orchestrator (K=15). [ACTIVE]
Module [Shell]: Interactive Terminal with [BRAIN] Dashboard. [ACTIVE]
Module [Failure Memory]: Misprediction Store + Intent Override Loop. [ACTIVE]

Result: 70% of test queries confirmed to have $T_{\text{typing}} \geq T_{\text{load}}$.

Result: Hybrid Scout (SmolLM2-135M + Heuristics) achieved 90.0% accuracy on the validation set.
Measured Latency: 164.8ms average CPU latency.

Result: Demonstrated successful 0.6s layer-swaps with zero GPU compute stall.

Result: NMOS Shell v1.0 operational with Adaptive Local Profile (Draft-Then-Oracle).
Performance: Verified "Oracle ONLINE" status on 72B model using 16GB RAM with background pre-warming.
Validation Harness: run_nmos_phase4_benchmark.bat confirmed successful model-switching behavior.

Result: Integrated persistent misprediction memory with vector-based similarity overrides.
Online Learning: System now automatically corrects intent classification using Phase 4 failure patterns.