TT-Distill: Continuous Neuro-Symbolic Routing via O(1) Latent Algebraic Swapping

TT-Distill: Test-Time Distillation Architecture via Latent Algebraic Swapping for Autonomous Agents Abstract As of early 2026, the AI industry faces a significant bottleneck in latency and reliability for autonomous agents, primarily due to the heavy reliance on expansive Test-Time Compute (e.g., MCTS) over monolithic models. This research introduces TT-Distill, a dual-pillar neuro-symbolic architecture designed to migrate intelligence from analytical reasoning (System 2) to reflexive execution (System 1) in near real-time. The architecture acts as a continuous topological router, discovering mathematical invariants in abstract reasoning tasks and crystallizing them into ultra-lightweight DoRA (Weight-Decomposed Low-Rank Adaptation) modules. By leveraging a custom Zero-Copy Apple Metal C++ backend, TT-Distill achieves an $O(1)$ cognitive context-switching latency of 0.0002 ms, effectively bypassing the Metal compute graph recreation penalty. Key Technical Achievements: Hardware-Native Swapping: Median DoRA hot-swap latency of 208 nanoseconds, a 1,000,000x speedup over standard weight re-initialization. High-Frequency Inference: Sustained operational frequency of 92.2 Hz (10.8 ms per step) on consumer-grade Apple Silicon (M2 Max). Transductive Learning Gains: Migration of complex code-generation solutions (System 2) into reflexive manifolds (System 1) reduces task resolution time from 65 seconds to under 6 seconds on the ARC-AGI benchmark. Neuro-Symbolic Hybridization: A tri-engine execution framework (Heuristic, Pure Math, Latent Algebraic) ensuring zero-hallucination compliance via formal topological filters. TT-Distill demonstrates that real-time AGI on Edge hardware is achievable by treating intelligence as a sequence of instantaneous geometric projections rather than probabilistic token generation. Keywords: Neuro-symbolic AI, Test-Time Distillation, Apple Silicon, Metal Performance, ARC-AGI, DoRA, Latent Algebraic Swapping.