| _version_ | 1866902300845408256 |
|---|---|
| author | Zeghloul, Mourad |
| author_facet | Zeghloul, Mourad |
| contents | <div dir="ltr"> <h3><strong>TT-Distill: Test-Time Distillation Architecture via Latent Algebraic Swapping for Autonomous Agents</strong></h3> <p><strong>Abstract</strong> 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 <strong>TT-Distill</strong>, a dual-pillar neuro-symbolic architecture designed to migrate intelligence from analytical reasoning (System 2) to reflexive execution (System 1) in near real-time.</p> <p>The architecture acts as a continuous topological router, discovering mathematical invariants in abstract reasoning tasks and crystallizing them into ultra-lightweight <strong>DoRA</strong> (Weight-Decomposed Low-Rank Adaptation) modules. By leveraging a custom Zero-Copy Apple Metal C++ backend, TT-Distill achieves an <strong>$O(1)$ cognitive context-switching latency of 0.0002 ms</strong>, effectively bypassing the Metal compute graph recreation penalty.</p> <p><strong>Key Technical Achievements:</strong></p> <ul> <li><strong>Hardware-Native Swapping</strong>: Median DoRA hot-swap latency of <strong>208 nanoseconds</strong>, a 1,000,000x speedup over standard weight re-initialization.</li> <li><strong>High-Frequency Inference</strong>: Sustained operational frequency of <strong>92.2 Hz</strong> (10.8 ms per step) on consumer-grade Apple Silicon (M2 Max).</li> <li><strong>Transductive Learning Gains</strong>: Migration of complex code-generation solutions (System 2) into reflexive manifolds (System 1) reduces task resolution time from <strong>65 seconds to under 6 seconds</strong> on the ARC-AGI benchmark.</li> <li><strong>Neuro-Symbolic Hybridization</strong>: A tri-engine execution framework (Heuristic, Pure Math, Latent Algebraic) ensuring zero-hallucination compliance via formal topological filters.</li> </ul> <p>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.</p> <p><strong>Keywords</strong>: <em>Neuro-symbolic AI, Test-Time Distillation, Apple Silicon, Metal Performance, ARC-AGI, DoRA, Latent Algebraic Swapping.</em></p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18905253 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TT-Distill: Continuous Neuro-Symbolic Routing via O(1) Latent Algebraic Swapping Zeghloul, Mourad Artificial Intelligence Test-Time Distillation Latent Space Communication DoRA Liquid Foundation Models (LFM) Autonomous Agents ARC-AGI-2 DORA-RS Edge AI <div dir="ltr"> <h3><strong>TT-Distill: Test-Time Distillation Architecture via Latent Algebraic Swapping for Autonomous Agents</strong></h3> <p><strong>Abstract</strong> 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 <strong>TT-Distill</strong>, a dual-pillar neuro-symbolic architecture designed to migrate intelligence from analytical reasoning (System 2) to reflexive execution (System 1) in near real-time.</p> <p>The architecture acts as a continuous topological router, discovering mathematical invariants in abstract reasoning tasks and crystallizing them into ultra-lightweight <strong>DoRA</strong> (Weight-Decomposed Low-Rank Adaptation) modules. By leveraging a custom Zero-Copy Apple Metal C++ backend, TT-Distill achieves an <strong>$O(1)$ cognitive context-switching latency of 0.0002 ms</strong>, effectively bypassing the Metal compute graph recreation penalty.</p> <p><strong>Key Technical Achievements:</strong></p> <ul> <li><strong>Hardware-Native Swapping</strong>: Median DoRA hot-swap latency of <strong>208 nanoseconds</strong>, a 1,000,000x speedup over standard weight re-initialization.</li> <li><strong>High-Frequency Inference</strong>: Sustained operational frequency of <strong>92.2 Hz</strong> (10.8 ms per step) on consumer-grade Apple Silicon (M2 Max).</li> <li><strong>Transductive Learning Gains</strong>: Migration of complex code-generation solutions (System 2) into reflexive manifolds (System 1) reduces task resolution time from <strong>65 seconds to under 6 seconds</strong> on the ARC-AGI benchmark.</li> <li><strong>Neuro-Symbolic Hybridization</strong>: A tri-engine execution framework (Heuristic, Pure Math, Latent Algebraic) ensuring zero-hallucination compliance via formal topological filters.</li> </ul> <p>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.</p> <p><strong>Keywords</strong>: <em>Neuro-symbolic AI, Test-Time Distillation, Apple Silicon, Metal Performance, ARC-AGI, DoRA, Latent Algebraic Swapping.</em></p> </div> |
| title | TT-Distill: Continuous Neuro-Symbolic Routing via O(1) Latent Algebraic Swapping |
| topic | Artificial Intelligence Test-Time Distillation Latent Space Communication DoRA Liquid Foundation Models (LFM) Autonomous Agents ARC-AGI-2 DORA-RS Edge AI |
| url | https://doi.org/10.5281/zenodo.18905253 |