ceceli33/titan-cognitive-core-v2: AKBASCORE MAM v1.4 — Silicon Hippocampus | Native Cognitive Memory Cartridges, Tensor Injection & Multi-Cartridge Relational Synthesis in a Frozen LLM

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23381336

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10.5281/zenodo.23381336

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Nov 5, 2026

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AKBASCORE AI TECHNOLOGY — MAM SILICON HIPPOCAMPUS v1.4 Native Internal Tensor Memory Injection, Append-Only Cognitive Cartridge Consolidation and Multi-Hop Relational Synthesis Mustafa Akbaş | AkbasCore AI Teknoloji | Mersin, Türkiye | October 2026 A frozen language model can receive independently constructed numerical memory cartridges, consolidate their internal attention states, and answer relational questions without the original source facts being supplied again as text. AKBASCORE MAM v1.4 demonstrates this capability using Qwen2.5-7B-Instruct with unchanged model weights, no fine-tuning, no LoRA and no optimizer. The central technical development is the transition from individual numerical memory cartridges to consolidated, multi-record model-native memory. Rather than retrieving source documents and inserting their text into the prompt, the system constructs numerical representations from the frozen transformer's own internal computations. These representations include layer-3 residual states and attention key/value tensors. Independent cartridges are combined through a native append-only CUT3 memory pathway. The consolidated numerical memory is subsequently used by the frozen transformer during inference. The model does not receive the original source facts again in the answering prompt. The memory is introduced through numerical attention states, not through conventional text retrieval. This is the distinguishing architectural mechanism investigated in the release. From physical cognitive cartridges to numerical memory consolidation The previous Silicon Hippocampus demonstrated WRITE · SSD · READ using 1,453 independent physical numerical memory cartridges, native PCA128 and FAISS IVF128 addressing, and PEAK50 cartridge selection. That system reported 48/48 correct selections and answers under each of two question-wording panels covering the same 48 synthetic facts. Version 1.4 extends the research from retrieving individual cartridges to combining multiple independently written cartridges. Its implementation demonstrates: Native numerical cartridge construction from frozen-model internal states. Layer-3 residual representation capture and injection. Attention K/V memory construction across 28 transformer layers. Append-only consolidation of independent cognitive cartridges. Bitwise preservation checks for previously written memory rows. Source-free factual answering from consolidated numerical memory. Three-stage relational question sequences. Counterfactual memory replacement and missing-memory controls. Internal numerical memory X-Ray visualization. Experimental comparison against a text-based memory control. Experimental evidence — Silicon Hippocampus v1.4 The published execution used an NVIDIA A100-SXM4-40GB GPU, Qwen2.5-7B-Instruct, bfloat16 precision and SDPA attention. AKBASCORE scenario Three independent cartridges were consolidated into 109 numerical memory rows, representing 6,250,496 bytes of K/V memory. The frozen model recovered the following relational sequence: AKBASCORE MAM → Morvex → reversal of time flow The complete-memory condition produced the expected direct answers and all three answers in the predefined relational sequence. Replacing the final cartridge with a counterfactual version changed the final answer to temporal stasis. The Matrix / Neo scenario Three independent cartridges were consolidated into 115 numerical memory rows, representing 6,594,560 bytes of K/V memory. The frozen model recovered: MX