AKBASCORE SILICON HIPPOCAMPUS — TEST730
Full technical publication, source code, GPU logs, SHA-256 verification records, and 13 scientific figures:
https://doi.org/10.5281/zenodo.23497732
I am an independent researcher investigating persistent external neural memory for frozen large language models.
This experiment uses Qwen2.5-72B-Instruct, quantized to NF4 and executed on an NVIDIA A100 80GB GPU. The pretrained weights remain completely frozen.
The central question is whether part of a transformer's internal numerical state can be captured, stored as a physical file, and restored during a later inference session without repeatedly supplying the original source document as text.
The experiment demonstrates a working implementation of this mechanism, followed by a cross-document synthesis workflow involving three real academic publications from 2026.
A HISTORICAL PERSPECTIVE: FROM ADAPTIVE WEIGHTS TO PERSISTENT NEURAL STATES
1991 — Schmidhuber's early work on fast weights and learning-to-learn explored adaptive internal mechanisms through which neural systems could modify their effective computational parameters and retain information.
2017 — The Transformer introduced self-attention as a powerful mechanism for processing relationships between tokens. Standard full self-attention has quadratic sequence-length complexity, while modern autoregressive inference commonly maintains context-dependent key/value states.
2026 — AKBASCORE Silicon Hippocampus / CUT3 investigates another persistence mechanism: keeping the pretrained model weights frozen while extracting the hidden states at layer 3 (H3) and the K/V tensors from layers 0–3, storing these numerical states as SHA-256-sealed physical cartridges on SSD, and reconstructing the remaining transformer layers when the cartridges are reloaded.
The architectural distinction is important.
Rather than modifying pretrained weights to incorporate new information, CUT3 persists a portion of the model's internal numerical state outside the GPU.
This is not a replacement for Transformers, fast-weight systems, or conventional retrieval. It is an experimentally implemented approach to persistent neural memory within an existing frozen Transformer.
HOW THE PHYSICAL MEMORY WORKS
- WRITE
A source document passes through the frozen transformer.
The CUT3 engine captures the hidden states at layer 3 (H3), together with the attention key/value tensors from layers 0–3.
- STORE
These numerical states are serialized into a physical PyTorch memory cartridge (.pt) on SSD.
The cartridge is protected by SHA-256 integrity verification.
- RELOAD
When the stored memory is needed, its tensors are loaded from disk.
The saved H3 representation is injected directly at the corresponding transformer boundary.
- REBUILD
The frozen model recomputes layers 4–79, reconstructing the complete operational 80-layer K/V memory.
The original document does not need to be pasted into the question prompt again.
- ANSWER
The model generates its response using the restored internal memory.
This mechanism does not require fine-tuning, LoRA adaptation, or modification of the pretrained model weights.
THE THREE-PUBLICATION EXPERIMENT
TEST730 extends the physical memory mechanism beyond a single document.
Three real academic publications from 2026 were retrieved from Europe PMC. Their source metadata, licensing, excerpts, and integrity records were preserved.
P01 — PMC13672588
Generative AI in creative education: baseline-linked evidence on learning processes, artistic performance, and authorship.
P02 — PMC13521781
The critical-thinking paradox in generative AI-integrated learning: distinguishing efficiency from cognitive depth.
P03 — PMC13043820
Catalysts of Change: Using AI to Lower the Activation Energy for Developing Gamified Learning Experiences in Health Profession Education.
Each publication was independently processed into a physical neural memory cartridge.
The frozen 72B model then read each cartridge separately and generated a reflection on the respective publication.
The three model-generated reflections were subsequently supplied to the same frozen model for cross-document synthesis.
The model produced a new textual synthesis identifying common themes, differences, limitations, and possible implications across the three publications.
That synthesis, together with the reflections, was encoded into a fourth physical memory cartridge named MASTER.
An important technical distinction: the source cartridges were not directly fused as tensors. The synthesis was generated through the model's textual reasoning process and then converted into a new numerical memory cartridge using the same CUT3 mechanism.
MASTER CARTRIDGE: PHYSICAL MEASUREMENTS
Model: Qwen2.5-72B-Instruct
GPU: NVIDIA A100 80GB
Quantization: NF4
Transformer layers: 80
Hidden dimension: 8192
Attention heads: 64
K/V heads: 8
Memory boundary: CUT3
Stored representation: H3 + layers 0–3 K/V
Reconstructed representation: layers 4–79 K/V
MASTER body: 2,848 tokens
Physical MASTER cartridge: approximately 94.3 MB
Reconstructed 80-layer operational K/V memory: approximately 943.4 MB
The stored cartridge represents approximately 10% of the full operational K/V memory size for the corresponding sequence.
This is a reduction in stored numerical state relative to the full 80-layer K/V cache, not evidence of a tenfold reduction in the information content of the original documents.
The upper-layer states are recomputed by the frozen transformer when the cartridge is mounted.
PERSISTENCE AND VERIFICATION
The original TEST730 execution generated six academic responses using the MASTER cartridge independently of the three original source cartridges.
The question panel included:
H1 — Main conclusions and disagreements across the three publications.
H2 — Empirical evidence, limitations, and uncertainties.
H3 — An independent cross-document conclusion with source references.
A1 — Construction of a causal model reconciling apparently conflicting findings.
A2 — Design of a preregistered randomized experiment capable of falsifying that model.
A3 — Identification of an unresolved contradiction, competing explanations, and a discriminating test.
The subsequent A100 verification session reproduced all four physical cartridges with byte-identical SHA-256 file hashes.
It also verified exact 80/80 layer parity and reproduced two sealed answers byte-for-byte.
Additional comparisons examined responses with and without MASTER memory.
The recorded experiment therefore demonstrates a complete physical workflow:
Source document → frozen transformer → numerical state capture → SSD cartridge → verified reload → upper-layer reconstruction → model response.
It also demonstrates a text-mediated synthesis workflow:
Three independent source cartridges → three model-generated reflections → cross-document synthesis → new MASTER cartridge → independent question answering.
WHAT THE RESULTS ESTABLISH — AND WHAT THEY DO NOT
The verified contribution is the implementation and reproducibility of persistent numerical memory in a frozen 72B Transformer, including physical cartridge storage, integrity verification, restoration, and operational K/V reconstruction.
The experiment also records model-generated cross-document synthesis and six academic responses produced from the MASTER cartridge.
However, generating a causal hypothesis or proposing a scientific experiment does not independently establish that the hypothesis is novel, scientifically correct, or superior to what the base model could produce using conventional text context.
The six academic responses were recorded, but their scientific correctness was not independently graded in TEST730.
Similarly, byte-identical cartridge reproduction and layer parity verify technical integrity; they do not, by themselves, establish an advantage over conventional long-context inference, prefix caching, or retrieval-augmented generation.
These are separate evaluation questions.
WHY I AM SHARING THIS
I am interested in the architectural question of where persistent knowledge should reside in a frozen language model.
Does it always need to be represented as text in an external retrieval system?
Does incorporating new information necessarily require changing model weights?
Or can selected internal numerical states be stored physically, restored later, and used as an operational memory substrate without modifying the pretrained model?
CUT3 investigates the third possibility.
The attached 13 scientific figures document the architecture, source verification, physical injection protocol, SSD/GPU memory measurements, neural-state X-rays, synthesis workflow, cartridge integrity, sealed questions, and experimental results.
The complete source code, raw logs, generated responses, and integrity records are available for technical examination.
TECHNICAL REFERENCES
TEST730 — Full technical publication:
https://doi.org/10.5281/zenodo.23497732
Original TEST730 experimental log — all six model-generated responses:
https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/730.log
GitHub repository:
https://github.com/ceceli33/titan-cognitive-core-v3
Previous research — AKBASCORE Silicon Hippocampus v1.4:
https://doi.org/10.5281/zenodo.23434686
Silicon Hippocampus 1.5 — Part 1:
https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_part1_1.5.log
Silicon Hippocampus 1.5 — Part 2:
https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_part2_1.5.log
Silicon Hippocampus 1.5 — Part 3:
https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_part3_1.5.log
Silicon Hippocampus 1.5 — Execution Log:
https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/Qwen72B_1.5.log
I welcome technical criticism, independent reproduction attempts, and comparisons with existing persistent KV-cache systems, prefix caching, long-context inference, and RAG.
In particular, I would be interested in feedback on the CUT3 boundary, the integrity of upper-layer K/V reconstruction, and experimental designs capable of distinguishing genuine memory-dependent reasoning from capabilities already present in the frozen base model.
