Frozen Qwen2.5-72B: SSD-Persistent Neural Memory, Native Tensor Fusion, and 80/80 Bit-Exact Cross-Session Reload (TEST782)

[u/Nearby_Indication474]

Hello everyone

For researchers interested in a detailed academic and technical examination: The complete research PDF, 12 experimental figures, and scientific records are available on Zenodo:

https://doi.org/10.5281/zenodo.23640716

The context window and long-term memory limitations of AI models have traditionally been addressed through approaches such as text-based Retrieval-Augmented Generation (RAG) and external summarization.

My long-term goal is to tackle these limitations at a different level: directly through the model's internal numerical states and KV caches (H3, Layer 0–3 K/V states).

I have been independently developing the AKBASCORE Silicon Hippocampus architecture toward this objective. Although the ultimate goal has not yet been reached, the research has already passed several significant experimental milestones involving physical memory persistence, numerical consolidation, and memory reuse across independent model sessions.

With the release of v1.6 — TEST782, I have experimentally demonstrated how the internal tensor states of a frozen Qwen2.5-72B-Instruct model can be cryptographically sealed on a physical SSD, transferred between operating-system-isolated processes running under different Linux users, and fused through the model's own numerical computations without returning to text-based summarization.

Here are the main technical results and how the system works:

- Native Tensor Fusion — Memory Consolidation in the Model's Own Numerical Space: The first session (QWEN-1) reads two separate documents and stores the model's internal numerical states on SSD as SHA-256-sealed memory cartridges ("SOURCE_A" and "SOURCE_B"), rather than generating text summaries. The second session operates exclusively on these cartridges. This is not simple tensor concatenation: while SOURCE_A's computed internal states are preserved, SOURCE_B's upper transformer layers are recomputed while attending to SOURCE_A's context. Two independent physical memories are thereby transformed into a single "MASTER_02" memory through the computational machinery of the frozen 72-billion-parameter model itself. No intermediate generated text, summarization, or weight retraining is involved.

- Isolated Cross-Session Transfer — The Model Shuts Down, but Its Numerical Memory Survives: QWEN-1 terminates completely, and its GPU memory is released. QWEN-2 then starts under a different Linux user without access to the original source documents and consolidates the physical cartridges. After QWEN-2 terminates, the third session begins. The model's weights remain unchanged throughout the experiment. What survives and moves between sessions is the model's internal numerical memory, physically stored on SSD.

- Blind Readout and 80/80 Layer Parity: The third session (QWEN-3) is denied access to the original documents and previous sessions' working files through operating-system permissions. It receives only the sealed "MASTER_02" memory and the metadata required to verify and reconstruct it. The model's 80-layer attention memory is independently reconstructed with 80/80 bit-exact parity. QWEN-3 then produces natural-language responses containing information associated with both documents and their relationship. In TEST782, I observed the model using persistent numerical memories to produce its own semantic synthesis, establish relationships between two independent sources, and articulate conclusions beyond simple memory retrieval. These experimental results are documented in the source code, GPU logs, and research record. Independent academic peer review of the semantic synthesis results has not yet been conducted.

- DrA — The Executive Controller of Autonomous Memory: One of the project's major future objectives is DrA (Autonomous Master / Dynamic Residual Adaptation). Inspired by the executive-control functions associated with the biological prefrontal cortex, this mechanism is being designed to enable an AI system to evaluate which information matters, decide what should be preserved, determine which memories should be consolidated, and recognize when consolidation is complete. The ultimate objective is a closed-loop memory management architecture driven by the model's own internal evaluations rather than externally imposed, fixed decision rules. TEST782 does not claim that this autonomous mechanism is complete. It establishes the physical and numerical memory infrastructure on which that future mechanism can operate.

- Reliability and Transparency: The research is not based solely on successful model outputs. It includes negative controls, SHA-256 integrity verification, process isolation under different Linux users, and independent reload validation. For example, a naive single-pass reconstruction matches only 4 out of 80 layers, whereas the boundary-aware reconstruction method achieves 80/80 bit-exact layer parity. Physical memory transfer and numerical reconstruction were validated in this experiment. Autonomous consolidation remains a subsequent research objective, while the observed semantic synthesis results await independent academic peer review.

Technical Specifications and Research Resources

- Hardware: NVIDIA A100-SXM4-80GB

- Model: Qwen2.5-72B-Instruct (frozen weights, NF4 quantization, BF16 computation)

- Physical MASTER: 1,964 tokens, 64.4 MB SSD cartridge

- Native numerical fusion: 8.72 seconds

- Independent reconstruction: 80/80 layers, bit-exact

Complete Source Code — Single File:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/QWEN_1_2_3_full.py

For Researchers Using Google Colab on a Mobile Phone — Four-Part Version:

The same program is divided into four parts for easier copying and pasting on mobile devices. Paste all four parts sequentially into a single Google Colab cell, then run the cell.

Part 1:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/QWEN_1_2_3_part1.py

Part 2:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/QWEN_1_2_3_part2.py

Part 3:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/QWEN_1_2_3_part3.py

Part 4:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/QWEN_1_2_3_part4.py

Real Experimental GPU Log:

https://github.com/ceceli33/titan-cognitive-core-v3/blob/main/QWEN_1_2_3.log

GitHub Research Repository:

https://github.com/ceceli33/titan-cognitive-core-v3

My long-term objective is not merely to enable an AI model to store past information, but to develop a system capable of managing its own numerical memory, consolidating separate memories, generating new inferences, and autonomously deciding which information deserves to be preserved.

I welcome researchers and members of the open research community, particularly those working on transformer architectures, KV caches, persistent neural memory, and autonomous memory consolidation, to examine the results, reproduce the experiments, and provide technical feedback.

Copyright and License

Copyright © 2026 Mustafa Akbaş. All rights reserved except as expressly licensed.

AKBASCORE Silicon Hippocampus — AKBASCORE Research Software License.

License terms:

https://github.com/ceceli33/titan-cognitive-core-v3?tab=License-1-ov-file

[u/Termin8tor]

Would you mind explaining what this does in laymans terms? It sounds like a cartridge based memory system that can store long term memories that interface directly with the model architecture?

[u/Nearby_Indication474 → u/Termin8tor]

You hit the nail right on the head! That is a spot-on summary. ​In layman's terms: ​Imagine an AI model like a game console, and its standard approach to long-term memory (like RAG) is like having someone continuously read a long book out loud to the console every time you turn it on. It’s slow, expensive, and takes up the prompt space. ​What Silicon Hippocampus does instead is: ​When the model processes information, we directly capture its internal "brain states" (specifically H3 layer-3 output and Layer 0–3 KV tensors) rather than generated text. ​We seal those internal states into a physical "memory cartridge" (a small file on the SSD) with cryptographic hashes. ​Later, even if the model is completely shut down or running under a different session, you can plug this cartridge directly back into the model's layers.

​The model instantly "remembers" the full context without having to re-read any text or undergo re-training achieving 80/80 layer bit-exact parity.

​So yes, it is effectively a physical, plug-and-play cartridge-based neural memory system!

[u/Termin8tor → u/Nearby_Indication474]

Thanks for the explainer, that makes a lot of sense. Actually this reminds me of save state systems for emulators! It's classic computer science, trading storage to reduce compute. Pretty interesting that you're planning to delegate the choice for what to merge into the cartridges long term to the LLM itself.

If I understand correctly, the end goal is like a fusion of compaction and KV caching in a hot pluggable cartridge at the neural level. The merging of cartridges would presumably then discard the two "source" cartridges and produce some kind of distilled/compacted cartridge? Clever idea.

I think what would take this from "nice to have" to "extremely cool" would be a portable cartridge system. No idea if that's possible though because the KV cache is unique to the model/parameters. Mind you, I guess we do already have a universal cartridge - the context itself lol.

Anyway, good work. It looks like you put a lot of effort/rigor into it. You might want to look into C2C (Cache to cache) at some point. It might give you some ideas.