Bypassing RAG: Injecting 30,000-Token Tensor Cartridges at Layer 3 of Frozen Qwen2.5-72B on One A100
Nearby_Indication474 · reddit · 2026-10-17
- Core idea: The AKBASCORE "Silicon Hippocampus v1.4" project skips RAG retrieval and KV-cache replay entirely — it captures the frozen model's own intermediate hidden representations (H3), stores them as standalone "tensor memory cartridges," and reinjects them directly at the Layer-3 boundary (CUT3) of the transformer's internal computation.
- Setup: Qwen2.5-72B-Instruct (NF4 quantized, BF16 compute, 80 layers) on a single A100-80GB; three cartridges of 10,000 tokens each (Pride and Prejudice, Frankenstein, Sherlock Holmes) form a 30,031-position working memory, each exactly 328,698,863 bytes on disk.
- Mechanism: During construction the frozen model processes text in 256-token chunks, persisting H3 plus Layer 0–3 K/V tensors; the source text is no longer needed afterward. On mounting, H3 is injected at CUT3 and layers 4–79 reconstruct the remaining internal states — no fine-tuning, no weight changes.
- Claimed significance: The distinction vs. conventional KV caching is restoring the model's own intermediate computational state rather than making it re-read retrieved text. Research record published on Zenodo.
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