Investigating the Fundamental Limit: A Feasibility Study of Hybrid-Neural Archival

Abstract

Large Language Models (LLMs) possess a theoretical capability to model information density far beyond the limits of classical statistical methods (e.g., Lempel-Ziv). However, utilizing this capability for lossless compression involves navigating severe system constraints, including non-deterministic hardware and prohibitive computational costs. In this work, we present an exploratory study into the feasibility of LLM-based archival systems. We introduce \textbf{Hybrid-LLM}, a proof-of-concept architecture designed to investigate the "entropic capacity" of foundation models in a storage context. \textbf{We identify a critical barrier to deployment:} the "GPU Butterfly Effect," where microscopic hardware non-determinism precludes data recovery. We resolve this via a novel logit quantization protocol, enabling the rigorous measurement of neural compression rates on real-world data. Our experiments reveal a distinct divergence between "retrieval-based" density (0.39 BPC on memorized literature) and "predictive" density (0.75 BPC on unseen news). While current inference latency ($\approx 2600\times$ slower than Zstd) limits immediate deployment to ultra-cold storage, our findings demonstrate that LLMs successfully capture semantic redundancy inaccessible to classical algorithms, establishing a baseline for future research into semantic file systems.

Figures (6)

• Figure 1: Visualizing Neural Arithmetic Coding. Arithmetic coding represents a sequence as a precise interval between 0 and 1. (Left) A statistical model, lacking semantic understanding, assigns a low probability (narrow interval) to the target word "Paris," requiring many bits to define the specific slice. (Right) An LLM leverages context to assign a high probability (wide interval) to "Paris." Because wide intervals are easier to target numerically, significantly fewer bits are required to encode the data.
• Figure 2: Content-Aware Hybrid Routing Logic. The system processes the input stream in segments. A lightweight 'Scout' (Zstd-1) filters out incompressible noise ($R \le 1.05$) and highly redundant logs ($R > 3.0$) to the CPU. Only data in the 'Semantic Zone' is routed to the GPU, ensuring that expensive neural inference is reserved for data where it yields information gain.
• Figure 3: Visualizing the GPU Butterfly Effect. (Top) Without quantization, microscopic floating-point drifts between parallel encoding and serial decoding accumulate, causing the arithmetic coder to diverge. (Bottom) Our protocol quantizes logits into a discrete probability space, enforcing bit-exact reproducibility across heterogeneous hardware.
• Figure 4: Inference Latency Scaling. Standard autoregressive attention (Red) suffers from quadratic complexity, making large-file compression intractable. Our Static Window Cache (Blue) ensures constant-time inference per token ($O(1)$), allowing the system to scale linearly with file size.
• Figure 5: Distributed Block-Parallel Architecture with Context Grafting. The source input is partitioned into independent segments. To mitigate context fragmentation, the final $K$ tokens of the preceding block (gold shading) are prepended to the current block to prime the attention mechanism of the LLM. Multiple GPU workers process these augmented blocks simultaneously. Importantly, the system only generates compressed bits for the unique target tokens (blue shading), enabling scalable $O(N/P)$ throughput without sacrificing semantic history.