Applying LZ77-style sequence compression and LZW…
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Applying LZ77-style sequence compression and LZW substitution to LLM context reduction
Hey everyone,
I’ve been experimenting with token optimization for LLM agent frameworks by treating terminal and tool outputs as a data compression problem rather than a text-filtering one.
The pipeline uses a bidirectional 42-stage architecture:
Algorithmic Reduction: Raw text passes through an LTSC (LZ77-style lossless sequence compression) layer combined with LZW token substitution to eliminate repetitive terminal patterns dynamically.
Structural Compaction: Code segments are reduced to AST skeletons, and nested JSON payloads are flattened into tabular structures (TOON) to minimize semantic token weights.
0-Risk Fallback: A local comparison check runs at every stage. If a compression layer increases string length or corrupts format, it instantly rolls back.
Response Filtering: A 7-stage outbound filter targets conversational boilerplate and normalizes whitespace.
In production testing, this algorithmic pipeline hits a 74% overall token compression rate (up to 93% on highly repetitive logs) without degrading the model's underlying reasoning capabilities.
The full implementation is open-source (MIT):
https://github.com/MrGray17/opentokenhttps://github.com/MrGray17/opentoken
I'd love to discuss the theoretical limits of combining algorithmic text sequence compression with LLM tokenizers, or how to better handle progressive disclosure as context fills up.!
#technology
Hey everyone,
I’ve been experimenting with token optimization for LLM agent frameworks by treating terminal and tool outputs as a data compression problem rather than a text-filtering one.
The pipeline uses a bidirectional 42-stage architecture:
Algorithmic Reduction: Raw text passes through an LTSC (LZ77-style lossless sequence compression) layer combined with LZW token substitution to eliminate repetitive terminal patterns dynamically.
Structural Compaction: Code segments are reduced to AST skeletons, and nested JSON payloads are flattened into tabular structures (TOON) to minimize semantic token weights.
0-Risk Fallback: A local comparison check runs at every stage. If a compression layer increases string length or corrupts format, it instantly rolls back.
Response Filtering: A 7-stage outbound filter targets conversational boilerplate and normalizes whitespace.
In production testing, this algorithmic pipeline hits a 74% overall token compression rate (up to 93% on highly repetitive logs) without degrading the model's underlying reasoning capabilities.
The full implementation is open-source (MIT):
https://github.com/MrGray17/opentokenhttps://github.com/MrGray17/opentoken
I'd love to discuss the theoretical limits of combining algorithmic text sequence compression with LLM tokenizers, or how to better handle progressive disclosure as context fills up.!
#technology
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