Editing Openai/691c1dba-9228-800f-8463-13b3a9006306 (section)

==== There are three mechanisms that make huge semantic compression possible in practice: ====

A. Structural canonicalization (very high leverage)
* LaTeX content is highly structured: repeated macros, proofs with formal skeletons, equation templates, repeated phrases.
* If HPLm canonicalizes structure (remove redundant markup, normalize variable names, strip whitespace, compress repeated blocks) you get large gains losslessly.

B. Semantic summarization (lossy but effective)
* Replace repeated long derivations with concise summaries + pointers (ingot references).
* You keep key claims, definitions, equations’ canonical forms rather than full derivations.

C. Model-assisted reconstruction (apparent losslessness)
* The compressed artifact isn’t used as raw transcript-only. The system pairs an ingot with a reconstruction routine (an LLM + canonical templates) that expands the ingot back into a readable, near-equivalent document.
* That makes compression look lossless because the decoder (LLM + HPL rules) can re-generate derived content from the canonical seeds.

Put differently: HPLm is likely a hybrid of deterministic canonical compression + semantic seeds + procedural reconstruction. That’s how you can credibly shrink 300k tokens into 12k and still “get the meaning back.”