Pre-inference context collapse layer cuts token usage by 10x with no quality loss
echozero3 · reddit · 2026-08-29
The author proposes a "pre-inference context collapse" layer as an alternative to standard RAG (retrieve-and-stuff). Instead of chunk retrieval + reranking, it builds a vector-field representation of the entire corpus, evaluates relational relevance to the query, and collapses the candidate field down to a compact evidence state before sending it to the model.
Key Results:
- Order-of-magnitude drop in tokens: Internal benchmarks show a 10x reduction in tokens sent to the model with zero measured quality regression (scoring matched historical runs).
- Performance: Runs efficiently single-threaded; benchmarked 140ms for 10M samples on a 2015 i7.
- Local-first: The full corpus stays on the user's side; only selected evidence chunks + field-topology coordinates are sent to external LLM APIs.
The author is asking for community feedback: Is "context collapse before inference" distinct enough from existing rerankers or chunking strategies, or is it just a fancier reranker with extra steps? Next steps include rigorous benchmarking against BM25 or plain cosine-similarity RAG.
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