Human-Readable Parallel Compression: Non-Expansion as Output Strategy:人間可読並列圧縮:非展開という出力戦

Existing prompt compression methods (LLMLingua, 500xCompressor) achieve high compression ratios but produce outputs unreadable by humans. This paper proposes Human-Readable Parallel Compression (HRPC), which achieves 4x compression while maintaining readability for both humans and AI. Through analysis, we discover that HRPC is not a "compression technique" but rather "non-expansion"—the direct output of parallel thought structures without serialization. This finding reframes the problem: apparent compression emerges naturally when parallel thinkers write without expanding their thoughts into linear structures. We demonstrate through translation experiments that: (1) HRPC texts collapse when processed by machine translation (Statistical machine translation), (2) AI systems (LLM-based AI) successfully translate HRPC by understanding parallel structure, (3) the same author's serialized (1000-character) version translates successfully via machine translation. These results prove that "unreadability" judgments reflect translation engine limitations, not source text deficiency. We formalize these findings as the Translation Symmetry Theorem (demands for cross-linguistic accommodation are bidirectionally equivalent) and the Parallel Processing Prerequisite Theorem (criticism of parallel-structure texts requires parallel processing capability).Co-written by Viorazu. and Claude ( Sonnet 4.5, Anthropic)共著:Viorazu. & Claude( Sonnet 4.5、Anthropic)