Lean's DEFLATE implementation (lean-zip) outperforms pure Rust's miniz_oxide in both compression speed and ratio at most levels, e.g., 30% faster at level 6.
The performance advantage is enabled by a formal proof of correctness (inflate_deflateRaw theorem), which allows AI agents to optimize the code autonomously without human review, ensuring no bugs are introduced.
Pareto frontier graphs show lean-zip dominates miniz_oxide across all compression levels, and is competitive with optimized libraries like zlib-ng and zlib-rs at high levels.
Caveats include higher memory consumption, slower decompression (1.45x slower), reliance on Lean's extern annotations for low-level functions, and that the proof only covers round-trip correctness, not side channels or performance.
The experiment demonstrates that with formal verification, AI-driven optimization can make Lean competitive with fast languages for basic algorithms, but it is not a general claim that Lean is faster than Rust.
The user proposes a tag-based EDIT tool for local LLM agents, using line-number and checksum tags instead of verbatim text to save tokens and increase reliability.
Current CAS (Check and Set) edit methods require the old text verbatim, causing token waste and failure with hallucinations or formatting issues.
The new approach returns lines prefixed with a 4-character tag (checksum) and allows edits by line:tag pairs, reducing token usage especially for deletions.
The tool is effective with models like DeepSeek v4 Flash, and the line:tag format helps LLMs exploit line information for ranges and follow-up calls.
An alternative is using a file-level CRC32 tag with only line numbers, which saves tokens but fails on any unrelated changes, making tradeoffs apparent.
The author suggests practical testing with ds4-agent across sessions to decide which method is better, with a command-line switch as an initial step.