AI generated

Full disclosure before anything else: I had my local AI agent draft this write-up and it ran the test battery itself. The hardware, the runs and every number in here are ours, but the typing is half machine. Ran this on consumer hardware: 5070 Ti 16GB + 64GB RAM, Qwen3.8 Flash Next IQ3_S behind a local OpenAI-compatible server (Strata stack), OpenCode 1.19.5 + plugin 0.1.4 in auto, native arm on identical prompts with --pure. Four tasks from the workspaces bundled in the repo. Results: 100KB book QA (5 questions) 66.6k tok / 248s → 15.0k / 50s. Three-question variant −27% / −14%. 33KB audit bundle: dead even, the gate stayed native with one preview call, no cost for being there. Quality: same score both arms on all four tasks. So the protocol holds up on a heavy quant. I expected IQ3 to fumble the intent/slot bookkeeping and eat the savings in failed round trips; it didn't, three calls, 5/5 slots resolved at 0.99 confidence, answers match the native arm's finds, anchors and all. One thing did bite, CMap-font PDFs with no usable text layer. Preview came back as garbage, then the sparse arm took nine raw_ref fallbacks and started decoding ToUnicode CMaps by hand in bash. Right answers, 2x tokens and wall time; the native arm sidestepped it with pdftotext. Worth noting the gate still said force_sro on that file, it didn't see the missing text layer coming. Pre-extract text for PDFs like this, or keep pdf pinned to native. Minor: decide also flags a 6.6KB file as long_document in my workspace, maybe worth a size-band look for txt. Thanks for shipping the benchmark workspaces in the repo, A/B took one evening instead of a week. The direction matches your numbers, and on local hardware the doc-QA deltas matter even more than on API models since every replayed observation costs prefill on my card. Caveat: n=1 per arm, keyword grading, one model.