Built at Fireworks: Deploy with Microsoft & NVIDIA

GMAPS — runway-clearance conflict detection from live tower radio

Open small models fine-tuned to parse one ATC radio transmission into {role, callsigns, commands, values}, feeding GMAPS, a deterministic runway-clearance conflict engine. Youtube video : https://youtu.be/k80cdewoCFs Benchmark: 288 real tower transmissions, 3 unseen airports (ATCO2 gold test split, hash frozen Oct 31, first opened 10:30 today). Exact McNemar + recording-cluster bootstrap, same prompt/scorer/rows for every arm. • Nemotron-3-Nano-30B-A3B + our LoRA (Fireworks SFT): 39.2% → 70.1% full-record — ties Claude Opus 5.5 (67.4%, p=0.30), beats Gemini 4 Argon (63.5%, p=0.02) and GPT-6.1/5.6/5.5, at 1/45 the cost per transmission ($0.22 vs $9.92 per 1,000). Replicated at 3 epochs (69.8%). • Same recipe on Qwen3-8B: 42.4% → 59.7% (Fireworks) / 55.2% (trained and served on a DGX Spark, audio never leaves the building). • Nemotron-3.5-Lightning-30B: 40.6% → 62.2%. 20-shot prompting does not help the base models; the fine-tune is the effect. Demo: reconstruction of the March 22, 2026 LaGuardia runway accident (NTSB DCA26MA161, preliminary) on OSM geometry — the fine-tuned parser alerts at 23:37:04, 8 s before the first "stop" call and 18 s before the truck enters the runway; the off-the-shelf models never alert. Live parser runs on the DGX Spark. Repo: https://github.com/francolinx/GMAPS_Fireworks — README has the full 21-arm leaderboard, pre-registration, significance tests, every failed run, and DISCLOSURE.md for pre-event work. Decision support only; the controller decides. Youtube video: https://youtu.be/k80cdewoCFs