TextSink — Four Voices. One Truth.

TextSink (TS) is a containerized Track 2 captioning agent built end-to-end on Gemma 4 (26B MoE) via a dedicated Fireworks deployment. one model does the visual grounding AND all four caption styles. THE SCORED PIPELINE. ffmpeg samples frames across the whole clip; Gemma 4 extracts scene facts as strict JSON. If grounding comes back empty, TS retries and refuses to caption blind; captions may only use extracted facts, so accuracy holds on any clip. Four tone contracts with distinct comedic mechanisms (broadcast precision, deadpan irony, dev-culture metaphor, warm relatability) yield four captions an LLM judge can blind-sort. The container implements the standard /input/tasks.json -> /output/results.json harness contract, runs clips concurrently (3 official clips in ~30s warm), and if the Gemma deployment is unreachable it cuts over per-task to serverless models — no task ever scores 0. BEYOND THE CONTRACT. The same grounding powers TS CC; real timed closed captions (.srt/.ass + burned video), each style one consistent voice with running gags. AND The Hecklers: two models (Gemma 4 vs gpt-oss-120b) argue about the video turn by turn, every line generated live. STAN & GUS (sarcastic old men), LINT vs VIBE (coder vs vibe-coder), DORIS & PEARL (gossiping neighbors), matching the 3 styles. GEMMA TEACHES GEMMA; AND THE STUDENT WINS. Gemma 4 drafted candidates at high temp, judged its own drafts on accuracy + tone, and the winners became a self-distilled SFT set. Fine-tuned ts-g3-captioner (Gemma-3-27B LoRA) beats its teacher's prompted best-of-3 18-4 head-to-head under a neutral judge, higher on every style (ab_results.json). EVIDENCE, NOT CLAIMS. GEMMA_PROVENANCE.md (where Gemma authored the entry), eval/ABLATIONS.md (designed mechanisms beat generic prompts 11-5 under 2 judges, on the fallback model), submission/fallback_verification (forced foreign-key run — zero empty captions). All demos generated on the official clips. MIT. Python 3.11 + ffmpeg. No framework lock-in.