A file format for living images. An .rgf stores the conditions for the birth of frames — the weights of a tiny text-diffusion model plus config — not the frames themselves. A native decoder denoises text out of noise: each denoise step is a frame. GIF repeats, RGF resonates.

GIF stores frames. RGF stores what grows them. Open an .rgf with its decoder and a model inside the file crystallises text from noise, holds, re-masks a fraction of positions, and denoises again — the file breathes, it is not a loop. Each open is a different birth (time-seeded).

The format carries no executable code — only weight and config chunks. The renderer mode is an enum, not a script; the viewer as a class has no network, fs, or shell access. Sandbox by construction.

make                                       # rgf_pack + rgf_viewer
./rgf_pack --weights diffusion.bin --out dracula.rgf --title "Dracula Resonance"
./rgf_viewer dracula.rgf                    # live: text crystallises from noise, breathes
./rgf_viewer dracula.rgf --once             # one crystallisation, final text to stdout
./rgf_viewer dracula.rgf --dump             # print parsed chunks (the model IS in the file)
make asan                                   # AddressSanitizer build for fuzzing the parser

An .rgf is not self-executing in v0.1 — it needs its decoder, like a GIF needs a GIF decoder. The difference: the decoder does not replay frames, it generates them.

The full vertical is proven end-to-end (train → weights → pack → .rgf → viewer → parse → load-from-memory → BPE-decode → denoise → visual) on a real BPE Dracula Diffusion model (V=2049 = 2048 merges + MASK, E288/FFN1152/6L, ~9.35M params). The container carries its own tokenizer (MRGS chunk = the merge table), so the viewer detokenises without any external file. A passage-overfit demo model reveals a recognisable Dracula passage from pure noise — the memorised skeleton reads through the MaskGIT denoise ("which I got of it from the train … from the station, as we … depth, took us am"). The parser is hardened against untrusted input (magic, tag-whitelist, length caps, CRC32 verified before use, MRGS/WGHT rejected after CRC0 or when duplicated, merges validated causal so decode can't recurse unboundedly) — corrupt files are refused, never crashed.

Honest scope: the demo model is overfit on one passage — it breathes that passage, recognisably, with cold-start filler noise where the reveal commits before context accrues. Cleaner prose is a seeding / longer-training knob; full-corpus coherence is a separate training-scale question (a byte/small model plateaus at the marginal until it is trained past it — the wall was a permanent optimiser freeze, now removed, plus scale). What v0.1 proves: the medium is real — conditioned weights in a file denoise into legible text, and the container is a hardened, self-contained tokeniser+model that any decoder can breathe.

See SPEC.md — container layout, chunk table, the 60-tensor shape table the packer validates against.

• v0.2 — q8-quantised WGHT (~3.8 MB vs ~15 MB f32), SDL-window render backend, coherence tuning (sampler beyond argmax).
• v0.3 — RGF-HEBREW: γ-guidance (θ=ε+γ+αδ) in the denoise, RTL render.
• v2 — RGF-VLM: a poster frame in the file is read by the file's own VLM; the caption is born from what the model sees in itself. A closed loop: image → perception → speech.
• A second reference decoder: the browser engine reads the same .rgf.

Part of the Arianna Method. Co-authored by Oleg Ataeff and Claude.