AI4AI-for-Anything / ResearchBoost-Agent

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 AI that accelerates the research that advances AI. 
ResearchBoost-Agent turns AI capabilities into faster AI experimentation, starting with the training code researchers run every day. It autonomously profiles bottlenecks, implements optimizations, and validates speedups against a frozen evaluation protocol, turning the same compute budget into more room to explore.
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## Quick start
Give the agent access to a model endpoint:
 [code] 
Then point your agent at docs/run-a-task.md.
## How a run works
1. **Frame the task.** The owner fixes what may not change: the upstream workload and per-GPU load, the reference precision, the training semantics. A formulating agent builds the judge for this task (`bench/` plus a frozen copy of `numerics/`), records the reference, freezes δ, proves with known-bad candidates that every gate fires, and writes `GOAL.md`.
2. **Run goal mode.** One Codex `/goal` session gets the workspace, the GPUs and the skill cards inside a bubblewrap sandbox. Every round it profiles the current best version, changes one hotspot behind a switch, measures with `bench.py time`, and logs the round in `WORK_LOG.md`. A supervisor watches from outside and intervenes only on rules and facts, never on optimization direction. See docs/goal-mode.md.
3. **Accept only what the judge accepts.** `bench.py check` is the only verdict. A full-check PASS is then confirmed with a real end-to-end training or evaluation run.
## Results
Every number below was printed by that task's frozen judge, or comes from a full training or evaluation run of the accepted candidate.
| Task | Upstream | Hardware during optimization | Judge verdict | Speedup | Code |
|---|---|---|---|---|---|
| RAE Stage-1 training (decoder + GAN) | bytetriper/RAE @a4d18c4 | 2 × H200 | PASS, statistically equivalent | **5.32×** step time; full 16-epoch run on 8 × H20: rFID 0.541 vs 0.569 for the public checkpoint | Faster-RAE |
| Self-Forcing video DMD training | guandeh17/Self-Forcing @33593df | 2 × H200 | PASS, statistically equivalent | **2.08×** | Faster-SelfForcing |
| LLaVA-1.5-7B stage-2 fine-tuning | haotian-liu/LLaVA @c121f04 | 4 × H20 | PASS, statistically equivalent | **2.39×**; real 1-epoch run: four benchmarks within −0.50 to +0.44 of the official weights | Faster-LLaVA |
| DMD2 one-step SD1.5 distillation + GAN | tianweiy/DMD2 @8d8fa55 | 4 × H20 | PASS, statistically equivalent | **1.72×**; FID-30k 8.70 vs 8.65 on 8 × H20 | Faster-DMD |
## The judge
A candidate counts only if the task's frozen judge accepts it. The judge runs these gates in order and stops at the first one that fails:
0. **Structure**: the training entry points the judge drives are unchanged (AST compare), data loading is bitwise identical, and exact items match: phase, call counts, RNG fingerprints, learning rate, cross-rank sync.
1. **Be