🧪 SplatLab

🚀 The research cycle (read, reproduce, extend, train, ablate, write) compressed from months of graduate-student time to days of mostly autonomous compute.

• 2026-09 — SplatLab is accepted at NeurIPS 2026! 🎉
• 2026-10 — Project page, paper PDF, interactive demo, and all 16 SplatLab-written papers are online.
• 2026-06 — 3 SplatLab-written papers accepted by human reviewers at a CVPR 2026 workshop.

SplatLab is an autonomous research system for 3D Gaussian Splatting. You give it a research prompt, a target-paper URL, or no input at all. It reproduces a published baseline, proposes an extension, trains and ablates it across Mip-NeRF 360 and LLFF on real GPUs, writes the manuscript, and ties every reported number to a logged training run.

It runs natively on Claude Code. One /research slash command turns the main session into a lab PI that runs for 24–48 h, dispatching 32 specialised subagents and coordinating them through a crash-safe ledger.json. An optional zero-dependency web dashboard can launch, watch, stop, and resume runs from the browser.

Five design choices set it apart:

A master orchestrator (the professor) delegates to phase coordinators (PhD students) and atomic leaf agents (undergraduates):

Prompt / paper URL / GitHub repo / nothing
  │
  ├─ [1] Grounding ────────── regime classifier · literature corpus + arXiv gap-fill
  │                           repo-reproducer clones + reproduces the target (±0.2 dB)
  │                           evolve-block-mapper places the 9 EVOLVE-BLOCK hooks
  │
  ├─ [2] Grounded ideation ── bottleneck-analyst (empirical B-IDs) + limitation-brainstormer
  │                           (conceptual L-IDs) → ideator + web-idea-scout
  │                           idea-level Elo pre-selection, novelty check, tweak-class ideas banned
  │
  ├─ [3] Implementation ───── implementer: parse → plan → code → review → validate → integrate → test
  │                           debugger / freeform-mutator on failure
  │
  ├─ [4] Tournament ───────── smoke (500 it) → single-scene probe → multi-scene → full 30k
  │                           cascade-weighted Elo · falsifier · 8-island evolutionary search
  │
  ├─ [5] Integrity audit ──── ablation-runner · failure-case-analyst · provenance / novelty / claim checks
  │
  └─ [6] Manuscript ───────── figure-generator · summary/body/related-work writers · latex-assembler
                              3 parallel reviewers → revision-agent until converged → paper.pdf

Each SOTA row is the target reproduced on our GPUs under the reproduce-first contract, not copied from its paper.

The full table (11 reproduced targets, 11 wins) is in the paper. 3 SplatLab-written papers submitted to a CVPR 2026 workshop were accepted by human reviewers. In a blind expert study, SplatLab ideas scored higher on novelty than PhD-student ideas (5.71 vs 4.92, p = 0.041).

Each first page below comes from a real compiled PDF produced by one autonomous run: the method was invented, trained, ablated, illustrated, written, and revised with no human editing. Click a page to read the paper.

Nebula3DGS +3.78 dB	AttDis & Friends ✅ CVPR’26 WS	NeedleSwap ✅ CVPR’26 WS	AuditSplat ✅ CVPR’26 WS
BubbleGS +2.71 dB	View-Curriculum-GS +2.83 dB	TileFocalGS +0.57 dB	FreezeGS +0.43 dB

All 16 papers, plus the OpenReview decisions, are on the project page.

• Claude Code (claude on PATH) with a Claude Max plan or an API key
• tmux, python3 + pyyaml
• For real training runs: CUDA GPU(s), a nerfstudio conda env (ns-train / ns-eval), mineru (PDF → Markdown), TeXLive (pdflatex, bibtex)
• Optional: node ≥ 18 for the web dashboard; PaperBanana for figure generation (export PAPERBANANA_DIR=/path/to/PaperBanana)

git clone https://github.com/seemandhar/SplatLab.git
cd SplatLab

conda create -n nerfstudio python=3.10 -y && conda activate nerfstudio
pip install nerfstudio pyyaml
pip install -e splatify/gs_template        # local nerfstudio-compatible 3DGS template (Track B)

# datasets go under data/ (paths are set in config.yaml → data_roots)
mkdir -p data && ln -s /path/to/mipnerf360 data/mipnerf360

The classic way to run SplatLab, and the one used for every run in the paper. Start Claude Code in the repo root and the main session becomes the lab PI:

claude
> /research "frequency-aware adaptive densification for 3DGS" --hours 24
> /research-status      # live progress: thesis, idea pipeline, best result, budget
> /research-stop        # graceful halt; in-flight experiments finish
> /research-resume      # pick up after a stop or crash, from the ledger checkpoint

Fully detached in tmux, so it survives ssh disconnects:

bash scripts/launch.sh "my idea" 24        # starts claude + /research in a tmux session
tmux attach -t splatlab         # watch the master console

Ways to start a run:

/research "your idea here" --hours 24                                   # from a research prompt
/research "improve it" --paper-url https://arxiv.org/abs/2505.05587     # target a published paper
/research "improve it" --git-url https://github.com/<org>/<3dgs-repo>   # clone + reproduce a repo first
/research --hours 24                                                    # no idea given: falls back to config.yaml

Other flags: --scenes a,b,c, --max-budget-usd N, --model-preset optimized|all_opus|all_sonnet.

A zero-dependency Node dashboard on top of the same commands. It is handy for watching a run from a browser or a laptop:

bash start-ui.sh                            # → http://localhost:8420

＋ New Run → enter an idea and a time budget → Launch. Tabs: Overview (status, thesis, ideas, best result, milestones, budget) · Experiments (smoke → probe → multi-scene cascade) · Live (event feed + read-only master console) · Paper (compiled PDF, figures, reports) · Runs (inspect or resume any past run). Stop / Resume / Kill sit in the top bar.

HOST=0.0.0.0 GSS_TOKEN=$(openssl rand -hex 16) bash start-ui.sh
# open http://<your-box>:8420 — the page asks for the token once

Edit config.yaml (the dashboard's launch form writes to it too):

hours: 24                      # wall-clock cap (authoritative)
max_budget_usd: null           # null on Claude Max; a number for API billing
model_preset: all_opus         # optimized | all_opus | all_sonnet
dataset: mipnerf360
scenes: [garden, bicycle, stump, room, counter, kitchen, bonsai]
gpu_indices: [0, 1, 2, 3]
allow_repo_reproduction: true  # false → restrict to the local splatify/gs_template (Track B)

SplatLab/
├── README.md
├── CLAUDE.md                # architecture, team roster, execution tracks
├── config.yaml              # run configuration
├── run.sh · resume.sh · start-ui.sh
├── .claude/
│   ├── commands/            # /research, /research-status, /research-stop, /research-resume
│   ├── agents/              # 32 subagent definitions + shared protocols
│   └── skills/              # academic-voice cleanup (humanizer)
├── splatify/                # evolutionary search: Elo, cascade, falsifier, EVOLVE-BLOCKs
│   └── gs_template/         # nerfstudio-compatible 3DGS template + method plugins
├── scripts/                 # launch, ledger helpers, GPU reservation, watchdog, metric extraction
├── ui/                      # optional zero-dependency Node dashboard (server.js + public/)
├── latex/ · refs/           # paper template + BibTeX corpus
├── assets/                  # figures used in this README
└── discoveries.txt …        # reference corpora read by the ideator and writers

workspace/run_<timestamp>/
├── ledger.json          # durable state: ideas, experiments, Elo, claims → measurements
├── events.jsonl         # append-only event log
├── sota/                # cloned + reproduced target repo, one worktree per idea
├── figures/             # teaser, pipeline, qualitative renders, tables
├── paper/main.pdf       # the compiled manuscript
└── RESEARCH_LOG.md      # written when no result clears the bar

A tool that compresses months into days can flood a peer-review system built for human pace. We recommend labelling derivative submissions as machine-generated and shipping their experiment ledger, and we discourage volume submissions. SplatLab is a research instrument, not a paper-mill backend.

Runs consume real GPU time (and API spend when not on Claude Max). The master may clone repos and run pip/conda to reproduce baselines; set allow_repo_reproduction: false to keep it on the local gs_template. The dashboard binds 127.0.0.1 by default. Never bind 0.0.0.0 without setting GSS_TOKEN.

• NERFIFY (CVPR 2026 Highlight): a multi-agent framework that turns NeRF papers into trainable Nerfstudio code.
• SPLATIFY (arXiv 2610.09116): reproduce, discover, innovate. A multi-agent framework that turns Gaussian Splatting papers and one-line ideas into trainable gsplat / Nerfstudio code. SplatLab builds on its splatify/ evolutionary search and gs_template.

If you find this work useful, please cite:

@misc{jain2026splatlabautonomousscientificdiscovery,
      title={SplatLab: Autonomous Scientific Discovery for 3DGS Research}, 
      author={Seemandhar Jain and Manmohan Chandraker},
      year={2026},
      eprint={2610.30468},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.30468}, 
}