A universal research automation system. Laptop to supercomputer. Local models to cloud APIs. Novice to expert. Computation to physical world.
Research automation should not require a supercomputer, a cloud budget, or an engineering team.
ARI is designed around one principle: describe the goal in plain Markdown — ARI handles the rest.
- A student with a laptop and a local LLM can run their first autonomous experiment in 10 minutes.
- A researcher with HPC cluster access can run 50-node parallel hypothesis searches overnight.
- A team can extend ARI to control lab hardware, robotics, or IoT sensors by adding a single MCP skill — without touching the core.
The system scales across five axes:
| Axis | Minimal | Full |
|---|---|---|
| Compute | Laptop (local process) | Supercomputer (SLURM cluster) |
| LLM | Local Ollama (qwen3:8b) | Commercial API (GPT-5, Claude) |
| Experiment spec | 3-line .md |
Detailed SLURM scripts + rules |
| Domain | Computational benchmarks | Physical world (robotics, sensors, lab) |
| Expertise | Novice (goal only) | Expert (full parameter control) |
🎬 Dashboard demo video — full walkthrough of the ARI web dashboard. Also available in 日本語 · 中文.
📄 Sample output paper (PDF) — a real paper autonomously generated by ARI, including figures, citations, and the reproducibility verification report. See Demonstrated Results for the headline numbers.
📖 Click to read the sample paper inline (scroll through all 11 pages)
experiment.md ──► ARI Core ──► results + paper + reproducibility report
│
┌────────────┼──────────────────────────────┐
│ │ │
BFTS Engine ReAct Loop Post-BFTS Pipeline
(best-first (per-node agent) (workflow.yaml driven)
tree search) │
MCP Skill Servers
(plugin system — add any capability here)
- You describe the goal. Write an experiment file. ARI reads it, generates hypotheses, runs experiments, and reports results.
- BFTS over hypothesis space. Best-First Tree Search guides exploration — evidence-driven, not exhaustive.
- Deterministic tools, reasoning LLM. MCP skills are pure functions. The LLM reasons; skills act.
- From paper to proof. ARI writes the paper and verifies its own claims via reproducibility check.
ARI's MCP plugin architecture is intentionally designed to grow beyond computation:
Today (computational):
ari-skill-hpc → SLURM job submission
ari-skill-evaluator → metric extraction from stdout
ari-skill-paper → LaTeX paper writing
ari-skill-vlm → VLM figure/table quality review
ari-skill-web → pluggable retrieval (Semantic Scholar + AlphaXiv)
Tomorrow (physical world):
ari-skill-robot → robot arm control via ROS2 MCP bridge
ari-skill-sensor → temperature/pressure sensor readout
ari-skill-labware → pipette control, plate reader integration
ari-skill-camera → computer vision experiment observation
Adding any of these requires no changes to ari-core. Write a server.py with @mcp.tool() functions, register it in workflow.yaml — done.
# 1. Install
git clone https://github.com/kotama7/ARI && cd ARI
bash setup.sh
# 2. Set up AI model (choose one)
ollama pull qwen3:8b # free, local
export ARI_BACKEND=openai OPENAI_API_KEY=sk-… # or cloud API
# 3. Launch the dashboard
ari viz ./checkpoints/ --port 8765
# Open http://localhost:8765 → use the Experiment Wizard to create and launch experimentsOr run directly from the CLI:
ari run experiment.md # run experiment
ari run experiment.md --profile hpc # with SLURM clusterSee docs/quickstart.md for the full dashboard walkthrough and docs/cli_reference.md for CLI commands.
Novice (3 lines):
# Matrix Multiply Optimization
## Research Goal
Maximize GFLOPS of DGEMM on this machine.Expert (full control):
# Protein Folding Force Field Sweep
## Research Goal
Minimize energy score across AMBER force field variants.
## SLURM Script Template
```bash
#!/bin/bash
#SBATCH --nodes=4 --ntasks-per-node=32 --time=02:00:00
module load gromacs/2024
gmx mdrun -v -deffnm simulation -ntmpi 32- HARD LIMIT: never exceed 128 MPI tasks
- Always use work_dir=/abs/path in slurm_submit
A 10-page React/TypeScript SPA for visual experiment management. Launch with:
ari viz ./checkpoints/ --port 8765 # http://localhost:8765| Page | Features |
|---|---|
| Home | Quick actions, recent experiments, system status |
| New Experiment | 4-step wizard: Chat/Write/Upload goal → Scope (depth, nodes, workers, recursion) → Resources (LLM, HPC, container) → Launch |
| Experiments | List/delete/resume all checkpoint projects with status and review scores |
| Monitor | Real-time phase stepper (Idle → Idea → BFTS → Paper → Review), live log streaming (SSE), cost tracking |
| Tree | Interactive BFTS node tree, click any node to inspect metrics, tool-call trace, generated code, and output |
| Results | Overleaf-like LaTeX editor (edit/compile/preview), paper PDF viewer, review report, reproducibility results, EAR browser |
| Ideas | VirSci-generated hypotheses with novelty/feasibility scores and gap analysis |
| Workflow | React Flow visual DAG editor for pipeline stages (drag, connect, enable/disable, skill assignment) |
| Settings | LLM provider/model, API keys, SLURM, container runtime, VLM review model, retrieval backend, Ollama host |
| Sub-Experiments | Recursive sub-experiment tree with parent-child tracking (via orchestrator skill) |
Real-time updates via WebSocket (tree changes) and SSE (log streaming). All data is per-project isolated.
The dashboard exposes a REST + WebSocket API that can also be used programmatically:
| Endpoint | Method | Purpose |
|---|---|---|
/state |
GET | Full experiment state (phase, nodes, config, cost) |
/api/launch |
POST | Launch new experiment with full config |
/api/run-stage |
POST | Run specific stage (resume / paper / review) |
/api/checkpoints |
GET | List all checkpoint projects |
/api/settings |
GET/POST | Read/write LLM, SLURM, container, and API key settings |
/api/workflow |
GET/POST | Read/write workflow.yaml pipeline |
/api/workflow/flow |
GET/POST | React Flow graph representation of workflow |
/api/chat-goal |
POST | Multi-turn LLM chat for experiment goal refinement |
/api/upload |
POST | Upload experiment.md or data files |
/api/upload/delete |
POST | Delete an uploaded file |
/api/stop |
POST | Gracefully stop running experiment |
/api/logs |
GET (SSE) | Stream real-time logs and cost data |
/api/checkpoint/{id}/files |
GET | List files in paper directory |
/api/checkpoint/{id}/file |
GET/POST | Read/save paper files |
/api/checkpoint/compile |
POST | Trigger LaTeX compilation |
/api/checkpoint/{id}/filetree |
GET | Full checkpoint directory tree |
/api/ear/{run_id} |
GET | Experiment Artifact Repository contents |
/api/sub-experiments |
GET/POST | List/launch recursive sub-experiments |
/memory/<node_id> |
GET | Retrieve node memory (tool-call trace) |
ws://host:{port+1}/ws |
WebSocket | Subscribe to real-time tree updates |
All dashboard features are also available via the command line:
| Command | Description |
|---|---|
ari run <experiment.md> |
Run a new experiment (BFTS + paper pipeline) |
ari resume <checkpoint_dir> |
Resume from checkpoint |
ari paper <checkpoint_dir> |
Run paper pipeline only (skip BFTS) |
ari status <checkpoint_dir> |
Show node tree and summary |
ari projects |
List all experiment runs |
ari show <checkpoint> |
Detailed results (tree + review report) |
ari delete <checkpoint> |
Delete a checkpoint |
ari settings |
View/modify config (model, partition, etc.) |
ari skills-list |
List all available MCP tools |
ari viz <checkpoint_dir> |
Launch web dashboard |
After a run completes, outputs are saved in ./checkpoints/<run_id>/:
| File | Description |
|---|---|
tree.json |
Full BFTS node tree (all nodes, metrics, parent-child links) |
results.json |
Per-node artifacts, metrics, and status |
idea.json |
VirSci-generated hypotheses and gap analysis |
science_data.json |
Science-facing data (no internal BFTS terms) |
full_paper.tex / .pdf |
Generated LaTeX paper and compiled PDF |
review_report.json |
Automated peer review score and feedback |
reproducibility_report.json |
Independent reproducibility verification |
figures_manifest.json |
Generated figure paths and captions |
rebuttal.json |
Automated rebuttal to review comments |
ear/ |
Experiment Artifact Repository (code, data, logs, reproducibility metadata) |
cost_trace.jsonl |
Per-call LLM cost tracking |
experiments/<slug>/<node_id>/ |
Per-node work directories and generated code |
15 skills total. 14 are registered by default in workflow.yaml; 1 additional skill (orchestrator) can be enabled by adding it to the config.
| Skill | Role | LLM? | Default |
|---|---|---|---|
ari-skill-hpc |
SLURM submit / poll / Singularity / bash | ✗ | ✓ |
ari-skill-evaluator |
Metric extraction from experiment file | △ | ✓ |
ari-skill-idea |
arXiv survey + VirSci hypothesis generation | ✓ | ✓ |
ari-skill-web |
DuckDuckGo, arXiv, Semantic Scholar / AlphaXiv, iterative citation, uploaded file access | △ | ✓ |
ari-skill-memory |
Ancestor-scoped node memory (JSONL) | ✗ | ✓ |
ari-skill-transform |
BFTS tree → science-facing data + EAR generation | ✓ | ✓ |
ari-skill-plot |
Matplotlib/seaborn figure generation | ✓ | ✓ |
ari-skill-paper |
LaTeX writing + BibTeX + review | ✓ | ✓ |
ari-skill-paper-re |
ReAct reproducibility verification | ✓ | ✓ |
ari-skill-figure-router |
Figure type classification and generation routing (SVG/matplotlib/LaTeX) | ✓ | ✓ |
ari-skill-benchmark |
CSV/JSON analysis, plotting, statistical tests | ✗ | ✓ |
ari-skill-review |
Peer-review parsing, rebuttal generation | ✓ | ✓ |
ari-skill-vlm |
Vision-Language model figure/table review | ✓ | ✓ |
ari-skill-coding |
Code generation + execution + file read + bash | ✗ | ✓ |
ari-skill-orchestrator |
Expose ARI as MCP server, recursive sub-experiments, dual stdio+HTTP transport | ✗ | — |
✗ = no LLM, △ = LLM used in some tools only, ✓ = primary tools use LLM.
| # | Principle | Meaning |
|---|---|---|
| P1 | Domain-agnostic core | ari-core has zero experiment-specific knowledge |
| P2 | Deterministic where possible | MCP tools are deterministic by default; LLM-using tools are explicitly annotated |
| P3 | Multi-objective metrics | No hardcoded scalar score |
| P4 | Dependency injection | Switching experiments = editing .md only |
| P5 | Reproducibility-first | Papers describe hardware by specs, not cluster names |
ARI autonomously designed, implemented, ran, and wrote up an end-to-end study of CSR SpMM (sparse matrix–dense matrix multiplication) on a multi-core CPU. The full paper — including methodology, algorithms, figures, and references — is available in docs/sample_paper.pdf.
Stoch-Loopline: Burstiness- and Tail-Latency-Aware Loopline Modeling for Robust Multi-Core CPU CSR SpMM Scaling
| Configuration | GFLOP/s | Effective Bandwidth |
|---|---|---|
| K-blocked CSR SpMM (peak throughput) | 23.82 | 58.30 GB/s |
| Validation sweep (peak, N = 16, 32 threads) | 26.22 | 65.55 GB/s |
| Highest measured bandwidth (root sweep) | 17.17 | 105.18 GB/s |
| Software prefetching gain (mean across widths) | +3.53 | +8.18 GB/s |
Hardware: fx700 multi-core CPU node, OpenMP, 32 threads. Synthetic CSR matrices up to M = K = 200,000 (≈ 3.2M nonzeros), uniform and Zipf row-length distributions, dense widths N ∈ {4, 8, 16, 32, 64, 128}.
What ARI produced autonomously: the Stoch-Loopline modeling framing, two CSR×dense kernel variants (row-parallel gather and rows-in-flight) with explicit unrolling/window knobs, the K-blocked / N-tiled+packed / scalar / no-AVX ablations, the experimental sweep, the figures, the references, and the reproducibility verification — all without human intervention.
MIT. See LICENSE.