Trio-Spark v1.0
Trio-Spark is MachineFi's first Situated World Model: a fast decision model for agents acting inside a specific environment. Give it the current state and 2–8 allowed moves. One pass returns the selected move and a probability for every option.
Trio-Spark is available through a hosted API. Model weights and training code are not distributed from this repository.
Try the playground · API docs · Demos and clients · Launch post
What it does
environment signals → structured state → Trio-Spark → next move → executor
↑ │
└──── fresh feedback ─────┘
Spark handles the bounded judgment step. Your application supplies the state, legal actions, executor, and safety controls. This pattern works across software agents, games, robots, and operational systems.
API
Create an API key in the Trio-Spark console.
curl https://platform.machinefi.com/api/spark/v1/decisions \
-H "Authorization: Bearer $TRIO_SPARK_API_KEY" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: $(uuidgen)" \
-d '{
"model": "trio-spark-preview",
"task": "Keep the machine safe while completing the operation.",
"state": {"temperature_c": 84, "load_pct": 91, "vibration": "rising"},
"choices": [
{"id": "continue", "description": "Continue at the current speed"},
{"id": "slow", "description": "Reduce speed and keep observing"},
{"id": "stop", "description": "Stop the machine now"}
]
}'
The response contains choice_id, the complete probability distribution, confidence, usage, and latency. The public API model identifier remains trio-spark-preview; the product release is Trio-Spark v1.0.
Measured examples
The public demo repository includes replayable evidence from production API runs.
| Environment | Result |
|---|---|
| Autonomous drone | 65 s, 25 calls, 75.7 m flown, 0 collisions, course completed |
| Browser agent | 4.002 s, 3 calls, requested item selected, autonomous DONE |
| Robot arm | 13.68 s, 13 calls, physical success, 0 forbidden contacts |
These demos send structured environment signals to Spark. They do not claim direct control from raw pixels.
Evaluation snapshot
The launch evaluation measured the production API on four public classification datasets.
| Benchmark | Accuracy |
|---|---|
| SST-2 | 92.89% |
| AG News | 88.46% |
| PubMedQA | 85.33% |
| TweetEval | 80.58% |
See the launch post for the published context. A JevBench adapter and independent sealed-set evaluation request are maintained separately; no official JevBench score is claimed here until that evaluation is complete.
Intended use
Trio-Spark is designed for frequent, low-latency decisions over an application-defined action set: routing, tool selection, GUI action choice, game moves, robot skills, and operational control.
It should sit behind application-level validation in safety-sensitive systems. The caller remains responsible for legal-action filtering, permissions, execution safeguards, and fallback behavior.
Access and licensing
The API is a commercial hosted service. The public Trio-Spark repository contains Apache-2.0 clients, demo integrations, and measured evidence. That code license does not grant access to model weights or training artifacts.