MachineFi Lab

Situated World Models.
Built to Move.

[MachineFi Lab @machinefilab]

Trio-Spark v1.3 is live ⚡

The SOTA System-1 decision model for autonomous agents.

Most agent pipelines burn compute generating text just to pick an action. Spark turns action selection into a single forward pass: zero generated tokens, perfectly calibrated confidence.

🏆 SOTA Performance Across 13 Benchmarks:
• 90.9% on the ten-task panel (+7.8% over v1.0, overtaking Jev 1.13 and level with Clef)
• #1 among all 7 models on PubMedQA, RTE, TweetEval, and SNLI
• Calibration error crushed to 0.019 (vs 0.234 in v1.0) — true confidence you can actually threshold

Upgrade is drop-in: same API, identical limits, text & multimodal vision support. Just point to "model": "trio-spark-v1.3".

⚡ Get 100 free decisions: https://t.co/OkcxCnTxF6

📖 Full benchmark report & transparency note:
https://t.co/AH03rVIoZ6

[MachineFi Lab @machinefilab]

🚀 Trio-Spark DI (9B) just hit the Top 10 on the Decision Index for models ≤9B!

Built for bulletproof agent reliability in the real world, not benchmark inflation:
✅ 0 errors on 110k+ requests
✅ 97.04% independent match rate
✅ Zero text generation overhead

Turn action selection into a single, perfectly calibrated forward pass. Live now as a gated 9B GGUF (CC BY-NC 4.0).

📊 Board: https://t.co/dATFanB6wn
📦 Weights: https://t.co/jQXhJRW8qP

[MachineFi Lab @machinefilab]

Trio-Spark v1.3 is live ⚡

The SOTA System-1 decision model for autonomous agents.

Most agent pipelines burn compute generating text just to pick an action. Spark turns action selection into a single forward pass: zero generated tokens, perfectly calibrated confidence.

🏆 SOTA Performance Across 13 Benchmarks:
• 90.9% on the ten-task panel (+7.8% over v1.0, overtaking Jev 1.13 and level with Clef)
• #1 among all 7 models on PubMedQA, RTE, TweetEval, and SNLI
• Calibration error crushed to 0.019 (vs 0.234 in v1.0) — true confidence you can actually threshold

Upgrade is drop-in: same API, identical limits, text & multimodal vision support. Just point to "model": "trio-spark-v1.3".

⚡ Get 100 free decisions: https://t.co/OkcxCnTxF6

📖 Full benchmark report & transparency note:
https://t.co/AH03rVIoZ6

[MachineFi Lab @machinefilab]

We showed our multimodal fast-decision model Trio-Spark random photos of US trucks and asked one question: "What is it hauling?"

• Zero training. Zero examples.
• 83% accuracy across 7 trailer types.
• 100% hazmat placards caught.
• Cost: Just $14 per MILLION photos. 🤯

Extracting equipment, hazmat, or load status on every highway frame used to be cost-prohibitive. Now, Spark delivers calibrated probabilities in a single pass, letting your pipeline automate the confident calls and only route edge cases to humans.

Run a camera network? Send us 10k labeled images and we'll prove the accuracy and cost on your own data.

Try it out: https://t.co/Q6VjLsluGe

[MachineFi Lab @machinefilab]

Picture the comment section under a popular YouTube video or live stream. Thousands of comments pour in. Most are fine, some are toxic, and a few are borderline.

We built a tiny demo: Trio-Spark v1.3 reads 600 real comments as they arrive and gives each one a probability that it's toxic.

You set two lines:
🟡 above the first → a human moderator reviews it
🔴 above the second → hidden automatically

Want it stricter? Drag the line. All 600 comments re-sort instantly, with no new model calls.

88% agreement with human raters. $0.0017 for all 600. 👇

[MachineFi Lab @machinefilab]

Your doorbell cam doesn't need to ping you for everything.

We pointed Trio-Spark at a front door for a day:
📦 package dropped off → "Package at your door"
😊 family walks in → "Welcome home," no alert
🚨 someone with a flashlight at 2 AM → alert

[QuickSilver Pro @qspro_ai]

Trio-Spark v1.3 is live on QuickSilver Pro ⚡

The SOTA System-1 decision model. One forward pass, zero generated tokens, a calibrated probability for every choice.

• 90.9% on a 10-task decision panel
• #1 of 7 models on PubMedQA, RTE, TweetEval & SNLI
• Calibration error 0.019: confidence you can threshold

$0.042 / 1M input tokens. Output is free.

Same QSP key, POST /v1/systemone:
https://t.co/KD9PuUwrEq

[MachineFi Lab @machinefilab]

We threw 1,000 customer-support tickets at Trio-Spark v1.3.

⏱ ~0.4 s and $0.000005 per ticket, on a single T4 (a low-end @nvidia GPU)
🎯 94.7% exactly matched human routing
💸 $0.005 total. Half a cent for all 1,000.

This is System-1 scaling in the real world. One forward pass per ticket.

[MachineFi Lab @machinefilab]

Trio-Spark v1.3 is live ⚡

The SOTA System-1 decision model for autonomous agents.

Most agent pipelines burn compute generating text just to pick an action. Spark turns action selection into a single forward pass: zero generated tokens, perfectly calibrated confidence.

🏆 SOTA Performance Across 13 Benchmarks:
• 90.9% on the ten-task panel (+7.8% over v1.0, overtaking Jev 1.13 and level with Clef)
• #1 among all 7 models on PubMedQA, RTE, TweetEval, and SNLI
• Calibration error crushed to 0.019 (vs 0.234 in v1.0) — true confidence you can actually threshold

Upgrade is drop-in: same API, identical limits, text & multimodal vision support. Just point to "model": "trio-spark-v1.3".

⚡ Get 100 free decisions: https://t.co/OkcxCnTxF6

📖 Full benchmark report & transparency note:
https://t.co/AH03rVIoZ6

[MachineFi Lab @machinefilab]

Meet Trio-Spark v1.1 — now with vision. 👀

A System-1 model that doesn't just describe the world—it makes decisions. Screenshots/video in, action probabilities out. One forward pass.

✅ Beat Clef-Flash on GQA (85.3% vs 76.8%)
✅ 53% lower API cost
✅ Fast enough to drive an arcade game live

Build your next agent with it. Try 100 free decisions now: https://t.co/OkcxCnTxF6

Benchmarks + demos: https://t.co/tSrwUiG0Ni

[MachineFi Lab @machinefilab]

Trio-Spark v1.0 is now listed on https://t.co/HqFsG6DkrR ✨

A Situated World Model for fast judgment. Give it a scenario and 2–8 possible moves—it returns the choice and full probability distribution in one pass.

Listing: https://t.co/aLQ3Szs3QS
Try it: https://t.co/pTYCtLWPV8

[MachineFi Lab @machinefilab]

Following our drone deployment, Trio-Spark v1.0 now generalizes to robotic manipulation.

By continuously evaluating the physical state, the model executes a strict sequence of 8 manipulation skills to complete the task in 13.68 seconds with zero collision violations.

Entirely token-free. One model inference per action boundary.

Live API: https://t.co/Q6VjLsluGe

Code & Demo: https://t.co/DWQFHDMO3X

[MachineFi Lab @machinefilab]

To validate its real-time physical capabilities, we integrated the API into a fully autonomous drone loop. The model successfully navigated a dynamic obstacle field, making 25 consecutive tactical decisions over a 75.7m course with zero collisions.

The framework is open-source. We invite the community to test the API and contribute new physical AI demos.

API: https://t.co/OkcxCnTxF6
GitHub: https://t.co/DWQFHDMO3X

[MachineFi Lab @machinefilab]

Today we're launching the first model of the Trio family: Trio-Spark v1.0, a Situated World Model built purely for instant action. Feed it a scenario and up to 8 possible moves, and it instantly returns the optimal choice and full probability distribution in exactly ONE pass.

Zero output tokens. Lightning-fast agent loops.

🏆 Benchmarks (Production T4 API):
• SST-2: 92.89% Accuracy
• AG News: 88.46% Accuracy
• Jevals PubMedQA: 85.33% Accuracy (DS 42.36)
• TweetEval (Offensive): 80.58% Accuracy
• PubMedQA (3-way): 68.60% Accuracy
• SST-5: 53.03% Accuracy
• Jevals HelpSteer2: 92.67% Coverage
• JevBench (Public 231): 81.39% Coverage

Whether your agent needs to click a control, route a request, place a game piece, or pause a machine, Spark decides what happens next.

Try it now: https://t.co/OkcxCnTxF6

Full benchmarks & demos: https://t.co/BRAui6iJBk

[MachineFi Lab @machinefilab]

Today we're launching the first model of the Trio family: Trio-Spark v1.0, a Situated World Model built purely for instant action. Feed it a scenario and up to 8 possible moves, and it instantly returns the optimal choice and full probability distribution in exactly ONE pass.

Zero output tokens. Lightning-fast agent loops.

🏆 Benchmarks (Production T4 API):
• SST-2: 92.89% Accuracy
• AG News: 88.46% Accuracy
• Jevals PubMedQA: 85.33% Accuracy (DS 42.36)
• TweetEval (Offensive): 80.58% Accuracy
• PubMedQA (3-way): 68.60% Accuracy
• SST-5: 53.03% Accuracy
• Jevals HelpSteer2: 92.67% Coverage
• JevBench (Public 231): 81.39% Coverage

Whether your agent needs to click a control, route a request, place a game piece, or pause a machine, Spark decides what happens next.

Try it now: https://t.co/OkcxCnTxF6

Full benchmarks & demos: https://t.co/BRAui6iJBk

[raullen @Raullen]

@liquidai just shipped an incredible on-device agentic model. The best part? It's so efficient that it's absolutely perfect for everyday 8GB or 16GB Macs.

We just pushed day-one support in rapid-mlx 0.12.4 to make it fly on your machine:

⚡️ 97.8 tok/s on an M2 Pro
🪶 2.0 GB peak memory
⏱️ 4.8s cold boot
🧠 128K context

Run it locally with one command:
- rapid-mlx serve lfm2.5-2.6b-4bit

[MachineFi Lab @machinefilab]

6/6 Start Building now 👇

▶️ Playground - watch Trio read real footage: https://t.co/E8th7NENo7

⭐ Trio- Retina on GitHub: https://t.co/Dbts4O1ZND

📄 v1.0 technical report: https://t.co/1V9qpeBCZP

📝 Full story:
https://t.co/v8DgkWfYSX

[MachineFi Lab @machinefilab]

AI conquered the digital world — language, code, images.

Now it's coming for the physical one.

Today we're launching Trio: a world model for physical operations.

AI learned language first. Now it's operating the real world.🧵🧵

[MachineFi Lab @machinefilab]

Trio is built for the messy reality of enterprise:

Scale: Runs across entire sites, tracking people & machines at once

Speed: Low-latency, real-time execution at the edge

Cost: Enterprise efficiency at just~$0.004/query

Trust: Every decision is verifiable & backed by evidence