TachyRoute

The Explainable, Multimodal, Early-Exit Decision Engine

TachyRoute is a revolutionary open-source Non-Autoregressive Decision Engine designed to redefine how machine learning systems make fast, explainable choices. By unifying multimodality, early-exit adaptive compute, and real-time evidence extraction, TachyRoute achieves state-of-the-art results across massive industry benchmarks in a single forward pass.

https://github.com/user-attachments/assets/16cbbfcd-e2d9-4912-96e0-33d88866aacc

It evaluates typed decisions (choice, score, boolean) over any state (text, code, or structured JSON) in under 15 milliseconds—without generating text, hallucinating, or parsing fragile JSON outputs.

🚀 The TachyRoute Leap (Why it's a Historic Breakthrough)

TachyRoute introduces three structural paradigm shifts to AI decision systems:

1. Adaptive Early-Exit Computing: Why run a 24-layer transformer for a simple question? TachyRoute actively monitors confidence during the forward pass. If the decision is clear at layer 6, it exits compute immediately. This drops P99 latency to a blistering ~15ms while retaining 100% of the accuracy.
2. Explainable Evidence Extraction (Free of Charge): Trust is everything. TachyRoute doesn't just output a decision; it leverages Attention Rollout on the exit layer to return the exact text span that caused the decision—adding 0ms to the inference time.
3. Dynamic Complexity Routing: Native zero-shot routing redirects massive workloads seamlessly between lightweight quantized models and massive multi-lingual experts (100+ languages supported) based strictly on computational necessity.

🏆 Benchmark Dominance (MASSIVE Intent & XNLI)

TachyRoute was built to shatter existing zero-shot classification and NLI ceilings.

💻 Quickstart

from tachyroute import Router

# Initialize TachyRoute with automated model routing
router = Router(preload=True)

state = "Hi, we were billed twice for March. Please refund the duplicate today or we will cancel our plan."

questions = {
    "department": {
        "type": "choice",
        "instructions": "Which department should handle this?",
        "criteria": {
            "billing": "invoices, payments, refunds",
            "technical": "bugs, outages, system errors"
        }
    },
    "urgency": {
        "type": "score",
        "instructions": "How urgent is this?",
        "criteria": ["not urgent", "soon", "critical"]
    }
}

# One forward pass, lightning fast.
result = router.predict(state, questions)

print(f"Decision: {result['answers']['department']['answer']}") 
# Decision: billing

print(f"Evidence: {result['answers']['department']['evidence']}") 
# Evidence: ["billed twice for"] -> True Explainability!

print(f"Compute depth: {result['routing']['reason']}") 
# Compute depth: Routed based on text characteristics. Max compute depth: 6 layers.

📦 Installation

Get started instantly:

pip install tachyroute

For serving the model over a high-performance HTTP API:

pip install tachyroute[serve]

🤝 Join the Revolution: Contribute to TachyRoute

We are building the future of structured AI decisions, and we want you to be a part of it. TachyRoute is completely open-source, and we are aggressively welcoming contributors to help us expand its capabilities.

Whether you want to optimize CUDA kernels, add new multimodality streams (vision/audio), or build native Rust bindings, there is a place for you here.

• Read the Contributing Guide to get started.
• Join the discussion in our GitHub Issues.
• Star the repo to support the movement!

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