AETHER // NANO · RESEARCH PREVIEW
Nano — The compiled execution language for autonomous systems
Reason once. Compile. Execute deterministically. Escalate only when necessary.
How do autonomous systems stop reasoning about the same thing over and over?
Today's agents
LLM → Think → Tool → Think → Tool
Every decision is another API call. Another two seconds. Another line on the invoice. The agent re-derives the same conclusion it reached yesterday — and the day before — because it has no way to keep what it already knows.
With Nano
LLM → Compile → Nano IR → Execute → escalate only when necessary
Nano compiles known reasoning into deterministic execution. Inference becomes the exception; execution becomes the default. Think of it as compiled memory — the system keeps what it has already figured out and runs it at machine speed.
What Nano looks like
strategy Momentum {
every 5m {
observe market
if RSI(14) < 30
and volume > average {
ATS.wake()
ATS.analyze()
execute()
}
}
}A strategy declares when it wakes, what it observes, and the conditions under which it acts. The compiler turns this into Nano IR — a deterministic decision graph the runtime can execute millions of times without a single model call.
Note what the strategy does not do: it never touches an exchange, a socket, or a clock directly. It emits intents; the runtime disposes of them within declared capability boundaries.
Restrictions are the feature
Deterministic Replay
Clock and entropy are injected, never ambient. Re-running a Nano program against the same inputs produces bit-identical execution — every decision reproducible, every run auditable.
No Exchange API in the Language
Strategies emit intents. The runtime — not the strategy — decides how intents are disposed. The language physically cannot place an order, send a packet, or touch the outside world on its own.
Effect Manifests as Capability Boundaries
Every program declares the effects it may produce. The runtime enforces that manifest as a hard capability boundary — undeclared effects are not throttled, they are impossible.
Admission Gates on AI-Compiled Workflows
Workflows compiled by a model do not enter the runtime unreviewed. Admission gates validate structure, effects, and bounds before anything executes.
Nano intentionally removes the constructs that destroy replayability. Each of these is absent from the language by design:
The substrate beneath the models
Claude / GPT / Gemini / local model → Nano → Execution
Nano never depends on a particular model. Any model capable of reasoning about a task can compile that reasoning into Nano IR — and the resulting program executes identically no matter which model produced it. Swap the model; keep the execution substrate.
Sealed by Protocol C
Nano orchestrates safe optimization loops — Propose → Compile → Replay → Benchmark → Verify → Policy Gate → Deploy. Nothing self-modifies without approval.
Every loop is bound to Aether's cryptographic execution protocol as engineering provenance: who proposed, which model, what objective, which benchmark approved, who accepted deployment. Deterministic execution plus cryptographic custody — accountability by construction.
Explore the Protocol Family →Heterogeneous compute, including quantum
AI → Nano → Quantum Runtime → Qiskit → IBM Quantum
Nano sits above quantum SDKs — it does not replace them. On the roadmap, the same IR could target Azure Quantum, IonQ, or D-Wave through the same runtime boundary. These backends are roadmap, not current capability.
Most AI frameworks stop at reasoning. Most quantum frameworks stop at execution. Most optimization frameworks stop at search. Nano coordinates all three.
One compiler. One IR. Increasing capability.
Nano
Deterministic execution language
The foundation. Compile known reasoning into Nano IR and execute it deterministically — replayable, capability-bounded, auditable by construction.
Nano+
Adaptive execution
Memory, confidence routing, multi-agent coordination, and learning on top of the same IR — execution that adapts without giving up determinism where it matters.
Nano++
Distributed optimization
Massive execution graphs, high-performance scheduling, and research workloads distributed across infrastructure. One compiler. One IR. Increasing capability.
Trading is the proving ground, not the ceiling
ATS is Nano's first autonomous engineering workload. Markets are the hardest honest test of an execution architecture — deterministic inputs, replayable history, measurable outcomes — but the loop it runs is a deterministic engineering loop, and trading is just the environment. The same architecture generalizes to any system that observes, decides, acts, and records.
Observe → Compile → Execute → Measure → Analyze → Learn → Recompile → Replay → Benchmark → Deploy
The question series
Papers publishing soon
Why not Go?
Go tells a computer how to compute. Nano tells an autonomous system when to act.
Why not TypeScript?
TypeScript models applications; Nano models deterministic decision graphs. Removing mutable state, async, and IO isn't missing functionality — it's removing uncertainty.
Why not Python?
Python is excellent for reasoning. Nano is built for repeated execution — reason once, compile, execute millions of times.
Why deterministic?
So “why did the agent do that?” is answerable exactly, every time.
Definition
Nano is the execution architecture for autonomous engineering systems. It compiles reasoning into deterministic execution, orchestrates safe optimization loops, and enables AI to improve complex systems — from quantitative trading and robotics to quantum computing — through reproducible, auditable, and continuously learnable workflows.
Known reasoning is compiled into a deterministic, replayable intermediate representation (Nano IR); novel situations escalate back to model reasoning.
Research preview. The Nano IR, deterministic reference interpreter, and memory layer are implemented and tested. The language front end, CLI, and public library are in progress.
Deterministic Replay · Capability Boundaries · Admission Gates · Protocol C Custody