VELION LLM

Agent-native distributed AI architecture focused on governed execution, Nano Core Agents, evidence, and scalable research.

Current public status: reference implementation + architecture specifications. Long-range capacity figures are design targets unless live evidence says otherwise.

Try the reference implementation

deno run public_release/demo.ts
deno run public_release/tests/velion_reference_test.ts
deno run public_release/benchmarks/local_reference_benchmark.ts

The public reference package demonstrates a small subset of VELION concepts: identity validation, explicit capabilities, deny-by-default checks, deterministic local execution, and SHA-256 evidence records.

REFERENCE_IMPLEMENTATION != PRODUCTION_VELION_RUNTIME

Architecture in one minute

A VELION task is modeled as:

IDENTITY → CAPABILITY → TASK → POLICY → EXECUTION → EVIDENCE

Security rules:

Identity != Authority
Capability != Approval
Intelligence != Privilege
Memory != Authority

Evidence

Public tests and benchmarks must be reproducible. Local/synthetic measurements are labeled as such and are never presented as distributed live capacity.

Scale

The architecture explores scale classes from small specialist teams toward large distributed fleets. Figures such as 10,000, 100,000, and 1,000,000 NCA are TARGET_CAPACITY / DESIGN_CAPACITY until stronger evidence is independently available.

See docs/SCALE-TRUTH.md.

Limitations

This public repository does not by itself prove production deployment, proprietary model weights, global adoption, revenue, partnerships, or live operation at long-range target capacity.

Research and contribution

Useful contributions include reproducible tests, security reviews, scheduler experiments, observability work, documentation corrections, and benchmark methodology improvements.

Authorship

VELION LLM is developed within IMPERIAL Core. Public architecture and project direction: Alexander Romaskevich (RomaskevicH). HANTER is the Chief Systems / AI Command Center role within the architecture.