vaderai.ai

Vader is a physical AI data protocol that creates an open marketplace for the embodied training data required to build robots and autonomous machines. Contributors earn $VADER tokens by producing validated physical AI data; developers and research teams — called "partners" — pay $VADER to access those datasets and the model infrastructure catalog built on top of them.

The Problem Vader Addresses

Physical AI — robots and autonomous systems that perceive, reason, and act in the real world — cannot be trained on text or internet images. It needs embodied data: first-person video of human physical tasks, and structured recordings of simulated robot motion. Almost all of this data sits locked inside closed research institutions, priced out of reach for independent developers and startups. Vader proposes an open, token-coordinated path to close that access gap.

Protocol Architecture: Two Layers

Vader's protocol is split into a Data Layer and a Model Infrastructure Layer, with $VADER as the connective tissue between contributors and partners.

Data Layer — EgoPlay

EgoPlay is Vader's egocentric video collection surface. Contributors perform structured daily physical tasks on camera from a first-person perspective and upload the recordings. Because robotic systems must eventually operate from this same viewpoint, egocentric video is directly applicable training material. Each validated submission earns $VADER. Data is packaged alongside synchronized hand-tracking data, episode labels, and scene segmentation binaries at 30fps capture.

Data Layer — SimPlay

SimPlay is a browser-based physics simulation environment powered by MuJoCo. Two live simulation surfaces produce structured motion and interaction data:

• Humanoid Simulation: Contributors operate a Unitree G1 humanoid through office-environment tasks at 250Hz physics fidelity across 12 distinct environment types, with randomized friction, mass, and lighting. The protocol has generated more than 50,000 episodes to date.
• Robot Arm Simulation: Bench manipulation tasks, listed as coming soon on the roadmap.

Because each simulation session consumes compute, contributors purchase session attempts in $VADER and earn $VADER back for approved runs — making the SimPlay surface partially self-funding.

Model Infrastructure Layer

This layer is a reference catalog that maps open-source physical AI model architectures to the data the protocol collects. It does not run training itself; it shows partners which model families the data can build and lets them pay $VADER to access both the datasets and the catalog. Three architecture families are catalogued:

• WAM (World Action Models): Generalist physical AI that learns representations of action across diverse environments. Reference example: 4B-parameter model at 30fps inference.
• VLA (Vision Language Action Models): Language-conditioned policies bridging scene understanding and physical action output. Reference example: 7B-parameter model with 6DoF output.
• TSP (Task-Specific Policies): Lightweight 400M-parameter, edge-deployable models optimized for precision in narrow task domains.

Referenced architectures include OpenVLA-7B, OpenVLA-OFT (6.4x faster inference, state of the art on LIBERO and BridgeV2), pi0 from Physical Intelligence, WorldVLA, and DreamZero-14B. Benchmark results are documented against LIBERO, BridgeV2, and CALVIN ABC-D.

$VADER Token

$VADER is the protocol's native utility token with deliberately simple mechanics:

• Data contribution rewards are distributed per validated EgoPlay or SimPlay submission
• SimPlay session access is paid per attempt by contributors
• Data layer and model infrastructure access is paid by partners

The litepaper explicitly excludes governance layers and complex staking structures, keeping the token's role purely functional. The $VADER contract address is 0x731814e491571A2e9eE3c5b1F7f3b962eE8f4870 on Base Network. VADER is additionally available on Solana via Raydium and on Base via Uniswap. Total and circulating supply sits at approximately 997 million tokens against a maximum supply of 1 billion. The token launched in November 2024.

Token Flywheel

The protocol's economic design creates a compounding loop: partners pay $VADER to access data, funding contributor rewards; rewards attract more contributors; more contributors produce more and better data, which draws more partners. On SimPlay, the loop tightens further because contributors also spend $VADER on session attempts, covering compute costs without external subsidy.

Seasons and Contribution Campaigns

Vader runs data contribution in discrete seasons. Season 7 is active as of September 2026, with contributors completing tasks via EgoPlay and SimPlay to earn $VADER. This seasonal structure lets the protocol target specific data modalities or task types each round and creates recurring engagement cycles for the contributor community.

Team and Corporate Entity

The project operates under the legal entity Vader Fun Corporation. Individual team members are not named publicly in any project materials reviewed.

Security and Audits

No independent smart contract audits are cited in the litepaper or on the project website. The litepaper includes standard risk disclosures noting that smart contracts may contain errors or be subject to exploits and that the protocol is in active development.

Solana Ecosystem Fit

Vader's core protocol token was deployed primarily on Base Network, but $VADER has a secondary presence on Solana accessible via Raydium. This makes VADER available to Solana-native traders and DeFi participants. The physical AI infrastructure Vader is building — training data pipelines for humanoid robots and egocentric AI systems — addresses a genuine bottleneck in the broader AI industry. Its token-incentive model aligns closely with the DePIN design patterns that have found strong traction on Solana, applying the same logic of on-chain coordination and per-contribution rewards to an emerging, high-value data category: the embodied data that will train the next generation of autonomous machines.