Technical Architecture and Technical Logic

1.1 Underlying Technical Architecture Design

Floa's core architecture is dual-driven by "Modular Microservices" and "Real-Time Digital Human Engine". Based on our self-developed digital human engine and private large model cluster, we focus on overcoming key challenges including real-time interaction of digital humans, secure rights confirmation of Web3 assets, and ecological expansion efficiency.

The overall technology stack is divided into five layers:

1.1.1 Infrastructure Layer: Ensuring Real-Time Interaction & Data Security

• Computing Resources: Adopt a three-tier architecture of "Hybrid Cloud + Edge Nodes + Rendering Accelerators". To meet the real-time driving of digital humans' facial expressions, movements, and voices, we have deployed edge rendering nodes across multiple global regions, optimizing end-to-end interaction latency to less than 100ms.

• Storage Solutions: Implement hierarchical processing based on data characteristics. Core assets (e.g., digital human avatars, motion libraries) are stored on IPFS with on-chain certification; training and interaction data use a distributed file system; we have specially designed a "Digital Asset Repository" to support high-concurrency, millisecond-level resource calls.

• Security Mechanisms: Build a three-layer protection system—smart contracts undergo third-party audits, data transmission is encrypted throughout, and each digital human identity is uniquely authenticated and copyright-anchored via blockchain, ensuring Web3-native security for assets and data.

1.1.2 Core Technology Layer: Deep Integration of Large Models & Digital Human Engine

This layer serves as Floa's intelligent core, enabling a closed loop from "Perception" to "Generation" and then to "Decision-Making". We have conducted in-depth optimizations based on open-source architectures, with the key improvement lying in the collaborative reasoning efficiency between large models and the digital human engine.

Below is a code snippet illustrating our interaction logic.

python
# Core Interaction Engine: End-to-End Generation from Text to Digital Human Performance
class FLOAAgentCore:
    def __init__(self, model_path: str, renderer_config: dict):
        # Load the large model, using bfloat16 precision to balance performance and overhead
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.llm = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.bfloat16,
            device_map="auto"
        )
        # Initialize the digital human rendering engine
        self.face_renderer = FaceRenderer(renderer_config["face"])
        self.motion_controller = MotionController(renderer_config["motion"])
        
    def generate_response(self, user_input: str) -> tuple:
        # 1. Generate text responses (with integrated context management)
        prompt = self._build_prompt(user_input)
        response_text = self._generate_text(prompt)
        
        # 2. Parallelly drive digital human performance (a key FLOA optimization)
        emotion = self._predict_emotion(response_text)  #Lightweight sentiment analysis
        motion_sequence = self._generate_motion(emotion, response_text)
        
        # 3. Compose rendering data streams
        render_data = {
            "facial": self.face_renderer.render(emotion),
            "motion": self.motion_controller.execute(motion_sequence)
        }
        return response_text, render_data

Our Core Optimizations:

Model Collaboration: Collaborate with leading model teams to customize a "Digital Human Multimodal Enhancement Layer" on the foundation model. This layer unifies the reasoning of speech, semantic, and visual signals, significantly enhancing interaction naturalness.

Decision Engine: Integrate rule-based engines with RLHF strategies. It not only handles task planning but also dynamically adjusts digital humans' interactive performance (e.g., tone, microexpressions), synchronizing "task execution" and "emotional interaction".

Tool Framework: The built-in API gateway has integrated 30+ services, with a dedicated ecological open interface layer designed. In the future, via key management and rate limiting, we will safely open digital human capabilities to developers.

1.1.3 Agent Capability Layer: Scalable Digital Human Skill System

• Basic Capabilities: Offer out-of-the-box voice/text conversation, real-time avatar driving, and basic task automation (e.g., schedule management, information retrieval).

• Advanced Capabilities: Gradually unlocked through training, including multi-agent collaboration (virtual teams), commercial scenario customization (brand endorsement, virtual live streaming), and Web3 asset integration (NFT management, on-chain transactions).

• Personalized Customization: Support full-dimensional customization from appearance (character modeling, outfits) and skills (model fine-tuning) to interaction styles (tone, expression preferences).

1.1.4 Ecological Interaction Layer: Multi-terminal & Cross-platform Adaptation

• User Interfaces: Compatible with Web, mobile DApps, and VR/AR devices, ensuring consistent digital human rendering across terminals. Provide a low-code editor for users to quickly create exclusive digital humans.

• Developer Interfaces: Gradually opened in phases: V1.5 (Basic Capability APIs) → V2.0 (Large Model Collaboration APIs) → V3.0 (Complete SDK & Developer Platform).

• Cross-ecosystem Integration: Seamlessly integrate with Web3 wallets, exchanges, and traditional SaaS services (e.g., WeChat Work, Slack), enabling cross-scenario interoperability of digital human identities.

1.1.5 Incentive & Governance Layer: Building a Value Closed Loop

• Smart Contracts: Implement token incentives for training contributions, NFT-based rights confirmation for digital human assets, and support rights circulation such as NFT staking and trading.

• Decentralized Governance: Plan to introduce a DAO mechanism, allowing core NFT holders to participate in community governance of API standards, copyright norms, and incentive policies.