Multimodal Generation Layer

The Multimodal Generation Layer is the topmost interface of the KOLI Model Stack — responsible for all user-facing AI content production. It consolidates diverse large model capabilities across modalities (text, voice, vision, video), enabling KOLI agents and applications to interpret and generate content in rich, natural, and dynamic formats.

By leveraging unified semantic representations and standardized API interfaces from the AI Engine Layer, the system allows seamless cross-modality orchestration across its submodules.

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Layer Structure Overview

Multimodal Generation Layer
├── LLM (Large Language Model) – Text understanding & generation
├── LASM (Large Audio & Speech Model) – Speech/audio comprehension & synthesis
├── LVLM (Large Vision-Language Model) – Image-text multimodal interaction
└── LVM-Video (Large Video Model) – Video comprehension & generation

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1. LLM – Large Language Model

Function: Provides the foundational capability for natural language understanding and generation.

Responsibilities:

• Conversational reasoning and Q&A

• Content generation and co-authoring

• Language grounding for other modalities (e.g., interpreting visual descriptions)

Use Case: In dialogue scenarios, the LLM interprets user intent and composes coherent responses. In cross-modal interactions, it ingests outputs from LASM or LVLM and generates structured or freeform textual content.

Input: "Explain what’s happening in this image."
→ [LVLM parses image] → [LLM generates explanation]

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2. LASM – Large Audio & Speech Model

Function: Handles speech and audio modalities, enabling real-time human-AI voice interaction.

Subcomponents:

• ASR (Automatic Speech Recognition): Voice-to-text transcription

• TTS (Text-to-Speech): Text-to-natural speech synthesis

• Speech understanding & prosody modeling

• Optional: music/audio generation and environment sound modeling

Use Case: An agent converts user voice to text via ASR, interprets it using LLM, and returns a spoken response via TTS — all in real time.

graph LR
  A[User Voice] --> B[ASR Engine]
  B --> C[LLM Response]
  C --> D[TTS Engine]
  D --> E[Audio Reply]

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3. LVLM – Large Vision-Language Model

Function: Fuses visual and textual information to support image understanding and image-conditioned generation.

Capabilities:

• Image captioning and visual reasoning

• Image-based Q&A (e.g., “What token logo is this?”)

• Prompt-based image generation or retrieval

Use Case: A user uploads a screenshot of a meme token. The LVLM interprets it and passes the results to the LLM, which contextualizes and explains the project's relevance or community sentiment.

{
  "image_input": "memecoin_logo.png",
  "lvlm_caption": "Dog-themed token icon with $FLOKI branding",
  "llm_output": "FLOKI is part of the meme-token ecosystem, often compared to DOGE and SHIB..."
}

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4. LVM-Video – Large Video Model

Function: Processes and generates temporal visual content (video) to allow dynamic storytelling and visual analytics.

Capabilities:

• Video summarization and scene segmentation

• Cross-modal retrieval (e.g., “Show me when he mentions Bitcoin”)

• Script-to-video synthesis (future-facing)

• Animation generation from still images + audio

Use Case: An agent digests a livestream recording, extracts key timestamps related to market trends, and generates a 60-second highlight reel.

Input: Video + prompt “Summarize price movement explanation”
→ Output: Video segment [00:35–01:12] with LLM-generated subtitles

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Cross-Modality Collaboration (Pipeline Example)

Multimodal generation is not isolated per modality. Each module interoperates via shared semantic tokens and unified orchestration logic.

flowchart TD
  A[Voice Input] --> B[ASR (LASM)]
  B --> C[LLM: Interpret Intent]
  C --> D[LVLM: Analyze Image]
  D --> E[LLM: Compose Response]
  E --> F[TTS (LASM)]
  F --> G[Final Audio Output]

This architecture allows agents to:

• Listen (voice input)

• Understand (textual/visual context)

• Respond (text/audio/video output)

• Adapt (based on language, tone, or environment)

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Semantic Context Management

All submodules rely on a shared semantic context layer — a memory representation of user dialogue, image embeddings, audio tokens, and metadata. This allows:

• Context-aware switching between modalities

• Stateful responses across long sessions

• Reduced hallucination by grounding across input signals

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Powered by the AI Engine Layer

Each modality module in the generation layer delegates actual computation to the lower Engine Layer (EnginePool), enabling:

• Modular extensibility (e.g., replace TTS engine)

• Scalable inference

• Hardware-level abstraction (GPU, edge, etc.)

Multimodal Generation Layer
    ↓ API Call →
AI Engine Layer (EnginePool)
    → Run model (LLM, TTS, etc.)
    → Return result

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Summary

The Multimodal Generation Layer transforms KOLI’s AI stack from a language-only interaction model into a rich, perceptual, and expressive system capable of:

• Natural dialogue

• Voice interfaces

• Visual understanding

• Video interpretation and generation

Together, LLM, LASM, LVLM, and LVM-Video form the sensory and expressive system of KOLI’s AI agents — creating immersive, multimodal Web3-native companionships.