# Summary of 3GPP Technical Document S4-260181

## Document Overview
This is a revision of S4aR260012 proposing additional details for negotiation messages and associated metadata in support of AI/ML-based media services (AIML_IMS-MED). The document provides JSON-formatted metadata examples and updates to align with the agreed call flow from S4aR260014.

## Main Technical Contributions

### 1. Negotiation Message Summary Table (Section A.4.2)

The document introduces **Table A4.2-1** which defines the complete set of negotiation messages for local inferencing call flows. Key updates include:

- **AI_APPLICATION_DISCOVERY_REQUEST/RESPONSE**: Discovery of AI/ML application families/types with optional UE capability filtering
- **AI_APPLICATION_REQUEST/RESPONSE**: Selection of specific AI/ML application with URN, returning application binary data and metadata
- **CANDIDATE_MODELS_LIST_REQUEST/RESPONSE**: Renamed from previous version, exchanges UE capabilities for list of candidate models
- **AI_MODEL_SELECTION_REQUEST/RESPONSE**: Model selection using URN(s), returning model binary data and metadata

Each message is mapped to possible HTTP protocol operations (GET, POST, RESPONSE) and associated metadata parameters.

### 2. Metadata Information Definitions (Section A.4.3)

#### A.4.3.1 Application Metadata

Defines characteristics and requirements of AI/ML applications including:

- **applicationIdentifier**: URN-based identification
- **taskList**: Contains task type identifiers, supported task types (ASR, TTS, Translation)
- **Performance constraints**:
  - maximumTaskInferenceLatency (milliseconds)
  - minimumTaskInferenceAccuracy
  - maximumLocalEnergyConsumption (joules)
  - taskAccuracy (e.g., mAP score)
- **taskOperationalCharacteristics**: computeIntensity, memoryFootprint, latencySensitivity, energySensitivity
- **associatedModels**: List of models with modelName and modelDescription

#### A.4.3.2 Endpoint Capabilities Metadata

Separates capabilities into **static** and **dynamic** categories:

**Static Capabilities** (fixed/infrequently changed):
- endpointIdentifier
- flopsProcessingCapabilities (peak compute in FLOPS)
- macOpProcessingCapabilities (MAC operations)
- supportedAiMlFrameworks
- accelerationSupported (boolean)
- supportedEngines (CPU, GPU, NPU)
- supportedPrecision (FP32, FP16, INT8)

**Dynamic Capabilities** (runtime-dependent):
- availableMemorySize
- currentComputeLoad
- energyMode (Eco/Balanced/Performance)
- batteryLevel
- acceleratorAvailability

This separation enables both long-term compatibility checks and short-term runtime optimization.

#### A.4.3.3 Model Information Metadata

Comprehensive model characterization including:

- **Identification**: modelIdentifier (URN), taskIdentifier (supports multi-task models)
- **Model properties**: modelSize (MB), format, formatVersion, framework, frameworkVersion
- **Input/Output specifications**:
  - inputMediaIdentifier, inputType, inputShape
  - outputIdentifier, outputType, outputShape, outputAccuracy
- **Performance metrics**:
  - targetInferenceLatency (with hardwarePlatformIdentifier)
  - flopsProcessingCapabilities
  - macOpProcessingCapabilities
  - energyEstimation (joules, platform-specific)
- **Data types**: modelDataType (Uint8, Float32, Float16)

### 3. Generic Negotiation Message Format (Section A.4.4)

Defines a **transport-protocol-independent** message format for AI metadata exchange over data channels:

**Messages Container**:
- Array of Message objects (1..n cardinality)

**Message Data Type** includes:
- **id**: Unique identifier within data channel session scope
- **type**: Message subtype enumeration:
  - CANDIDATE_MODELS_REQUEST
  - CANDIDATE_MODELS_RESPONSE
  - AI_APPLICATION_DISCOVERY_REQUEST/RESPONSE
  - AI_APPLICATION_REQUEST/RESPONSE
  - AI_MODEL_SELECTION_REQUEST/RESPONSE
- **payload**: Type-dependent message content
- **sessionId**: Associated multimedia session identifier
- **sendingAtTime**: Wall clock timestamp (optional)

This format provides flexibility for various transport protocols (e.g., HTTP) without imposing specific constraints.

## Key Design Principles

1. **Separation of concerns**: Application, endpoint, and model metadata are independently defined
2. **Static vs. dynamic distinction**: Enables efficient capability negotiation and runtime adaptation
3. **Protocol independence**: Generic message format supports multiple transport options
4. **Comprehensive metadata**: Covers functional, performance, energy, and accuracy requirements
5. **Multi-task support**: Models can serve multiple AI/ML tasks
6. **Platform-specific metrics**: Latency and energy measurements tied to hardware platforms