# Summary of S4-260129: Call Flow for Split Inferencing

## Document Information
- **Source:** Samsung Electronics Co., Ltd.
- **Meeting:** TSG-SA WG4 Meeting #135 (February 2026, Goa, India)
- **Work Item:** AIML_IMS-MED
- **Purpose:** Approval of call flow for split inferencing

## Main Technical Contribution

This document proposes a detailed call flow for **split inferencing** in IMS-based AI/ML services, where AI model execution is distributed between the UE and network elements (MF - Media Function). The contribution is intended for inclusion in clause AC.4.3 of the base Change Request.

## Split Inferencing Call Flow

### Session Establishment and Bootstrap (Steps 1-2)
- MMTel service establishment
- Bootstrap Data Channel (BDC) establishment between UE and MF per TS 23.228, clause AC.7.1

### Application Discovery and Selection (Steps 3-6)
- **Application List Request:** UE requests available DC applications from MF via HTTP over BDC
- **MF Routing:** MF replaces root URL with replacement URL and forwards to DCSF
- **Application Metadata Creation:** DCSF creates user-specific DC application list based on:
  - User subscription information
  - Application metadata including:
    - Generic app information (description, app ID, URL)
    - AI-specific information (AI feature tag indicating requirements, AI task descriptions)
- **Metadata Delivery:** DCSF provides application list URL and metadata to UE via MF
- **User Selection:** User selects application based on AI service description and AI task annotations

### Application Download (Steps 7-9)
- UE requests selected application from MF
- MF fetches AI application from DCSF
- Application downloaded to UE via BDC along with **AI task metadata** (expressed as task manifest per clause AC.7)

### AI Task Selection and Configuration (Steps 10-13)
- **Task Presentation:** User presented with list of AI tasks supported by application, including:
  - Annotations from AI task metadata
  - Task description information
  - Information on execution endpoints supported by each task/subtask
- **User Task Selection:** User selects desired AI task(s)
- **Application Data Channel:** Established between UE and DC AS per TS 23.228, clause AC.7.2
- **Split Configuration Decision:** UE identifies which tasks/AI models to execute locally vs. in network based on:
  - User-selected AI tasks
  - AI task metadata
  - UE capabilities
- **Configuration Request:** UE requests split inference configuration from network, identifying AI models for UE and network execution

### Model Distribution and Configuration Response (Steps 14-16)
- **Requirements Check:** MF verifies requirements for network-side AI tasks/models; MF reallocation if requirements not met
- **Model Fetching:** MF obtains AI models for both UE and network execution from either:
  - DCAR via DCSF (step 15a), or
  - DC AS (step 15b - alternative)
- **Configuration Response:** MF sends response to UE including AI models for UE execution

### Inference Execution (Steps 17-22)
- **SDP Re-negotiation:** Associates media/data/intermediate data flows between UE and MF with corresponding tasks
- **UE Inference:** Tasks designated for UE execution are performed
- **Data Transfer to Network:** Output (media/data/intermediate data) from UE tasks sent to MF
- **Network Inference:** MF executes tasks designated for network execution
- **Result Delivery:** MF sends output (results or intermediate data for further UE processing) to UE
- **Optional Further UE Processing:** UE may execute additional tasks as part of selected AI task(s)

### Dynamic Task Reselection (Step 23)
- User/UE may reselect AI tasks during session using AI task metadata from step 9
- On reselection, flow returns to step 12 (split configuration decision)

## Key Technical Features

### Metadata Framework
- **Application metadata** includes both generic and AI-specific information
- **AI task metadata** (task manifest) provides detailed information on:
  - Task descriptions
  - Execution endpoint options
  - Requirements for split execution

### Flexibility in Execution Distribution
- UE determines split configuration based on capabilities and metadata
- Network validates requirements and may reallocate MF resources
- Dynamic task reselection supported during active session

### Model Distribution Options
- Multiple sources for AI model retrieval (DCAR via DCSF or DC AS)
- Models distributed to both UE and network as needed for split execution

### Media/Data Flow Management
- SDP re-negotiation ensures proper association of data flows with tasks
- Support for intermediate data exchange between UE and network for multi-stage inference pipelines