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