# HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference

arXiv: https://arxiv.org/abs/2512.22137 · HTML: https://mlsanity.com/paper/2512.22137/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2025.

## Built on by (most cited first, 6 of 6)

- [Evolving Idea Graphs with Learnable Edits-and-Commits for Multi-Agent Scientific Ideation](https://mlsanity.com/paper/2605.04922/) (2026)
- [A Task Decomposition and Planning Framework for Efficient LLM Inference in AI-Enabled WiFi-Offload Networks](https://mlsanity.com/paper/2604.21399/) (2026)
- [BANDMAS: Causality-Inspired Semantic Packet Scheduling for Bandwidth-Efficient Multi-Agent Collaboration](https://mlsanity.com/paper/2608.00458/) (2026)
- [Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization](https://mlsanity.com/paper/2610.01017/) (2026)
- [JOVE: Joint Execution and Verification for Resource-Aware LLM Task Graphs](https://mlsanity.com/paper/2610.03296/) (2026)
- [Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution](https://mlsanity.com/paper/2610.17816/) (2026)

## Abstract (arXiv)

Edge-cloud collaborative inference is becoming a practical necessity for LLM-powered edge devices: on-device models often cannot afford the required reasoning capability, while cloud-only inference could be prohibitively costly and slow under strict latency and token/API budgets. However, existing edge-cloud collaboration methods often route per query or fixed steps simply based-on the estimated difficulty. Such coarse and static heuristics overlook subtask dependencies, missing opportunities for parallel execution and budget-adaptive routing. To this end, we propose HybridFlow, a resource-adaptive edge-cloud inference framework that (i) builds a dependency-aware DAG for each query and executes newly unlocked subtasks in parallel, reducing end-to-end latency; (ii) routes each subtask online to the edge or cloud via a learned benefit--cost utility model that dynamically trades accuracy gains against token/API and latency budgets, thereby reducing unnecessary cloud usage while preserving reasoning quality. Across GPQA, MMLU-Pro, AIME24, and LiveBench-Reasoning, HybridFlow improves the cost-accuracy trade-off, reducing latency and cloud API usage while maintaining competitive accuracy against strong structured reasoning baselines.