LLM Orchestration refers to the coordination of one or more large language models within a broader system pipeline, managing how models receive inputs, invoke tools, retrieve information, delegate subtasks, and produce outputs across multi-step workflows. It encompasses routing logic, memory management, prompt chaining, and error handling that enable reliable, goal-directed behavior in agentic and retrieval-augmented generation systems. Research challenges include latency management, context window constraints, and maintaining coherence across complex multi-model interactions.

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LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents

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LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D scene graph, decomposing the scene into a static background and dynamic object nodes. To enable fine-grained editing and realism, it introduces a feedback-driven agentic pipeline. An Orchestrator converts user instructions into executable graphs that coordinate specialized multi-modal agents and tools. An Object Grounding Agent aligns free-form text with target object nodes in the scene graph; a Behavior Editing Agent generates multi-object trajectories from language instructions; and a Behavior Reviewer Agent iteratively reviews and refines the generated trajectories. The edited scene graph is rendered and harmonized using a video diffusion tool, and then further refined by a Video Reviewer Agent to ensure photorealism and appearance alignment. LangDriveCTRL supports both object node editing (removal, insertion, and replacement) and multi-object behavior editing from natural-language instructions. Quantitatively, it achieves nearly higher instruction alignment than the previous SoTA, with superior photorealism, structural preservation, and traffic realism.

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