LLMBind: A Unified Modality-Task Integration Framework

TL;DR

LLMBind is a versatile framework that unifies diverse multimodal tasks using dual-pathway mechanisms and MoE architecture, enabling high performance and extensibility across perception and generation tasks.

Contribution

It introduces LLMBind, a novel, extensible multimodal integration framework with dual-pathway design and MoE routing, addressing task flexibility and modality interference issues.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Demonstrates superior task extensibility and modality disentanglement.

Curates a large multi-turn interactive dataset for visual refinement.

Abstract

Despite recent progress in Multi-Modal Large Language Models (MLLMs), it remains challenging to integrate diverse tasks ranging from pixel-level perception to high-fidelity generation. Existing approaches often suffer from either restricted task extensibility or severe performance degradation due to modality interference. n this paper, we present LLMBind, an extensible framework that unifies multimodal tasks through a dual-pathway mechanism: In-Situ semantic embeddings for localization-sensitive tasks like semantic segmentation and Ex-Situ task-prompts for generation across image, video, and audio modalities. Additionally, we employ a Mixture-of-Experts (MoE) architecture to route task-specific tokens, thereby achieving modality disentanglement and mitigating negative transfer. We also curate a 400k multi-turn interactive dataset focused on iterative visual refinement to enable human-like interaction. Extensive experiments demonstrate that LLMBind achieves excellent performance across multiple perception and generation benchmarks while maintaining superior expandability.