CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning

2026-05-28

AI Summary

Existing multimodal retrieval-augmented generation (MMRAG) approaches face significant challenges, including high retrieval noise, cross-modal semantic misalignment, lack of adaptive reasoning, and incoherent generation. To address these issues, this work proposes the CogniVerse framework, which uniquely integrates a cognition-inspired reflection mechanism, Riemannian manifold alignment, spectral graph-optimized knowledge graphs, and an optimal transport-based loss function. This integration enables dynamic retrieval filtering, precise cross-modal alignment, and globally consistent hierarchical generation. Experimental results demonstrate that CogniVerse substantially outperforms current state-of-the-art models in both question-answering accuracy and generation coherence, while simultaneously achieving reduced retrieval latency.

Abstract

Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models in knowledge-intensive question answering by integrating external visual, textual, and structural knowledge. However, existing MMRAG frameworks suffer from critical limitations, including noisy and irrelevant retrieval, cross-modal semantic misalignment, lack of adaptive reasoning, and incoherent generation across local and global contexts. We introduce \textbf{CogniVerse}, a novel MMRAG framework that addresses these challenges through a cognitive-inspired, mathematically rigorous approach. Drawing from human-like reasoning, CogniVerse integrates three synergistic components: (1) a Cognitive Reflection Module that dynamically assesses retrieval necessity and filters relevant multi-modal content, reducing noise and computational overhead; (2) a Multi-modal Retrieval Module that aligns embeddings in a Riemannian manifold using information geometry and refines knowledge graphs via spectral graph theory, ensuring precise and coherent retrieval; and (3) a Hierarchical Generation Module that employs an optimal transport-based loss to balance token-level accuracy and global semantic coherence. Extensive experiments demonstrate that CogniVerse significantly outperforms state-of-the-art systems in both accuracy and coherence, while reducing retrieval latency.

Problem

• Multi-modal Retrieval-Augmented Generation
• semantic misalignment
• noisy retrieval
• adaptive reasoning
• coherent generation

Innovation

• Cognitive Reflection
• Information Geometry
• Riemannian Manifold
• Optimal Transport
• Spectral Graph Theory