# Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection

arXiv: https://arxiv.org/abs/2603.25203 · HTML: https://mlsanity.com/paper/2603.25203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 5 of 5)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen3-Omni Technical Report](https://mlsanity.com/paper/2509.17765/) (2025)
- [MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models](https://mlsanity.com/paper/2406.11288/) (2024)
- [DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs](https://mlsanity.com/paper/2601.03559/) (2026)
- [Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking](https://mlsanity.com/paper/2601.02669/) (2026)

## Abstract (arXiv)

Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We present Probabilistic Concept Graph Reasoning (PCGR), an interpretable and evolvable framework that reframes multimodal misinformation detection (MMD) as structured and concept-based reasoning. PCGR follows a build-then-infer paradigm, which first constructs a graph of human-understandable concept nodes, including novel high-level concepts automatically discovered and validated by multimodal large language models (MLLMs), and then applies hierarchical attention over this concept graph to infer claim veracity. This design produces interpretable reasoning chains linking evidence to conclusions. Experiments demonstrate that PCGR achieves state-of-the-art MMD accuracy and robustness to emerging manipulation types, outperforming prior methods in both coarse detection and fine-grained manipulation recognition.