Before the Token Commits: Trajectory-Level Benchmarking of Visual Hallucinations in Diffusion VLMs
cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- DLM-SWAI: Steering Diffusion Language Models Before They Unmask
- Qwen2.5-VL Technical Report
- TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models
- MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
- TDGNet: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs
- Diffusion Language Models Know the Answer Before Decoding
- LaViDa: A Large Diffusion Language Model for Multimodal Understanding
- Seeing to Ground: Visual Attention for Hallucination-Resilient MDLLMs
- Large Language Diffusion Models
- DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning
- Activation Steering for Masked Diffusion Language Models
- AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
- Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
- MMaDA: Multimodal Large Diffusion Language Models
- Woodpecker: Hallucination Correction for Multimodal Large Language Models
- LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning
- Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding
- HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models
- Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models
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