From Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models

arXiv:2610.17706 · cs.CV, cs.AI · Submitted 2026-10-13 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-13

Updated: 2026-10-13

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Hallucination — 1× in this paper · explained in 4 episodes
• Hallucination Mitigation — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice
• Mechanistic Circuit Identification for Controllable Data Generation
• AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
• Hallucinations Leave a Grounding Signature:Verifier-Guided Decoding for Selective Object Correction

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