AI models are getting suspiciously good at 'solving' picture puzzles even when you hide the picture, which means they're just getting better at guessing the answer.
April 6, 2026
Original Paper
Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models
arXiv · 2604.03179
The Takeaway
It exposes a major flaw in how we measure AI progress, proving that better results can sometimes come from sophisticated hallucinations rather than real visual understanding. This suggests we may be training models to lie more convincingly instead of seeing more clearly.
From the abstract
The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to enhance their visual reasoning capabilities. Although many studies have reported improved performance, it remains unclear whether RL training truly enables models to learn from visual information. In this work, we propose the Hallucination-as-Cue Framework, an analytical framework designed to investigate the effects of R