arXiv:2606.16682v1 Announce Type: cross Abstract: When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge. We show that Evaluator Preference Collapse (EPC) is dramatically amplified in multimodal settings. Using GPT-4o to evaluate DeepSeek-chat across text and visual tasks, we find that a single strategy (step_by_step) absorbs 48.4% of all weight -- 3.2x the collapse observed in text-only self-evaluation -- while three visual-domain strategies receive only 9.1% combined weight. We then demonstrate a novel phenomenon we term cross-modal cont
The proliferation of multimodal AI systems and agentic architectures highlights the urgent need to understand their intrinsic biases and failure modes, especially as they move toward self-improvement loops.
Evaluator Preference Collapse in multimodal AI agents indicates a fundamental challenge in achieving robust and diverse self-correction, potentially leading to monocultures of thought and reduced utility across complex tasks.
The understanding that multimodal AI self-evaluation significantly amplifies bias and cross-modal contagion, demanding new approaches to agent design and evaluation methodologies.
- · AI alignment researchers
- · AI safety tooling developers
- · Organizations developing diverse evaluation frameworks
- · Developers relying solely on self-evaluation for AI agent improvement
- · AI agents that fail to develop diverse internal evaluation strategies
- · Applications requiring broad strategy exploration
Multimodal AI agents develop highly biased evaluation preferences, privileging certain strategies over others.
This bias leads to reduced performance diversity and potential brittleness in real-world applications requiring nuanced, multimodal reasoning.
It could accelerate the development of external verification mechanisms and human-in-the-loop systems to counteract intrinsic AI self-evaluation biases.
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