arXiv:2607.00434v1 Announce Type: cross Abstract: Vision-language models (VLMs) have become a paradigm for multimodal learning, yet remain unstable due to object hallucination, weak visual grounding, and catastrophic forgetting after full-parameter instruction tuning. We claim these failures result from a lack of explicit control over visual representation learning during the standard next-token prediction objective. As a result, visual embeddings thus become passively optimized and prone to injecting redundant or spurious signals. To counter this, we introduce Information-Regularized Attentio
This paper addresses critical, recognized stability issues in vision-language models, indicating a maturing field where fundamental limitations are being tackled rigorously.
Improved stability and control in vision-language models are crucial for their broader deployment in sensitive applications, reducing risks of error and increasing reliability.
This research introduces methods to make visual embeddings less passive and more explicitly controlled, potentially leading to more robust and less 'hallucinatory' multimodal AI.
- · AI developers
- · Multimodal AI applications
- · Companies relying on VLM for visual-centric reasoning
- · Competitors with less stable VLM architectures
- · Applications plagued by object hallucination
VLMs become more reliable for real-world tasks, expanding their immediate application scope.
Increased trust and adoption of advanced AI in domains requiring high visual accuracy, such as robotics or autonomous systems.
The acceleration of AI agents with sophisticated multimodal understanding, leading to more capable and less error-prone autonomous systems in complex environments.
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Read at arXiv cs.LG