arXiv:2606.31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image. In this paper, we identify an internal signature of hallucination: progressive degradation of text-to-image cross-attention during generation, leading to specific failure patterns like unfocused or biased attention. Existing mitigation strategies are largely outcome-driven and do not explicitly target this failure mode. To address this problem, we propose ADAPT (Attention Dynamics Alignment with Preference
The rapid advancement and adoption of MLLMs create an urgent need to address fundamental issues like hallucination, which directly impacts their reliability and real-world utility.
Improving the faithfulness of MLLMs is crucial for their integration into critical applications, reducing the risks associated with unreliable AI outputs and enhancing user trust.
This research introduces a novel, internal mechanism to combat MLLM hallucination, moving beyond outcome-driven corrections to address the root cause of generative inconsistencies.
- · AI developers
- · MLLM users
- · Automation software providers
- · Providers of unreliable AI solutions
- · Manual content verification services
More reliable MLLMs will accelerate their deployment in higher-stakes white-collar and creative tasks.
Increased trust in MLLMs could lead to significant collapse of certain human-driven content creation and verification workflows.
The ability to produce more faithful multimodal outputs might accelerate the development of more complex and autonomous AI agents.
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Read at arXiv cs.CL