aiqbench analysis
This research introduces a novel ensemble architecture combining a fine-tuned 4B-parameter Vision-Language Model with a zero-shot 400B judge to detect character-level hallucinations in visual outputs. By leveraging synthetic data generated by large models and specific two-token hidden state features, the system achieves top-tier performance across multiple languages on the SHROOM-Visions 2026 shared task. The approach highlights practical strategies for improving reliability in multimodal AI systems without relying solely on massive standalone models for every inference step.
Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection
arXiv:2609.10244v1 Announce Type: new Abstract: We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small ($4$B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states, and is ensembled with a $\sim$400B zero-shot VLM judge at prediction time. Both components see off-the-shelf OCR of any visible in-image text. We use synthetic hallucination data generated by the large model as a source of ensemble diversity, and use validation to select feature layer, training data and OCR grounding. Our official entry reaches mean Cor $0.487$ / Cor-lbl $0.387$ on the hidden test set, placing $6$th/$28$ (EN), $6$th/$21$ (FR), $8$th/$21$ (IT) and $7$th/$22$ (ZH) on the task's primary Cor-lbl metric.
Abstract:We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small ($4$B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states, and is ensembled with a $\sim$400B zero-shot VLM judge at prediction time. Both components see off-the-shelf OCR of any visible in-image text. We use synthetic hallucination data generated by the large model as a source of ensemble diversity, and use validation to select feature layer, training data and OCR grounding. Our official entry reaches mean Cor $0.487$ / Cor-lbl $0.387$ on the hidden test set, placing $6$th/$28$ (EN), $6$th/$21$ (FR), $8$th/$21$ (IT) and $7$th/$22$ (ZH) on the task's primary Cor-lbl metric.
Computation and Language (cs.CL)
arXiv:2609.10244 [cs.CL]
(or arXiv:2609.10244v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.10244 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 210 words
