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IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Large Vision-Language Models (LVLMs) have achieved strong multimodal performance, yet ensuring the factual correctness of generated content remains challenging. Existing methods that provide statistical guarantees on factuality typically rely on external verifiers or generation-time confidence signals, which introduce auxiliary dependencies or often fail for confident but incorrect outputs. We argue that reliable factuality control can instead be achieved through introspective signals derived from the model itself. We introduce IntroConformal, a training-free Conformal Risk Control (CRC) frame

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