Subvolts take AI models struggle with vision; new research tackles object hallucination and multi-human generation. Dec 9, 2025 - Developers
- Learn about new techniques to improve visual grounding and retrieval in multimodal AI systems.
- Business
- Understand how AI is improving its ability to process visual information for better search and content generation.
This interview covers significant research advancements in multimodal AI and generative models, offering practical solutions to current limitations.
Why Vision Language Models Ignore What They See with Munawar Hayat - #758
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
This TWIML AI Podcast episode features Munawar Hayat discussing new papers on multimodal and generative AI. They cover object hallucination in Vision-Language Models, attention-guided alignment for better visual grounding, and a novel approach to contrastive learning for complex retrieval tasks. The discussion also addresses challenges in rendering multiple human subjects in generative models, introducing the new MultiHuman Testbench.
AI models that understand images and text sometimes ignore what they see, relying on old knowledge instead. This episode explores how researchers are fixing this, making AI better at tasks like searching with combined pictures and text, and improving AI's ability to draw multiple people realistically.
This episode offers insights into current challenges and solutions in Vision-Language Models (VLMs) and generative AI, particularly for on-device deployment. It is for AI researchers and developers working on multimodal systems, image generation, and efficient AI. The topics discussed, like object hallucination and multi-human rendering, are directly relevant to ongoing work at frontier labs like Google Gemini, Meta AI, and OpenAI as they push the boundaries of multimodal understanding and generation.
- HostSam Charrington
- GuestMunawar Hayatresearcher, Qualcomm AI Research
- Guest: Munawar Hayat, researcher at Qualcomm AI Research
- Show: The TWIML AI Podcast
- Episode Title: Why Vision Language Models Ignore What They See with Munawar Hayat - #758
- Length: 58 minutes
- Topics Discussed: Object hallucination in VLMs, attention-guided alignment, generalized contrastive learning, multi-human rendering in generative models, MultiHuman Testbench.
In this episode, we’re joined by Munawar Hayat, researcher at Qualcomm AI Research, to discuss a series of papers presented at NeurIPS 2025 focusing on multimodal and generative AI. We dive into the persistent challenge of object hallucination in Vision-Language Models (VLMs), why models often discard visual information in favor of pre-trained language priors, and how his team used attention-guided alignment to enforce better visual grounding. We also explore a novel approach to generalized contrastive learning designed to solve complex, composed retrieval tasks—such as searching via combined text and image queries—without increasing inference costs. Finally, we cover the difficulties generative models face when rendering multiple human subjects, and the new "MultiHuman Testbench" his team created to measure and mitigate issues like identity leakage and attribute blending. Throughout the discussion, we examine how these innovations align with the need for efficient, on-device AI deployment.
The complete show notes for this episode can be found at https://twimlai.com/go/758.
- Vision-Language Models (VLMs)
- Munawar Hayat
- AI systems that understand and connect images with text.
- object hallucination
- Munawar Hayat
- When AI incorrectly creates or sees objects that aren't there.
- attention-guided alignment
- Munawar Hayat
- A technique to make AI focus on the right visual parts when linking images and text.
- contrastive learning
- Munawar Hayat
- An AI training method that teaches by comparing similar and different examples.
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Questions people ask
- What is object hallucination in Vision-Language Models?
- Object hallucination occurs when Vision-Language Models (VLMs) incorrectly generate or identify objects that are not present in the visual input, often due to reliance on pre-trained language knowledge.
- What is the MultiHuman Testbench?
- The MultiHuman Testbench is a new tool created by Hayat's team to measure and mitigate issues in generative models when rendering multiple human subjects, such as identity leakage and attribute blending.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Sam Charrington.