OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences | ScienceToStartup | ScienceToStartup

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References (21)

[1]

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

2026Xingjun Ma, Yixu Wang et al.

[2]

USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models

2025Baolin Zheng, Guanlin Chen et al.

[3]

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration

Founder's Pitch

"Introducing OOD-MMSafe, a benchmark and framework for enhancing safety in Multimodal Large Language Models by focusing on consequence-driven safety."

MLLM Safety•Score: 7•View PDF ↗

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OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences

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References (21)

Founder's Pitch

"Introducing OOD-MMSafe, a benchmark and framework for enhancing safety in Multimodal Large Language Models by focusing on consequence-driven safety."

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OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences

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References (21)

Founder's Pitch

"Introducing OOD-MMSafe, a benchmark and framework for enhancing safety in Multimodal Large Language Models by focusing on consequence-driven safety."

Commercial Viability Breakdown

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