Build Multimodal Adversarial Benchmarking Agents
This challenge focuses on developing an advanced agent system to perform rigorous, adversarial evaluation of cutting-edge multimodal Large Language Models (LLMs). Participants will design and implement a multi-agent system capable of generating complex, multimodal prompts (text, image, audio) and then evaluating the responses from different LLMs for accuracy, coherence, and robustness against adversarial attacks. The system will leverage the A2A (Agent-to-Agent) Protocol for seamless communication between an adversarial prompt generation agent and an evaluation agent. DSPy will be used to programmatically optimize the adversarial prompt generation and response analysis, ensuring the benchmark is adaptive and thorough. This challenge emphasizes the practical application of agentic AI for quality assurance and objective comparison of next-generation generative models.
AI Research & Mentorship
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on developing an advanced agent system to perform rigorous, adversarial evaluation of cutting-edge multimodal Large Language Models (LLMs). Participants will design and implement a multi-agent system capable of generating complex, multimodal prompts (text, image, audio) and then evaluating the responses from different LLMs for accuracy, coherence, and robustness against adversarial attacks. The system will leverage the A2A (Agent-to-Agent) Protocol for seamless communication between an adversarial prompt generation agent and an evaluation agent. DSPy will be used to programmatically optimize the adversarial prompt generation and response analysis, ensuring the benchmark is adaptive and thorough. This challenge emphasizes the practical application of agentic AI for quality assurance and objective comparison of next-generation generative models.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master A2A Protocol for secure and efficient agent-to-agent communication in a distributed system.
Implement advanced multimodal prompt generation strategies using LLM's capabilities for diverse input types.
Design DSPy programs to optimize prompt engineering and model reasoning for adversarial test case creation.
Build automated evaluation metrics for assessing multimodal LLM responses for factual consistency, creativity, and safety.
Orchestrate a comparison framework between GPT 5.1 Pro and Gemini 2.5 Pro, focusing on their performance under stress tests.
Develop hybrid reasoning components to analyze and interpret both numerical scores and qualitative aspects of model outputs.
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Operating window
Key dates and the organization behind this challenge.
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