Solve the hCaptcha challenge with multimodal large language model
hCaptcha Challenger harnesses the spatial chain-of-thought (SCoT) reasoning capabilities of multimodal large language models (MLLMs) to construct an agentic workflow framework. This architecture empowers autonomous agents to perform zero-shot adaptation on diverse spatial-visual tasks through dynamic problem-solving workflows, eliminating the requirement for task-specific fine-tuning or additional training parameters. 0 comments on Hacker News.
hCaptcha Challenger harnesses the spatial chain-of-thought (SCoT) reasoning capabilities of multimodal large language models (MLLMs) to construct an agentic workflow framework. This architecture empowers autonomous agents to perform zero-shot adaptation on diverse spatial-visual tasks through dynamic problem-solving workflows, eliminating the requirement for task-specific fine-tuning or additional training parameters.
hCaptcha Challenger harnesses the spatial chain-of-thought (SCoT) reasoning capabilities of multimodal large language models (MLLMs) to construct an agentic workflow framework. This architecture empowers autonomous agents to perform zero-shot adaptation on diverse spatial-visual tasks through dynamic problem-solving workflows, eliminating the requirement for task-specific fine-tuning or additional training parameters. 0 comments on Hacker News.
hCaptcha Challenger harnesses the spatial chain-of-thought (SCoT) reasoning capabilities of multimodal large language models (MLLMs) to construct an agentic workflow framework. This architecture empowers autonomous agents to perform zero-shot adaptation on diverse spatial-visual tasks through dynamic problem-solving workflows, eliminating the requirement for task-specific fine-tuning or additional training parameters.
Hacker News story: Solve the hCaptcha challenge with multimodal large language model
Reviewed by Tha Kur
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April 06, 2025
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