Parameter-efficient fine-tuning lets MLLMs serve as effective retrievers for natural-language-guided cross-view geo-localization, beating dual-encoder baselines on GeoText-1652 and CVG-Text while using far fewer trainable parameters.
Toolformer: Lan- guage models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551,
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Argos is an agentic verifier that adaptively picks scoring functions to evaluate accuracy, localization, and reasoning quality, enabling stronger multimodal RL training for AI agents.
citing papers explorer
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Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Parameter-efficient fine-tuning lets MLLMs serve as effective retrievers for natural-language-guided cross-view geo-localization, beating dual-encoder baselines on GeoText-1652 and CVG-Text while using far fewer trainable parameters.
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Multimodal Reinforcement Learning with Adaptive Verifier for AI Agents
Argos is an agentic verifier that adaptively picks scoring functions to evaluate accuracy, localization, and reasoning quality, enabling stronger multimodal RL training for AI agents.