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Renmin University of China
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Explicit evidence graphs in VeriGraph enable LLMs to achieve 87.61% claim grounding, transforming how we verify AI-generated conclusions.
Arbor's innovative approach to autonomous research enables a cumulative learning process that outperforms existing models by over 2.5 times in real-world tasks.
Agent-World reveals that self-evolving environments can dramatically boost agent performance, outperforming established models by leveraging dynamic task synthesis.
Current multimodal models are stuck in bi-modal interactions, but OmniGAIA and OmniAtlas offer a path towards truly omni-modal AI assistants capable of reasoning and tool use across video, audio, and images.
