Search papers, labs, and topics across Lattice.
Nanjing University of Science and Technology
5
0
11
Research ideas generated through a novel multi-agent system show a significant boost in diversity and novelty, outperforming traditional LLM methods.
Generalist foundation models beat specialized GUI agents at e-commerce risk management, suggesting scale trumps zero-shot grounding for complex, real-world web tasks.
Even state-of-the-art AI-generated image detectors struggle when images are cropped, resized, or compressed, revealing a critical gap in real-world robustness.
LLMs can now explore knowledge graphs on their own, discovering better reasoning paths and outperforming even closed-source models on question answering.
By aligning latent representations with multiple visual foundation models, FRAPPE offers a more scalable and data-efficient way to imbue generalist robotic policies with robust world-awareness.
