Search papers, labs, and topics across Lattice.
University of California, Santa Cruz
5
0
6
0
Agents are great at setting up tasks but falter at validating and submitting, revealing a critical weakness in autonomous medical research workflows.
Decoupling noise diffusion from residual learning allows for superior domain harmonization, leading to robust image-to-image translation with minimal data.
Mid-tier LLMs outperform their stronger counterparts in harness self-evolution, challenging assumptions about model capability and adaptability.
Stop hand-feeding your LLM clinical data: ClinSeekAgent actively seeks and synthesizes multimodal evidence, boosting Claude Opus's performance by 15% on multimodal tasks.
VLMs struggle more with *seeing* than *thinking*, and targeted pre-training on visual perception alone unlocks surprisingly large gains in downstream reasoning.
