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Adapting pretrained models on-the-fly to ever-changing data distributions could redefine how we deploy AI in dynamic environments.
TCR-SRIM reveals that relying on predicted structures can obscure critical interaction patterns, challenging the efficacy of popular structure prediction models in TCR-epitope learning.
MLLMs can significantly reduce hallucinations and improve reasoning by shifting focus from linguistic shortcuts to causal visual grounding through VIGIL.
A stealthy skill injection method that achieves an 89.3% success rate while evading detection in LLM agents reveals critical vulnerabilities in current safety mechanisms.
Boost a VLM's rare-object reasoning without any finetuning by injecting learned embeddings from vision foundation models and synonym-augmented text.