Infection-Reasoner, a 4B VLM, reaches 86.8% accuracy on wound infection classification while producing rationales rated mostly correct by experts, via GPT-5.1 distillation followed by reinforcement learning.
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Controlled experiments show PLM-GNN hybrids improve code tasks over GNN-only baselines, with PLM source having larger impact than GNN backbone.
CARE fine-tunes LLMs on counselor-validated crisis dialogues to produce responses with stronger semantic and strategic alignment to expert standards than general-purpose models in Hebrew and Arabic.
citing papers explorer
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Infection-Reasoner: A Compact Vision-Language Model for Wound Infection Classification with Evidence-Grounded Clinical Reasoning
Infection-Reasoner, a 4B VLM, reaches 86.8% accuracy on wound infection classification while producing rationales rated mostly correct by experts, via GPT-5.1 distillation followed by reinforcement learning.
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PLMGH: What Matters in PLM-GNN Hybrids for Code Classification and Vulnerability Detection
Controlled experiments show PLM-GNN hybrids improve code tasks over GNN-only baselines, with PLM source having larger impact than GNN backbone.
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CARE: Counselor-Aligned Response Engine for Online Mental-Health Support
CARE fine-tunes LLMs on counselor-validated crisis dialogues to produce responses with stronger semantic and strategic alignment to expert standards than general-purpose models in Hebrew and Arabic.