Talks EMNLP2025 Keynote: Heng Ji EMNLP2025 Eo: Expert Generalization in MOE EMNLP2025 Wu: Zero Shot Graph Learning EMNLP2025: MUSE, MCTS Driven Red Teaming Posters EMNLP2025 Wednesday Morning Posters EMNLP2025 Friday Afternoon Posters EMNLP2025 Extra Things Takes although parsing maybe dead for natural language, structure helps parse scientific information (i.e. drugs, molecules, proteins, etc.) two idea: 1) how to formalize approach mathematically 2) what can LMs do that humans can’t do? information-rich statefulness + constraints for pruning space is the unlock for ability to build on previous results; i.e. “critical thinking” Tasks to Do EMNLP2025 Fan: medium is not the message: I wonder if we can remove keyboard based signals from BM25 using this method EMNLP2025 Xu: tree of prompting: a bunch of multi-hop retrieval datasets to benchmark for RAG-DOLL EMNLP2025 Bai: understanding and leveraging expert specialization of context faithfulness: a good set of retrieval benchmarks Tasks Can Do EMNLP2025 Keynote: Heng Ji: “protein LLM requires early exit to capture dynamical Beauvoir”; what if we Mixture of Depth a protein LM? EMNLP2025 Hutson: measuring informative of open and questions: formalize this as a rho– POMDP , or use actual value of information measures with Belman backup EMNLP2025 Karamanolakis: interactive machine teaching: use MCTS UCB to pick the next set of constitutions to optimize for EMNLP2025 Yu: Long-Context LM Fail in Basic Retrieval: I wonder how thoughtbubbles do on the dataset EMNLP2025 Bai: understanding and leveraging expert specialization of context faithfulness: could be interesting using the same freeze/clamping technique for cultural work EMNLP2025 Vasu: literature grounded hypothesis generation: maybe could use its same hypothesis generation pipeline for RAG EMNLP2025Li: enhancing RAG RESPONSE evaluator: maybe could be useful to use to evaluate edge rewards for RAGDOLL