From the 1 of 9 linked papers with an AI index.
9 papers
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs
Zijie Liu, Jie Peng, Jinhao Duan +7
The paper proposes a training‑free method that replicates heavily used experts and quantizes less important ones to rebalance workload in sparse mixture‑of‑experts large language m…
LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems
Zhiyuan Wang, Aniri, Tianlong Chen +4
Foundation models often generate unreliable answers, while heuristic uncertainty estimators fail to fully distinguish correct from incorrect outputs, causing users to accept errone…
Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos
Yixuan Shen, Peng He, Honglu Liu +6
K-12 science classrooms are rich sites of inquiry where students coordinate phenomena, evidence, and explanatory models through discourse; yet, the multimodal complexity of these i…
AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Song Wang, Zhen Tan, Zihan Chen +3
Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existin…
SConU: Selective Conformal Uncertainty in Large Language Models
Zhiyuan Wang, Qingni Wang, Yue Zhang +4
As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies hav…
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Zhiyuan Wang, Jinhao Duan, Qingni Wang +4
Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approach…