4 papers
AdaSTORM: Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Temporal Multi-Agent Collaboration
Bing Hao, Ruijie Wang, Haodong Qian +5
Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of n…
SPOT: Span-level Pause-of-Thought for Efficient and Interpretable Latent Reasoning in Large Language Models
Yunlong Chu, Minglai Shao, Yuhang Liu +4
Explicit Chain-of-Thought improves the reasoning performance of large language models but often incurs high inference cost due to verbose token-level traces. While recent approache…
RouteGoT: Node-Adaptive Routing for Cost-Efficient Graph of Thoughts Reasoning
Yuhang Liu, Ruijie Wang, Yunlong Chu +4
Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such a…
Information Loss in LLMs' Multilingual Translation: The Role of Training Data, Language Proximity, and Language Family
Yumeng Lin, Xufeng Duan, David Haslett +2
Large language models have achieved impressive progress in multilingual translation, yet they continue to face challenges with certain language pairs-particularly those with limite…