1 citations · 2 across the 5 of their papers we have counts for
9 papers
Who Verifies the Verifiers? Accountable Semantic Consensus for AI-Mediated Web Information Verification
Yanna Jiang, Mingyuan Li, Xu Wang +1
Large language models (LLMs) increasingly decide whether Web evidence supports online claims, often by voting over several assessors whose errors are presumed independent and benig…
Diversity Combining for Multi-Path LLM Reasoning
Guangsheng Yu, Litianyi Zhang, Qin Wang +5
Multi-path reasoning methods such as self-consistency (SC) sample reasoning paths and choose the most frequent answer. However, their gains quickly plateau as increases, an…
MemoryAthena: Adaptive Routing over Latent and Generated Memories
Mingyuan Li, Guangsheng Yu, Juyuan Zhang +4
Learned-memory methods store information in an explicit table and consume it through a separate reader, allowing addressing, storage, and reading to be modified independently. We s…
Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
Mingyuan Li, Guangsheng Yu, Xu Wang +1
Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrie…
OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections
Mingyuan Li, Boyang Huang, Tianqi Jiang +5
Queue overflow, a severe consequence of urban traffic congestion, occurs when vehicle queues exceed intersection capacity, obstructing upstream traffic and triggering cascading gri…
CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning
Mingyuan Li, Chunyu Liu, Zhuojun Li +5
Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effecti…
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities
Mingyuan Li, Jiahao Wang, Bo Du +2
Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has been the research trend for TSC.…
Parallel Unlearning in Inherited Model Networks
Xiao Liu, Mingyuan Li, Guangsheng Yu +3
Unlearning is challenging in generic learning frameworks with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel…
BlockFUL: Enabling Unlearning in Blockchained Federated Learning
Xiao Liu, Mingyuan Li, Xu Wang +5
Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchai…