6 papers
SMaRT: Select, Mix, and ReinvenT -- A Strategy Fusion Framework for LLM-Driven Reasoning and Planning
Nikhil Verma, Manasa Bharadwaj, Wonjun Jang +4
Large Language Models (LLMs) have redefined complex task automation with exceptional generalization capabilities. Despite these advancements, state-of-the-art methods rely on singl…
Cross-Attention Speculative Decoding
Wei Zhong, Manasa Bharadwaj, Yixiao Wang +2
Speculative decoding (SD) is a widely adopted approach for accelerating inference in large language models (LLMs), particularly when the draft and target models are well aligned. H…
BuddyMoE: Exploiting Expert Redundancy to Accelerate Memory-Constrained Mixture-of-Experts Inference
Yun Wang, Lingyun Yang, Senhao Yu +5
Mixture-of-Experts (MoE) architectures scale language models by activating only a subset of specialized expert networks for each input token, thereby reducing the number of floatin…
MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation
Zichen Zhu, Hao Tang, Yansi Li +13
Existing Multimodal Large Language Model (MLLM)-based agents face significant challenges in handling complex GUI (Graphical User Interface) interactions on devices. These challenge…
Generalization Capability for Imitation Learning
Yixiao Wang
Imitation learning holds the promise of equipping robots with versatile skills by learning from expert demonstrations. However, policies trained on finite datasets often struggle t…
UoB-NLP at SemEval-2025 Task 11: Leveraging Adapters for Multilingual and Cross-Lingual Emotion Detection
Frances Laureano De Leon, Yixiao Wang, Yue Feng +1
Emotion detection in natural language processing is a challenging task due to the complexity of human emotions and linguistic diversity. While significant progress has been made in…