7 papers
Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments
Alexandra Neagu, Jeffrey T. H. Wong, Marcus Messer +2
A central pedagogical value evaluated in AI tutor benchmarks is scaffolding: guiding students through graduated steps toward a solution. Alignment and evaluation methods for embedd…
Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling
Jeffrey T. H. Wong, Zixi Zhang, Junyi Liu +1
Existing Multi-Agent Systems (MAS) typically rely on homogeneous model configurations, failing to exploit the diverse expertise inherent in different post-trained architectures. We…
On the Existence and Behavior of Secondary Attention Sinks
Jeffrey T. H. Wong, Cheng Zhang, Louis Mahon +3
Attention sinks are tokens, often the beginning-of-sequence (BOS) token, that receive disproportionately high attention despite limited semantic relevance. In this work, we identif…
Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
Haoran Wu, Can Xiao, Jiayi Nie +15
LLMs now form the backbone of AI agents across a diverse range of applications, including tool use, command-line interfaces, and web or computer interaction. These agentic LLM infe…
A3 : an Analytical Low-Rank Approximation Framework for Attention
Jeffrey T. H. Wong, Cheng Zhang, Xinye Cao +5
Large language models have demonstrated remarkable performance; however, their massive parameter counts make deployment highly expensive. Low-rank approximation offers a promising…
ARIES: Autonomous Reasoning with LLMs on Interactive Thought Graph Environments
Pedro Gimenes, Zeyu Cao, Jeffrey Wong +1
Recent research has shown that LLM performance on reasoning tasks can be enhanced by scaling test-time compute. One promising approach, particularly with decomposable problems, inv…