4 papers
When Is a Task Vector Enough? An Empirical Theory of Implicit Multimodal ICL
Jiaqian Li
Implicit multimodal in-context learning compresses demonstrations into internal interventions, ranging from static task vectors to query-conditioned transformations and attention r…
HACO: Hedged Agent Computing for Reliable LLM Systems
Enhan Li, Hongyang Du
As large language model (LLM) agents move from isolated prompting to longhorizon workflows, failures increasingly arise at the role-to-instance binding boundary, where task-specifi…
JAUNT: Joint Alignment of User Intent and Network State for QoE-centric LLM Tool Routing
Enhan Li, Hongyang Du
Large Language Models (LLMs) increasingly rely on emerging protocols such as the Model Context Protocol (MCP) to invoke external tools and services. However, current tool routing m…
NetMCP: Network-Aware Model Context Protocol Platform for LLM Capability Extension
Enhan Li, Hongyang Du, Kaibin Huang
Large Language Models (LLMs) remain static in functionality after training, and extending their capabilities requires integration with external data, computation, and services. The…