2 citations · 2 across the 12 of their papers we have counts for
8 papers · 1 filter
Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang +1
Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics. H…
Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +1
Multi-agent systems built on large language models (LLMs) are largely homogeneous, as their agents behave alike even across distinct LLMs. We show that when such agents act concurr…
Fresh Memory, Stale Plans: Derivation Currency for Distributed LLM-Agent Memory
Evan Chen, Shiqiang Wang, Christopher G. Brinton
A large language model (LLM) agent that inherits a plan through shared memory can hold the latest requirement yet act on a plan derived from an older one: fresh memory, stale plan.…
Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating
Evan Chen, Shiqiang Wang, Kevin S Chan +2
Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained routers or collaboration-aware f…
Agentic Performance at the Edge: Insights from Benchmarking
Shiqiang Wang, Herbert Woisetschläger
Agentic artificial intelligence (AI) is a natural fit for Internet of Things (IoT) and edge systems, but edge deployments are often constrained to models around 8 billion parameter…
PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +1
Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents preserve privacy at the cost of over…
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2
Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a gro…
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
Evan Chen, Wenzhi Fang, Shiqiang Wang +1
Locally deployed Small Language Models (SLMs) must continually support diverse tasks under strict memory and computation constraints, making selective reliance on cloud Large Langu…