activity
20232026
most citedImproved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates

10 citations · 29 across the 43 of their papers we have counts for

collaborators
Showing 2026Show all

10 papers · 1 filter

cs.AI2026

Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment

Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour +2

Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for…

cs.AI2026

Fresh Memory, Stale Plans: Dependency-Scoped Validation for Distributed LLM-Agent Memory

Evan Chen, Shiqiang Wang, Christopher G. Brinton

Distributed LLM-agent teams can read the latest shared facts and still act on an obsolete plan. A planner may derive an action from requirement , another agent may commit $r_4…

cs.AI2026

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…

cs.CR2026

Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme

Xiaoyan Ma, Seohyun Lee, Taejoon Kim +1

Over-the-air federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels, but this same property also creates a crit…

cs.AI2026

Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs

Wenzhi Fang, Liangqi Yuan, Guangchen Lan +2

Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot r…

cs.LG2026

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…