Hi, I'm Wentao.

Global Credit Assignment via Dynamical Criticality

First author | Peking University

ICML 2026 | Accepted

This ICML 2026 paper studies temporal credit assignment in recurrent, convolutional recurrent, and spiking systems through criticality-driven online local learning with constant activation memory.

Decision-Making Large Language Model for Wireless Communication: A Comprehensive Survey on Key Techniques

Ning Yang, Mingrui Fan, Wentao Wang, Haijun Zhang

Third author (advisor is first author) | Institute of Automation, Chinese Academy of Sciences

IEEE COMST | Published

This survey organizes decision-making LLMs for wireless communication across data construction, architecture adaptation, reasoning control, multi-agent coordination, and open challenges.

Cooperative Edge Caching with Large Language Model in Wireless Networks

Ning Yang, Wentao Wang, Lingtao Ouyang, Haijun Zhang

Second author (student first; advisor is first author) | Institute of Automation, Chinese Academy of Sciences

IEEE TMC | Accepted

We formulate cooperative multi-base-station edge caching as an LLM-native sequential decision problem, build an SFT+GRPO training pipeline, and design an opportunity-aware reward.

MC-TRCM: Observation-Aware Recursive Fusion for Incomplete Mobile and Wearable Mental-Health Feature Views

First author | Dalian University of Technology

ICONIP 2026 | Accepted

Observation-aware recursive fusion for asynchronous and incomplete mobile and wearable feature views, combining shared-private representations with task-conditioned multi-task prediction.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration

Co-first author (advisor listed first) | Institute of Automation, Chinese Academy of Sciences

NeurIPS 2026 | Accepted

LinearARD aligns row-wise dense self-relations between native-RoPE teachers and RoPE-scaled students, restoring long-context performance with an exact linear-memory distillation objective.

Cluster-Aware Attention-Based Deep Reinforcement Learning for Pickup and Delivery Problems

Wentao Wang, Lifeng Han, Guangyu Zou

First author | Dalian University of Technology

Applied Intelligence | Major Revision

CAADRL combines global self-attention, intra-cluster attention, and a dynamic dual-decoder to exploit clustered structure in pickup and delivery problems while reducing inference latency.