Junfeng Ren
Spatial Intelligence, Efficient Transformer Inference, and LLM Agents
M.S. Student, SUSTech
junfengren3253@gmail.com
I am a master's student in Electronic Information at the Southern University of Science and Technology (SUSTech). My research spans spatial intelligence and world modeling, efficient Transformer inference, and LLM agent systems.
My work includes semantic occupancy prediction and collaborative perception for autonomous and embodied systems, training-free token merging and attention-level redundancy reduction for efficient Transformers, and reliable adaptation and context-efficient tool use for LLM agents.
Research Directions
Spatial Intelligence & World Models
Learning structured 3D representations for perception, collaboration, and prediction in autonomous and embodied systems.
Efficient Transformer Inference
Designing training-free mechanisms to reduce redundant token and attention computation in Transformers.
LLM Agent Systems
Studying reliable adaptation, cross-agent knowledge transfer, and context-efficient tool use in LLM agent systems.
Selected Research
Spatial Intelligence & World Models
Learning to Merge Tokens for Communication-Efficient Collaborative Occupancy Prediction
Collaborative semantic occupancy prediction under limited bandwidth, using receiver-driven communication and adaptive token merging to exchange compact, task-relevant scene information.
Collaborative 4D Occupancy World Models
Modeling how shared 3D occupancy evolves over time by integrating multi-agent observations, temporal context, and future occupancy prediction.
Efficient Transformer Inference
SCMerge — Content-Spatial Consistent Token Merging
Training-free token merging that combines semantic similarity with local spatial consistency to reduce redundant visual tokens while preserving scene structure.
QKV-level Token Merging
Exploring token merging within the attention pipeline to reduce redundant QKV computation beyond conventional representation-level token reduction.
LLM Agent Systems
FedHarness-RB
Reliable cross-agent transfer of executable harness improvements, using target-side validation and selective deployment to reduce negative transfer across heterogeneous LLM agents.
CriticShift
Reliability-aware tool-output selection that preserves task-critical evidence while reducing unnecessary context for tool-using LLM agents.
Research Experience
National University of Singapore
Department of Computer Science, School of Computing
Research on reliable and efficient LLM agent systems, with a focus on cross-agent harness transfer, context-efficient tool use, and reliability evaluation.
Southern University of Science and Technology
Guangdong Provincial Key Laboratory of Advanced Wireless Communication Technologies
Research on spatial intelligence and efficient Transformer inference, including collaborative semantic occupancy prediction, communication-efficient perception, world modeling, and training-free token merging.
Shandong University of Science and Technology
IoT Engineering Laboratory
Worked on resource-constrained embedded and IoT systems, including real-time scheduling, edge computing, and robotic system development.