activity
20242026
collaborators

7 papers

cs.AI2026

EASformer: Environment-Action-State Alignment for Industrial Generative Ranking

Bohao Li, DingLun Cao, Dinglun Cao +12

Modern industrial ranking systems increasingly adopt generative recommendation (GR) paradigms, in which user behavior sequences and non-sequential (NS) features are tokenized and j…

cs.CL2026

PrimeSeeker: Capability-Oriented Supervision for Deep Search Agents

Linzhi Peng, Hanting Chen, Heng Chang +3

Large language model search agents are often trained with synthetic questions whose difficulty is increased through larger evidence graphs, additional hops, and longer trajectories…

cs.CV2026

Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings

Yan Gong, Bohao Li, Bowen Du +1

Computer-Aided Design (CAD) floor plan drawings contain both graphical primitives and textual annotations, which provide complementary geometric and semantic cues for intelligent d…

cs.LG2026

A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs

Bohao Li, Tao Zou, Junchen Ye +2

Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk prediction. However, due to diverse cli…

cs.CV2025

Text-Enhanced Panoptic Symbol Spotting in CAD Drawings

Xianlin Liu, Yan Gong, Bohao Li +4

With the widespread adoption of Computer-Aided Design(CAD) drawings in engineering, architecture, and industrial design, the ability to accurately interpret and analyze these drawi…

cs.LG2024

Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction

Ke Cheng, Linzhi Peng, Junchen Ye +2

Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repea…

cs.LG2024

DyGKT: Dynamic Graph Learning for Knowledge Tracing

Ke Cheng, Linzhi Peng, Pengyang Wang +3

Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length le…