Boci Peng

Orcid: 0000-0002-0984-8740

According to our database1, Boci Peng authored at least 14 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Rethinking Vector Field Learning for Generative Segmentation.
CoRR, March, 2026

Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning.
CoRR, March, 2026

Graph Retrieval-Augmented Generation: A Survey.
ACM Trans. Inf. Syst., February, 2026

Mem-T: Densifying Rewards for Long-Horizon Memory Agents.
CoRR, January, 2026

SegMem-RAG: Adaptive Memory for Retrieval-Augmented Generation in Open-Ended Knowledge Environments.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Memory in the Age of AI Agents.
CoRR, December, 2025

GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs.
Proceedings of the ACM on Web Conference 2025, 2025

DPS: Diverse Prototype Selection for Adaptive In-Context Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024
LLaSA: Large Language and E-Commerce Shopping Assistant.
CoRR, 2024

Multi-view Transformer-Based Network for Prerequisite Learning in Concept Graphs.
Proceedings of the Semantic Web - ISWC 2024, 2024

Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

A Diffusion Model with User Preference Guidance for Recommendation.
Proceedings of the Database Systems for Advanced Applications, 2024

2021
Prerequisite Learning with Pre-trained Language and Graph Embedding Models.
Proceedings of the Natural Language Processing and Chinese Computing, 2021


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