Vidit Bansal — accepted AI-conference papers

2026

Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent

Wenliang Zhong, Rob Barton, Lucas Goncalves, Kushal Kumar, Feng Jiang, Hehuan Ma +4

CVPR 2026

Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the first universal framework that bridges these gaps through textual guidelines. To incorporate complex guidelines without

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2025

How to Talk to Language Models: Serialization Strategies for Structured Entity Matching

Haoteng Yin, Jinha Kim, Prashant Mathur, Krishanu Sarker, Vidit Bansal

NAACL 2025findings

Entity matching (EM), which identifies whether two data records refer to the same real-world entity, is crucial for knowledge base construction and enhancing data-driven AI systems. Recent advances in language models (LMs) have shown great potential in resolving entities with rich textual attributes…

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