1 citations · 1 across the 11 of their papers we have counts for
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UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
Xiangyu Peng, Can Qin, Zeyuan Chen +3
Multimodal retrieval-augmented Generation (MM-RAG) is a key approach for applying large language models (LLMs) and agents to real-world knowledge bases, yet current evaluations are…
Strefer: Empowering Video LLMs with Space-Time Referring and Reasoning via Synthetic Instruction Data
Honglu Zhou, Xiangyu Peng, Shrikant Kendre +4
Next-generation AI companions must go beyond general video understanding to resolve spatial and temporal references in dynamic, real-world environments. Existing Video Large Langua…
Benchmarking Deep Search over Heterogeneous Enterprise Data
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath +3
We present a new benchmark for evaluating Deep Search--a realistic and complex form of retrieval-augmented generation (RAG) that requires source-aware, multi-hop reasoning over div…
BingoGuard: LLM Content Moderation Tools with Risk Levels
Fan Yin, Philippe Laban, Xiangyu Peng +7
Malicious content generated by large language models (LLMs) can pose varying degrees of harm. Although existing LLM-based moderators can detect harmful content, they struggle to as…
Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath +3
Automated service agents require well-structured workflows to provide consistent and accurate responses to customer queries. However, these workflows are often undocumented, and th…