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Dont Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination
Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit +5
Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterpri…
Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation
Prafulla Kumar Choubey, Xin Su, Man Luo +9
Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economic…
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…
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…
Unanswerability Evaluation for Retrieval Augmented Generation
Xiangyu Peng, Prafulla Kumar Choubey, Caiming Xiong +1
Existing evaluation frameworks for retrieval-augmented generation (RAG) systems focus on answerable queries, but they overlook the importance of appropriately rejecting unanswerabl…
ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement
Xiangyu Peng, Congying Xia, Xinyi Yang +3
Post-training Large Language Models (LLMs) with explicit reasoning trajectories can enhance their reasoning abilities. However, acquiring such high-quality trajectory data typicall…