5 papers
MAPGD: Multi-Agent Prompt Gradient Descent for Collaborative Prompt Optimization
Yichen Han, Yuhang Han, Siteng Huang +7
Prompt engineering is crucial for fully leveraging large language models (LLMs), yet most existing optimization methods follow a single trajectory, resulting in limited adaptabilit…
THOR: Transformer Heuristics for On-Demand Retrieval
Isaac Shi, Zeyuan Li, Fan Liu +4
We introduce the THOR (Transformer Heuristics for On-Demand Retrieval) Module, designed and implemented by eSapiens, a secure, scalable engine that transforms natural-language ques…
eSapiens's DEREK Module: Deep Extraction & Reasoning Engine for Knowledge with LLMs
Isaac Shi, Zeyuan Li, Fan Liu +4
We present the DEREK (Deep Extraction & Reasoning Engine for Knowledge) Module, a secure and scalable Retrieval-Augmented Generation pipeline designed specifically for enterprise d…
eSapiens: A Platform for Secure and Auditable Retrieval-Augmented Generation
Isaac Shi, Zeyuan Li, Fan Liu +4
We present eSapiens, an AI-as-a-Service (AIaaS) platform engineered around a business-oriented trifecta: proprietary data, operational workflows, and any major agnostic Large Langu…
eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
Isaac Shi, Zeyuan Li, Wenli Wang +3
We introduce eSapiens, a unified question-answering system designed for enterprise settings, which bridges structured databases and unstructured textual corpora via a dual-module a…