10 papers
Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions
Zelin Li, Yiyun Su, Matt White +3
Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets, and time. We study this interpr…
CoWeaver: A Bi-directional, Learnable and Explainable Matching Engine for Mixed Human-Agent Science Collaboration
Jiayao Gu, Kexin Chu, Peidong Liu +5
LLM-based agents excel at writing articles, coding and information retrieval. However, they fail to form strong collaborations within the scientific community due to the bidirectio…
Constructing Composite Features for Interpretable Music-Tagging
Chenhao Xue, Weitao Hu, Joyraj Chakraborty +5
Combining multiple audio features can improve the performance of music tagging, but common deep learning-based feature fusion methods often lack interpretability. To address this p…
PeopleSearchBench: Evaluating AI-Powered People Search Platforms with Criteria-Grounded Verification
Tianyu Shi, Wei Wang, Zequn Xie +10
AI-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized benchmark exists for their rigorous…
ComfySearch: Autonomous Exploration and Reasoning for ComfyUI Workflows
Jinwei Su, Qizhen Lan, Zeyu Wang +7
AI-generated content has progressed from monolithic models to modular workflows, especially on platforms like ComfyUI, allowing users to customize complex creative pipelines. Howev…
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…
Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows
Jinwei Su, Qizhen Lan, Yinghui Xia +6
Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks. However, existing multi-agent frameworks often rely on static or task-level wo…
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…