10 papers
MIRA: A Musical Intent Refinement Agent for Aligning Text-to-Music Generation with User Intent
Zekai Liu, Zhilin Wang, Xuzheng He +2
Text-to-music systems produce increasingly convincing audio, yet evaluation reveals little about whether the result matches user intent. A global text-audio relevance score can ove…
SAGE: A Statistical Acceptance Gate for Self-Evolving Agents
Yihao Wang, Linhan Xia, Rui Liu +6
Large Language Model (LLM)-based agents increasingly self-evolve by editing a persistent skill document that encodes their workflow, tool-use rules, and decision logic. This loop h…
Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations
Yang Yang
Chunking is the first and most consequential step in retrieval-augmented generation (RAG): every downstream retrieval decision inherits the chunk boundaries. We present a three-sta…
SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents
Wenxuan Wang, Haoyu Sun, Fukuan Hou +4
Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions. As these memories grow, they may reinforce one another, di…
Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
Haoyu Sun, Wenxuan Wang, Mingyang Song +5
Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent eva…
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…