5 citations · 10 across the 20 of their papers we have counts for
21 papers
BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces
Liangwei Yang, Jielin Qiu, Zixiang Chen +9
Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited. Existing benchmarks for user understanding often…
Enterprise Sales Copilot: Enabling Real-Time AI Support with Automatic Information Retrieval in Live Sales Calls
Jielin Qiu, Liangwei Yang, Ming Zhu +11
During live sales calls, customers frequently ask detailed product questions that require representatives to manually search internal databases and CRM systems. This process typica…
Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial
Jielin Qiu, Zixiang Chen, Liangwei Yang +11
We present a technical tutorial for building enterprise-grade realtime voice agents from first principles. While end-to-end speech-to-speech models may ultimately provide the best…
Position: Vector Prompt Interfaces Should Be Exposed to Enable Customization of Large Language Models
Liangwei Yang, Shiyu Wang, Haolin Chen +12
As large language models (LLMs) transition from research prototypes to real-world systems, customization has emerged as a central bottleneck. While text prompts can already customi…
VoiceAgentRAG: Solving the RAG Latency Bottleneck in Real-Time Voice Agents Using Dual-Agent Architectures
Jielin Qiu, Jianguo Zhang, Zixiang Chen +12
We present VoiceAgentRAG, an open-source dual-agent memory router that decouples retrieval from response generation. A background Slow Thinker agent continuously monitors the conve…
Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents
Yuxin Liu, Mingye Zhu, Siyuan Liu +2
The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models. For realism in social simulation, these agents must…
AudioCapBench: Quick Evaluation on Audio Captioning across Sound, Music, and Speech
Jielin Qiu, Jianguo Zhang, Zixiang Chen +13
We introduce AudioCapBench, a benchmark for evaluating audio captioning capabilities of large multimodal models. \method covers three distinct audio domains, including environmenta…
Prompt Optimization Via Diffusion Language Models
Shiyu Wang, Haolin Chen, Liangwei Yang +8
We propose a diffusion-based framework for prompt optimization that leverages Diffusion Language Models (DLMs) to iteratively refine system prompts through masked denoising. By con…
LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering
Jielin Qiu, Zuxin Liu, Zhiwei Liu +18
As large language models (LLMs) evolve into sophisticated autonomous agents capable of complex software development tasks, evaluating their real-world capabilities becomes critical…
GeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs
Liangwei Yang, Jing Ma, Jianguo Zhang +11
Graph neural networks (GNNs) on text--attributed graphs (TAGs) typically encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear nei…
LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
Jielin Qiu, Zuxin Liu, Zhiwei Liu +14
The emergence of long-context language models with context windows extending to millions of tokens has created new opportunities for sophisticated code understanding and software d…
Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Rithesh Murthy, Ming Zhu, Liangwei Yang +6
Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix…
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback
Thai Hoang, Kung-Hsiang Huang, Shirley Kokane +12
Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involv…
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Akshara Prabhakar, Zuxin Liu, Ming Zhu +12
Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect m…
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
Jianguo Zhang, Thai Hoang, Ming Zhu +13
Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environme…
PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data
Juntao Tan, Liangwei Yang, Zuxin Liu +11
Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users. A key scenario in this domain involves enabling AI m…
ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs
Shirley Kokane, Ming Zhu, Tulika Awalgaonkar +15
Evaluating Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, i…
PRACT: Optimizing Principled Reasoning and Acting of LLM Agent
Zhiwei Liu, Weiran Yao, Jianguo Zhang +13
We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the…
xLAM: A Family of Large Action Models to Empower AI Agent Systems
Jianguo Zhang, Tian Lan, Ming Zhu +19
Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing speci…
APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
Zuxin Liu, Thai Hoang, Jianguo Zhang +14
The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed t…
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
Rithesh Murthy, Liangwei Yang, Juntao Tan +15
The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stabil…