10 citations · 29 across the 43 of their papers we have counts for
44 papers
Critical-State RL: Diagnosing Trainable States for Multi-Turn Tool Use
Zixiang Chen, Wenting Zhao, Zhepeng Cen +9
Multi-turn tool-use failures can hinge on a single model call, yet reward variation alone does not reveal which call would benefit from training. When rewards depend on later inter…
Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning
Heng Wang, Jielin Qiu, Wenting Zhao +8
Large language models achieve superior performance on tasks that require extended reasoning, but long chains of thought make the KV cache a severe memory bottleneck. Existing KV ca…
AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses
Cheng Qian, Wenting Zhao, Liangwei Yang +6
Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter's parameters, through teacher forcing, on-policy distillation, a…
A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency
Zhao Yang, Wang Huan, Li Yingshuo +2
Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction, especially under high-noise knowledge bas…
RealUserSim: Bridging the Reality Gap in Agent Benchmarking via Grounded User Simulation
Ming Zhu, Juntao Tan, Rithesh Murthy +6
LLM-based user simulation is the primary mechanism for end-to-end agent evaluation, yet simulated users are poor proxies for real humans: unconstrained LLM defaults produce a Forma…
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…
LiveAgentBench: Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges
Hao Li, Huan Wang, Jinjie Gu +3
As large language models grow more capable, general AI agents have become increasingly prevalent in practical applications. However, existing benchmarks face significant limitation…
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…
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…
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…
Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics
Akshara Prabhakar, Roshan Ram, Zixiang Chen +5
As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, th…
xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning
Cheng Qian, Zuxin Liu, Shirley Kokane +10
Modern LLM deployments confront a widening cost-performance spectrum: premium models deliver strong reasoning but are expensive, while lightweight models are economical yet brittle…
ToolLibGen: Scalable Automatic Tool Creation and Aggregation for LLM Reasoning
Murong Yue, Zhiwei Liu, Liangwei Yang +8
Large Language Models (LLMs) equipped with external tools have demonstrated enhanced performance on complex reasoning tasks. The widespread adoption of this tool-augmented reasonin…
Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels
Zhepeng Cen, Haolin Chen, Shiyu Wang +8
Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a training-generation gap and limits robust…
CoDA: Coding LM via Diffusion Adaptation
Haolin Chen, Shiyu Wang, Can Qin +12
Diffusion language models promise bidirectional context and infilling capabilities that autoregressive coders lack, yet practical systems remain heavyweight. We introduce CoDA, a 1…
UserRL: Training Interactive User-Centric Agent via Reinforcement Learning
Cheng Qian, Zuxin Liu, Akshara Prabhakar +10
Reinforcement learning (RL) has shown promise in training agentic models that move beyond static benchmarks to engage in dynamic, multi-turn interactions. Yet, the ultimate value o…
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…
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
Zhiwei Liu, Jielin Qiu, Shiyu Wang +9
The rapid rise of Large Language Models (LLMs)-based intelligent agents underscores the need for robust, scalable evaluation frameworks. Existing methods rely on static benchmarks…
UserBench: An Interactive Gym Environment for User-Centric Agents
Cheng Qian, Zuxin Liu, Akshara Prabhakar +9
Large Language Models (LLMs)-based agents have made impressive progress in reasoning and tool use, enabling them to solve complex tasks. However, their ability to proactively colla…
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…
Entropy-Based Block Pruning for Efficient Large Language Models
Liangwei Yang, Yuhui Xu, Juntao Tan +5
As large language models continue to scale, their growing computational and storage demands pose significant challenges for real-world deployment. In this work, we investigate redu…
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…