10 citations · 29 across the 43 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments
Kung-Hsiang Huang, Akshara Prabhakar, Sidharth Dhawan +6
Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data. Integrating AI agents into CRM…
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…
Text2Data: Low-Resource Data Generation with Textual Control
Shiyu Wang, Yihao Feng, Tian Lan +6
Natural language serves as a common and straightforward signal for humans to interact seamlessly with machines. Recognizing the importance of this interface, the machine learning c…
Editing Arbitrary Propositions in LLMs without Subject Labels
Itai Feigenbaum, Devansh Arpit, Huan Wang +5
Large Language Model (LLM) editing modifies factual information in LLMs. Locate-and-Edit (L\&E) methods accomplish this by finding where relevant information is stored within the n…