5 citations · 18 across the 15 of their papers we have counts for
5 papers · 1 filter
Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills
Md Tahmid Rahman Laskar, Gundeep Singh, Xue-Yong Fu +11
Enterprise customer-support agents increasingly rely on Large Language Models (LLMs) to execute bounded, tool-using workflows. We study this problem through the lens of Skill LLMs:…
When Better Turns Do Not Make Better Agents: Diagnosing the Gap Between Next-Turn Metrics and Workflow Success
Md Tahmid Rahman Laskar, Xue-Yong Fu, Gundeep Singh +11
Agent models are frequently evaluated one decision at a time, where the model predicts the next action based on the gold interaction history, which is scored against a reference. W…
SFT or RL for Tool-Calling Agents? A Controlled Study Across Data, Method, and Scale
Md Tahmid Rahman Laskar, Xue-Yong Fu, Shashi Bhushan TN
Limited controlled evidence exists on how training data, adaptation method, and model scale jointly affect tool-calling performance in language-model agents. We evaluate supervised…
Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs
Gundeep Singh, Parsa Kavehzadeh, Jing Xia +5
Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools…
From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
Md Tahmid Rahman Laskar, Xue-Yong Fu, Seyyed Saeed Sarfjoo +3
Voice agents increasingly require reliable tool use from speech, whereas prominent tool-calling benchmarks remain text-based. We study whether verified text benchmarks can be conve…