3 citations · 3 across the 7 of their papers we have counts for
7 papers
ToolGate: An Executable Acceptance Pipeline for Tool-Dependent Scientific Benchmark Construction
Ke Zhang, Yankang Liu, Roya Zandi +1
Scientific benchmarks are commonly built by domain experts who write tasks and cross-check one another's work, or who adapt existing material from textbooks, published papers, and…
PHREEQC-MCQ-200: A Diagnostic Benchmark for Tool-Augmented Scientific Simulator Agents
Ke Zhang, Sahchit Chundur, Mohammad Javad Qomi +1
Large language model agents are increasingly connected to scientific software, yet it remains unclear when tool access makes scientific computation more reliable rather than merely…
Beyond Compilation: Evaluating Faithful Natural-Language-to-Lean Statement Formalization
Ke Zhang, Patricio Gallardo Candela, Sudhir Murthy +3
Lean verifies that a generated declaration is well typed, but cannot check whether it states what the user intended. We measure how far compilation overstates faithfulness in state…
Understanding Tool-Augmented Agents for Lean Formalization: A Factorial Analysis
Ke Zhang, Patricio Gallardo, Maziar Raissi +1
Automatic translation of natural language mathematics into faithful Lean 4 code is hindered by the fundamental dissonance between informal set-theoretic intuition and strict formal…
Pioneer Agent: Continual Improvement of Small Language Models in Production
Dhruv Atreja, Julia White, Nikhil Nayak +5
Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them to a specific task remains…
mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
Zhengmao Ye, Dengchun Li, Zetao Hu +8
Transformer-based, pre-trained large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly in the emerging {\em pretrain-then-finetu…
DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud
Qinlong Wang, Tingfeng Lan, Yinghao Tang +8
Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model per…