activity
20242026
most citedFine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation

1 citations · 1 across the 15 of their papers we have counts for

collaborators

16 papers

cs.AI2026

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…

cs.CL2026

Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs

Ruoxi Zhao, Maziar Raissi

Evaluating LLM coding agents in algorithmic trading is difficult because static benchmarks risk data contamination and numerical backtest outputs require ground truth from actual c…

eess.SY2026

VeraGrid-Agent: Tool-Augmented LLMs for Distribution Optimal Power Flow at the Grid Edge

Shivanshu Tripathi, Hamed Mohsenian-Rad, Maziar Raissi

Language models have demonstrated remarkable success in solving a wide range of tasks. However, answering complex scientific questions about the power flow often requires solving t…

cs.SD2026

Spectrogram-Based Joint Detection, Localization, and Classification of Events in Continuously Recorded IBR Waveforms

Shivanshu Tripathi, Maziar Raissi, Hamed Mohsenian-Rad

Continuously recorded high-resolution waveform measurements provide rich information about fast power system dynamics. However, they require automated methods to identify events. T…

cs.AI2026

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…

cs.AI2026

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 not that it expresses the statement a user intended. We study two questions for autoformalization without canonical Le…