1 citations · 1 across the 15 of their papers we have counts for
16 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…
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…
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…
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…
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…
From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries
Reza Akbarian Bafghi, Sukirt Thakur, Maziar Raissi
Accurate and rapid estimation of hemodynamic metrics, such as pressure and wall shear stress (WSS), is important for assessing the severity of Coronary Artery Disease (CAD). Existi…
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…
Test-Driven Agentic Framework for Reliable Robot Controller
Shivanshu Tripathi, Reza Akbarian Bafghi, Maziar Raissi
In this work, we present a test-driven, agentic framework for synthesizing a deployable low-level robot controller for navigation tasks. Given a 2D map with an image of an ultrason…
Data-Efficient Physics-Informed Learning to Model Synchro-Waveform Dynamics of Grid-Integrated Inverter-Based Resources
Shivanshu Tripathi, Hossein Mohsenzadeh Yazdi, Maziar Raissi +1
Inverter-based resources (IBRs) exhibit fast transient dynamics during network disturbances, which often cannot be properly captured by phasor and SCADA measurements. This shortcom…
NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations
Satish Chandran, Maedeh Makki, Maziar Raissi +2
We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving differential equations. Rather than enfor…
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
Sukirt Thakur, Marcus Roper, Yang Zhou +8
More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions…
Aligning to What? Limits to RLHF Based Alignment
Logan Barnhart, Reza Akbarian Bafghi, Stephen Becker +1
Reinforcement Learning from Human Feedback (RLHF) is increasingly used to align large language models (LLMs) with human preferences. However, the effectiveness of RLHF in addressin…
Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation
Reza Akbarian Bafghi, Carden Bagwell, Avinash Ravichandran +2
Adapting deep learning models to new domains often requires computationally intensive retraining and risks catastrophic forgetting. While fine-tuning enables domain-specific adapta…
Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning
Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi +2
Self-supervised learning (SSL) has revolutionized learning from large-scale unlabeled datasets, yet the intrinsic relationship between pretraining data and the learned representati…
Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting
Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni +1
Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge. We observe that this is…