9 citations · 38 across the 25 of their papers we have counts for
26 papers
Hopfield Networks in Non-volatile Memory
Patrick Miller, Ulya R. Karpuzcu
Many important computational workloads are bound by the performance of the memory system that supplies data to conventional processors. Compute in Memory (CiM) promises to circumve…
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
Patrick Emami, Sameera Horawalavithana, Truc Nguyen +11
Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists"…
MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding
Sai Munikoti, Ian Stewart, Chengping Chai +4
The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-specific datasets that integrat…
SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
Nithin Somasekharan, Youssef Hassan, Shiyao Lin +5
Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reas…
Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery
Renuka Chintalapati, Sid Raskar, Anurag Acharya +3
Coding agents accumulate extensive context during long-running tasks, yet fixed context windows force practitioners to choose between truncation and task failure. While numerous me…
Reward Design for Physical Reasoning in Vision-Language Models
Derek Lilienthal, Manisha Mukherjee, Sameera Horawalavithana
Physical reasoning over images requires integrating perception, domain knowledge, and multi-step symbolic inference, yet Vision-Language Models (VLMs) fall far short of human perfo…
Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models
Sameera Horawalavithana, Lauren Phillips, Ian Stewart +2
Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new and more capable LLMs emerge…
Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing
Reilly Raab, Mike Parker, Dan Nally +4
The advent of language models (LMs) has the potential to dramatically accelerate tasks that may be cast to text-processing; however, real-world adoption is hindered by concerns reg…
Evaluating the Robustness of Dense Retrievers in Interdisciplinary Domains
Sarthak Chaturvedi, Anurag Acharya, Rounak Meyur +3
Evaluation benchmark characteristics may distort the true benefits of domain adaptation in retrieval models. This creates misleading assessments that influence deployment decisions…
Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models
Ian Stewart, Sameera Horawalavithana, Brendan Kennedy +2
Multimodal foundation models (MFMs) such as OFASys show the potential to unlock analysis of complex data such as images, videos, and audio data via text prompts alone. However, the…
WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain
Rounak Meyur, Hung Phan, Sridevi Wagle +5
Wind energy project assessments present significant challenges for decision-makers, who must navigate and synthesize hundreds of pages of environmental and scientific documentation…
Benchmarking LLMs for Environmental Review and Permitting
Rounak Meyur, Hung Phan, Koby Hayashi +12
The National Environment Policy Act (NEPA) stands as a foundational piece of environmental legislation in the United States, requiring federal agencies to consider the environmenta…
Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities
Sai Munikoti, Ian Stewart, Sameera Horawalavithana +4
Multimodal models are expected to be a critical component to future advances in artificial intelligence. This field is starting to grow rapidly with a surge of new design elements…
ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science
Sai Munikoti, Anurag Acharya, Sridevi Wagle +1
Large language models record impressive performance on many natural language processing tasks. However, their knowledge capacity is limited to the pretraining corpus. Retrieval aug…
Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science
Sridevi Wagle, Sai Munikoti, Anurag Acharya +2
Large language models (LLMs) have shown remarkable achievements in natural language processing tasks, producing high-quality outputs. However, LLMs still exhibit limitations, inclu…
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Sai Munikoti, Anurag Acharya, Sridevi Wagle +1
Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations. Re…
NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain
Anurag Acharya, Sai Munikoti, Aaron Hellinger +3
As LLMs have become increasingly popular, they have been used in almost every field. But as the application for LLMs expands from generic fields to narrow, focused science domains,…
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +2
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuc…
SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions
Sameera Horawalavithana, Sai Munikoti, Ian Stewart +2
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving LLMs to align e…
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +5
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph…
Social-Media Activity Forecasting with Exogenous Information Signals
Kin Wai Ng, Sameera Horawalavithana, Adriana Iamnitchi
Due to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread. Modeling soc…
Malicious and Low Credibility URLs on Twitter during the AstraZeneca COVID-19 Vaccine Development
Sameera Horawalavithana, Ravindu De Silva, Mohamed Nabeel +3
We investigate the link sharing behavior of Twitter users following the temporary halt of AstraZeneca COVID-19 vaccine development in September 2020. During this period, we show th…
Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks
Sameera Horawalavithana, John Skvoretz, Adriana Iamnitchi
Predicting the flow of information in dynamic social environments is relevant to many areas of the contemporary society, from disseminating health care messages to meme tracking. W…
On the Privacy of dK-Random Graphs
Sameera Horawalavithana, Adriana Iamnitchi
Real social network datasets provide significant benefits for understanding phenomena such as information diffusion or network evolution. Yet the privacy risks raised from sharing…