5 citations · 10 across the 17 of their papers we have counts for
17 papers
When More Cores Hurts: The Vector Database Scaling Paradox in HPC
Seth Ockerman, Song Young Oh, Amal Gueroudji +12
Vector databases have been designed and optimized for cloud environments; however, emerging scientific AI workloads (e.g., molecular search, meteorological trajectory detection, an…
No Test Cases, No Problem: Distillation-Driven Code Generation for Scientific Workflows
Siddeshwar Raghavan, Tanwi Mallick
Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Output (I/O) test cases. However…
Toward Reliable, Safe, and Secure LLMs for Scientific Applications
Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick
As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities, from potential "biosafety risks" t…
SCORE: Specificity, Context Utilization, Robustness, and Relevance for Reference-Free LLM Evaluation
Homaira Huda Shomee, Rochana Chaturvedi, Yangxinyu Xie +1
Large language models (LLMs) are increasingly used to support question answering and decision-making in high-stakes, domain-specific settings such as natural hazard response and in…
Statistical Early Stopping for Reasoning Models
Yangxinyu Xie, Tao Wang, Soham Mallick +5
While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, particularly under uncertainty, given…
Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents
Chih-Hsuan, Yang, Tanwi Mallick +5
Large Language Models (LLMs) in multi-agent systems (MAS) have shown promise for complex tasks, yet current training methods lack principled ways to connect system-level evaluation…
ChatVis: Large Language Model Agent for Generating Scientific Visualizations
Tom Peterka, Tanwi Mallick, Orcun Yildiz +3
Large language models (LLMs) are rapidly increasing in capability, but they still struggle with highly specialized programming tasks such as scientific visualization. We present an…
PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
Seth Ockerman, Amal Gueroudji, Tanwi Mallick +4
Spatiotemporal graph neural networks (ST-GNNs) are powerful tools for modeling spatial and temporal data dependencies. However, their applications have been limited primarily to sm…
GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?
Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang +6
We present GeoGrid-Bench, a benchmark designed to evaluate the ability of foundation models to understand geo-spatial data in the grid structure. Geo-spatial datasets pose distinct…
Comparative Evaluation of Prompting and Fine-Tuning for Applying Large Language Models to Grid-Structured Geospatial Data
Akash Dhruv, Yangxinyu Xie, Jordan Branham +1
This paper presents a comparative study of large language models (LLMs) in interpreting grid-structured geospatial data. We evaluate the performance of a base model through structu…
A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation
Yangxinyu Xie, Bowen Jiang, Tanwi Mallick +10
Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressi…
Information Retrieval for Climate Impact
Maarten de Rijke, Bart van den Hurk, Flora Salim +28
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and di…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…
Debiasing Watermarks for Large Language Models via Maximal Coupling
Yangxinyu Xie, Xiang Li, Tanwi Mallick +2
Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communicatio…
ChatVis: Automating Scientific Visualization with a Large Language Model
Tanwi Mallick, Orcun Yildiz, David Lenz +1
We develop an iterative assistant we call ChatVis that can synthetically generate Python scripts for data analysis and visualization using a large language model (LLM). The assista…
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners
Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao +5
This study introduces a hypothesis-testing framework to assess whether large language models (LLMs) possess genuine reasoning abilities or primarily depend on token bias. We go bey…
Towards Rationality in Language and Multimodal Agents: A Survey
Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang +6
This work discusses how to build more rational language and multimodal agents and what criteria define rationality in intelligent systems. Rationality is the quality of being guide…