1 citations · 4 across the 7 of their papers we have counts for
9 papers
Pooling Helps, Learned Weighting Hurts In-Context: Decomposing Group Attention
Michael Fore, James Mason Inder, Mrishika Nair +2
Group attention, introduced by the time series forecasting model Chronos-2, attends over the variates of a group at a fixed patch index and serves both multivariate (MV) and in-con…
Non-Parametric Spatiotemporal Trajectory Prediction via State-Conditioned Transition Sampling
Michael Fore, Akshay Jain, Justin Downes +2
We present a training-free method for multi-modal trajectory prediction that achieves comparable accuracy to a 57M-parameter transformer while requiring no GPU and zero learned par…
Multi-Agent Geospatial Copilots for Remote Sensing Workflows
Chaehong Lee, Varatheepan Paramanayakam, Andreas Karatzas +7
We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on…
LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching
Simranjit Singh, Michael Fore, Andreas Karatzas +6
As Large Language Models (LLMs) broaden their capabilities to manage thousands of API calls, they are confronted with complex data operations across vast datasets with significant…
Unlearning Climate Misinformation in Large Language Models
Michael Fore, Simranjit Singh, Chaehong Lee +3
Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language mod…
An LLM-Tool Compiler for Fused Parallel Function Calling
Simranjit Singh, Andreas Karatzas, Michael Fore +2
State-of-the-art sequential reasoning in Large Language Models (LLMs) has expanded the capabilities of Copilots beyond conversational tasks to complex function calling, managing th…
GeckOpt: LLM System Efficiency via Intent-Based Tool Selection
Michael Fore, Simranjit Singh, Dimitrios Stamoulis
In this preliminary study, we investigate a GPT-driven intent-based reasoning approach to streamline tool selection for large language models (LLMs) aimed at system efficiency. By…
Evaluating Tool-Augmented Agents in Remote Sensing Platforms
Simranjit Singh, Michael Fore, Dimitrios Stamoulis
Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input tem…
GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots
Simranjit Singh, Michael Fore, Dimitrios Stamoulis
Geospatial Copilots unlock unprecedented potential for performing Earth Observation (EO) applications through natural language instructions. However, existing agents rely on overly…