5 citations · 5 across the 5 of their papers we have counts for
5 papers
No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models
Jack Boylan, Chris Hokamp
Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so every pra…
NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment
Shashank Mangla, Chris Hokamp, Jack Boylan +4
We design and implement NegotiationGym, an API and user interface for configuring and running multi-agent social simulations focused upon negotiation and cooperation. The Negotiati…
GLiREL -- Generalist Model for Zero-Shot Relation Extraction
Jack Boylan, Chris Hokamp, Demian Gholipour Ghalandari
We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. Inspire…
STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs
Aaron Zolnai-Lucas, Jack Boylan, Chris Hokamp +1
We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode…
KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction
Jack Boylan, Shashank Mangla, Dominic Thorn +3
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been…