23 citations · 46 across the 18 of their papers we have counts for
10 papers · 1 filter
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate +10
The resurgence of autonomous agents built using large language models (LLMs) to solve complex real-world tasks has brought increased focus on LLMs' fundamental ability of tool or f…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
Formally Specifying the High-Level Behavior of LLM-Based Agents
Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo +7
Autonomous, goal-driven agents powered by LLMs have recently emerged as promising tools for solving challenging problems without the need for task-specific finetuned models that ca…
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations
Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse +5
Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the…
Learning to Guide a Saturation-Based Theorem Prover
Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni +8
Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the…
Neural Analogical Matching
Maxwell Crouse, Constantine Nakos, Ibrahim Abdelaziz +1
Analogy is core to human cognition. It allows us to solve problems based on prior experience, it governs the way we conceptualize new information, and it even influences our visual…
An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic
Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse +1
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning. While there have been various proposals for encoding…
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio +4
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. I…
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni +7
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to o…
High-Fidelity Vector Space Models of Structured Data
Maxwell Crouse, Achille Fokoue, Maria Chang +6
Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most mach…