483 citations · 511 across the 13 of their papers we have counts for
13 papers
Performance Foundations of Parallel & Distributed Reasoning Language Models
Maciej Besta, Leonard Schmidt, Lara Nonino +7
Reinforcement Learning with Verifiable Rewards (RLVR) and other RL-style post-training paradigms have been used for aligning large language models (LLMs) with reasoning standards.…
DiG-bench: Discovery in Games
Ruairidh M. Battleday, Kai Sandbrink, Jimi Cullen-Drohan +13
Discovery---formulating novel generalizations---is a central part of the scientific process. Despite its importance, there is a gap in the current AI benchmark landscape, with few…
Spritz: Path-Aware Load Balancing in Low-Diameter Networks
Tommaso Bonato, Ales Kubicek, Abdul Kabbani +3
Low-diameter topologies such as Dragonfly and Slim Fly are increasingly adopted in HPC and datacenter networks, yet existing load balancing techniques either rely on proprietary in…
CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Tiancheng Chen, Ales Kubicek, Langwen Huang +1
Training large language models (LLMs) now requires resources that exceed a single datacenter, making cross-datacenter strategies increasingly crucial. We present CrossPipe, a frame…
Affordable AI Assistants with Knowledge Graph of Thoughts
Maciej Besta, Lorenzo Paleari, Jia Hao Andrea Jiang +15
Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven a…
Reasoning Language Models: A Blueprint
Maciej Besta, Julia Barth, Eric Schreiber +16
Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-R1, and Alibaba's QwQ, have redefined AI's problem-solving capab…
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
Maciej Besta, Ales Kubicek, Robert Gerstenberger +13
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by retrieving supporting documents into the prompt, but existing methods do not explicitly target queries…
CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks
Maciej Besta, Lorenzo Paleari, Marcin Copik +9
Large Language Models (LLMs) are transforming a wide range of domains, yet verifying their outputs remains a significant challenge, especially for complex open-ended tasks such as…
Edge-Disjoint Spanning Trees on Star-Product Networks
Kelly Isham, Laura Monroe, Kartik Lakhotia +3
A star-product operation may be used to create large graphs from smaller factor graphs. Network topologies based on star-products demonstrate several advantages including low-diame…
Demystifying Chains, Trees, and Graphs of Thoughts
Maciej Besta, Florim Memedi, Zhenyu Zhang +13
The field of natural language processing (NLP) has witnessed significant progress in recent years, with a notable focus on improving large language models' (LLM) performance throug…
A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network
Nils Blach, Maciej Besta, Daniele De Sensi +10
Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption…
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek +8
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or…
TornadoQSim: An Open-source High-Performance and Modular Quantum Circuit Simulation Framework
Ales Kubicek, Athanasios Stratikopoulos, Juan Fumero +2
In this article, we present TornadoQSim, an open-source quantum circuit simulation framework implemented in Java. The proposed framework has been designed to be modular and easily…