483 citations · 1k across the 69 of their papers we have counts for
72 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.…
Network Design for Wafer-Scale Systems with Wafer-on-Wafer Hybrid Bonding
Patrick Iff, Tommaso Bonato, Maciej Besta +2
Transformer-based large language models are increasingly constrained by data movement as communication bandwidth drops sharply beyond the chip boundary. Wafer-scale integration usi…
GraphSeek: Next-Generation Graph Analytics with LLMs
Maciej Besta, Łukasz Jarmocik, Orest Hrycyna +7
Graphs are foundational across domains but remain hard to use without deep expertise. LLMs promise accessible natural language (NL) graph analytics, yet they fail to process indust…
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…
Psychologically Enhanced AI Agents
Maciej Besta, Shriram Chandran, Robert Gerstenberger +9
We introduce MBTI-in-Thoughts, a framework for enhancing the effectiveness of Large Language Model (LLM) agents through psychologically grounded personality conditioning. Drawing o…
Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors
Patrick Iff, Paul Bruegger, Marcin Chrapek +3
Advances in embedding models for text, image, audio, and video drive progress across multiple domains, including retrieval-augmented generation, recommendation systems, and others.…
BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers
Patrik Okanovic, Sameer Deshmukh, Grzegorz Kwasniewski +8
The energy consumption of large-scale ML models is dominated by data movement, shuffling billions of parameters across memory hierarchies and data centers. Sparsification offers a…
Higher-Order Graph Databases
Maciej Besta, Shriram Chandran, Jakub Cudak +6
Recent advances in graph databases (GDBs) have been driving interest in large-scale analytics, yet current systems fail to support higher-order (HO) interactions beyond first-order…
FoldedHexaTorus: An Inter-Chiplet Interconnect Topology for Chiplet-based Systems using Organic and Glass Substrates
Patrick Iff, Maciej Besta, Torsten Hoefler
Chiplet-based systems are rapidly gaining traction in the market. Two packaging options for such systems are the established organic substrates and the emerging glass substrates. T…
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…
PlaceIT: Placement-based Inter-Chiplet Interconnect Topologies
Patrick Iff, Benigna Bruggmann, Maciej Besta +2
2.5D integration technology is gaining traction as it copes with the exponentially growing design cost of modern integrated circuits. A crucial part of a 2.5D stacked chip is a low…
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…
High Performance Unstructured SpMM Computation Using Tensor Cores
Patrik Okanovic, Grzegorz Kwasniewski, Paolo Sylos Labini +3
High-performance sparse matrix-matrix (SpMM) multiplication is paramount for science and industry, as the ever-increasing sizes of data prohibit using dense data structures. Yet, e…
Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments
Maciej Besta, Robert Gerstenberger, Patrick Iff +9
Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other application domains…
Demystifying Higher-Order Graph Neural Networks
Maciej Besta, Florian Scheidl, Lukas Gianinazzi +4
Higher-order graph neural networks (HOGNNs) and the related architectures from Topological Deep Learning are an important class of GNN models that harness polyadic relations betwee…
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…
Low-Depth Spatial Tree Algorithms
Yves Baumann, Tal Ben-Nun, Maciej Besta +3
Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements…
Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang +9
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In…
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…
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers
Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi +4
Many graph representation learning (GRL) problems are dynamic, with millions of edges added or removed per second. A fundamental workload in this setting is dynamic link prediction…
RapidChiplet: A Toolchain for Rapid Design Space Exploration of Chiplet Architectures
Patrick Iff, Benigna Bruggmann, Blaise Morel +3
Chiplet architectures are on the rise as they promise to overcome the scaling challenges of monolithic chips. A key component of such architectures is an efficient inter-chiplet in…
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…
Cached Operator Reordering: A Unified View for Fast GNN Training
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun +4
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However,…