most citedDemystifying Chains, Trees, and Graphs of Thoughts

11 citations · 14 across the 4 of their papers we have counts for

collaborators

10 papers

cs.LG2026

Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness

Haochen Zhang, Jiaheng Guo, Yu-Chao Huang +3

Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patien…

cs.CL202611 cited

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…

cs.CL20263 cited

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…

cs.NI2026

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…

cs.AI2025

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…

cs.CL2025

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…