77 citations · 90 across the 11 of their papers we have counts for
11 papers
When Deep Research Agents Stagnate: Enhancing Reasoning with Retrieval-Aware Agent Control
Heydar Soudani, Elizabeth Lingg, Faegheh Hasibi +1
In this paper, we analyze the reasoning trajectories of a variety of DRAs and show that existing agents often suffer from reasoning stagnation: the majority of iterations contribut…
Uncertainty Quantification for Multimodal Retrieval Augmented Generation
Simon Binz, Heydar Soudani, Faegheh Hasibi
Retrieval Augmented Generation (RAG) improves the question answering capabilities of Large Language Models (LLMs) by incorporating external knowledge and has recently been extended…
Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents
Mahta Rafiee, Heydar Soudani, Zahra Abbasiantaeb +3
Deep research agents have emerged as LLM-based systems designed to perform multi-step information seeking and reasoning over large, open-domain sources to answer complex questions…
LegacyTranslate: LLM-based Multi-Agent Method for Legacy Code Translation
Zahra Moti, Heydar Soudani, Jonck van der Kogel
Modernizing large legacy systems remains a major challenge in enterprise environments, particularly when migration must preserve domain-specific logic while conforming to internal…
Uncertainty Quantification for Retrieval-Augmented Reasoning
Heydar Soudani, Hamed Zamani, Faegheh Hasibi
Retrieval-augmented reasoning (RAR) is a recent evolution of retrieval-augmented generation (RAG) that employs multiple reasoning steps for retrieval and generation. While effectiv…
Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis
Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi
Large Language Models (LLMs) are valued for their strong performance across various tasks, but they also produce inaccurate or misleading outputs. Uncertainty Estimation (UE) quant…
A Survey on Recent Advances in Conversational Data Generation
Heydar Soudani, Roxana Petcu, Evangelos Kanoulas +1
Recent advancements in conversational systems have significantly enhanced human-machine interactions across various domains. However, training these systems is challenging due to t…
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi
Language Models (LMs) memorize a vast amount of factual knowledge, exhibiting strong performance across diverse tasks and domains. However, it has been observed that the performanc…
Amirkabir campus dataset: Real-world challenges and scenarios of Visual Inertial Odometry (VIO) for visually impaired people
Ali Samadzadeh, Mohammad Hassan Mojab, Heydar Soudani +2
Visual Inertial Odometry (VIO) algorithms estimate the accurate camera trajectory by using camera and Inertial Measurement Unit (IMU) sensors. The applications of VIO span a divers…
Data Augmentation for Conversational AI
Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi
Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large a…
Persian Natural Language Inference: A Meta-learning approach
Heydar Soudani, Mohammad Hassan Mojab, Hamid Beigy
Incorporating information from other languages can improve the results of tasks in low-resource languages. A powerful method of building functional natural language processing syst…