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Towards Reliable LLM Reasoning: Coordinated Agents, Variance-Aware Evaluation, and Lean Inference by Prof. Akhil Arora
October 30, 2025 @ 11:00 am - 12:00 pm
- Abstract: Large language models (LLMs) are increasingly deployed as reasoning engines, yet their practical use remains constrained by three persistent challenges: achieving high-quality reasoning at low cost, measuring performance reliably, and ensuring efficient, reproducible deployment. In this talk, I will present a research agenda addressing these challenges through new methods, benchmarks, and systems for practical LLM reasoning. I begin with Next, I turn to Finally, I focus on Together, these contributions chart a path toward LLM reasoning that is not only more powerful, but also leaner, more reliable, and environmentally responsible.
- Bio: Akhil Arora is a Tenure-Track Assistant Professor of Computer Science at Aarhus University, where he heads the CLAN for AI Research on Language and Networks (or “CLAN” for short). He is a fellow of the Copenhagen Center for Social Data Science (SODAS), an affiliate of the Pioneer Centre for AI and ELLIS, and a formal collaborator of the Wikimedia Foundation, the non-profit organization that manages Wikipedia and related projects. Akhil’s research lies broadly in human-centered AI with a focus on improving human knowledge-seeking, bridging knowledge gaps, and promoting knowledge equity on the Web. To this end, he devises methods and tools blending techniques from NLP, AI, Graph ML, and Computational social science. Recently, his group has been devising robust, trustworthy, accessible, and efficient LLM inference strategies.
- Akhil received his PhD in Computer Science from EPFL (2024) in Switzerland, his MS from IIT Kanpur (2013), and his undergraduate degree from NCU Gurgaon (2010). In days of yore, he spent close to five years in the industry working with the research labs of Xerox and American Express as a Research Scientist. His work on influence maximization has been recognized as the 8th most influential paper of SIGMOD 2017 by Paper Digest and received the 2018 ACM SIGMOD Most Reproducible Paper Award. He is a recipient of the prestigious EDIC Doctoral Fellowship, an alumnus of the coveted Heidelberg Laureate Forum, and a DAAD AINet fellow on human-centered AI. Akhil is a director of the P1-programs on
