Berk Kalelioğlu
Research interests
Berk focuses on machine learning, agentic AI tools, and large and small language models (LLMs and SLMs).He is part of the AIMultiple benchmark team, conducting assessments and providing insights to help readers understand emerging technologies and their real-world applications.
Professional experience
He began his career as a Tech Project Lead at ODTU IVME-R, where he led a project to build physical quantum and pseudorandom number generators.After his tenure at IVME-R, he co-founded a game development company and released a game on Steam.
He later shifted his career toward AI and joined AIMultiple as a Researcher.
Education
Berk holds a Bachelor’s degree in Mathematics from Ankara University.Latest Articles from Berk
Computer Use Agents: Benchmark & Architecture
Computer-use agents operate real desktops and web apps. Their designs, limits, and trade-offs are often unclear. We break down how leading systems work, how they learn, and how their architectures differ. We also reference a focused UI-grounding benchmark on 100 desktop screenshots, across 4 task types and 5 runs per sample. It isolates the quality…
AI VC Benchmark: 11 AI Agents on Venture Capital Tasks
Partnering with early stage VCs, we converted two analyst workflows into benchmarks with human-verified ground truth and scored 11 AI agents on them. See the tasks, results and the scoring method: Each of the 11 models ran each task once. Scores are out of 100. Kimi K3 produced no scorable deal-sourcing run and is recorded…
Tabular Models Benchmark: Performance Across 19 Datasets 2026
We benchmarked 8 tabular learning models on 19 real-world datasets covering roughly 260,000 samples, with dataset sizes from 435 to 48,800 rows. Every model ran on the same machine with 5-fold cross-validation and identical splits. Each dataset is a round-robin of head-to-head matches between models, decided by the primary metric. Elo aggregates all 483 matches…
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