AI researcher evaluating bottlenecks in closed-loop LLM systems with a focus on long-horizon capabilities
How media typically covers Arun Arvindh
Arun Arvindh as author
Small language models appear to succeed on short tasks but fail rapidly on extended multi-step tasks due to execution errors and self-conditioning degradation, while scaling and sequential test-time compute significantly improve long-horizon task completion.
“Co-author of research on LLM scaling and execution capabilities”