1 citations · 1 across the 6 of their papers we have counts for
4 papers · 2 filters
Iterative Deployment Improves Planning Skills in LLMs
Augusto B. Corrêa, Yoav Gelberg, Luckeciano C. Melo +3
We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change…
Frontier Large Language Models Rival State-of-the-Art Planners
Augusto B. Corrêa, André G. Pereira, Jendrik Seipp
A series of influential studies established that large language models cannot reliably solve even simple planning tasks. We show that the latest generation of frontier models overt…
Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code
Augusto B. Corrêa, André G. Pereira, Jendrik Seipp
In recent years, large language models (LLMs) have shown remarkable capabilities in various artificial intelligence problems. However, they fail to plan reliably, even when prompte…
Counting and Reasoning with Plans
David Speck, Markus Hecher, Daniel Gnad +2
Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantita…