| name | pro-long-programmatic-memory-enables-long-horizon- |
| description | Skill generated from arXiv paper 2607.20064: PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning |
| metadata | {"arxiv":{"id":"2607.20064","title":"PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning","authors":["Alexis Fox","Junlin Wang","Paul Rosu","Bhuwan Dhingra"],"published":"2026-07-22","categories":["cs.AI"],"url":"https://arxiv.org/abs/2607.20064","utility":1}} |
PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning
arXiv: 2607.20064
Published: 2026-07-22
Authors: Alexis Fox, Junlin Wang, Paul Rosu, Bhuwan Dhingra
Categories: cs.AI
Utility: 1.00
Key Innovation
Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected in their limited performance on continual learning benchmarks such as ARC-AGI-3, especially when models are evaluated out of the box. Various agent harnesses have been proposed to close this gap, and each commits to a strategy for handling long sequences of observations, i.e., what information to save from the environment and how...
Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.AI.
References