The paper shows that agents reason better over long periods when no past information is thrown away.
Keeping every past action in a searchable log helped coding agents solve long tasks far more reliably.
Long-running agents often lose crucial details because summaries discard information before anyone knows what will matter later.
PRO-LONG instead records every observation， action， result， and short plan in one structured text log.
The agent then uses ordinary coding tools， such as search commands and Python， to recover relevant evidence.
On 25 hidden-rule games in ARC-AGI-3， the method improved the same base agents by 18 percentage points on average.
It also matched or nearly matched stronger specialized systems while using 4.2 to 5.8 times fewer billed tokens.
overall， shows long-term agent memory may work best when storage stays lossless and retrieval becomes programmable.
- arxiv. org/abs/2607.20064
Title： "PRO-LONG： Programmatic Memory Enables Long-Horizon Reasoning"