PRO-LONG gives LLM agents searchable programmatic memory and lifts ARC-AGI-3 by 18 points
dair_ai · x · 2026-07-24
PRO-LONG proposes a minimal context-management framework for LLM agents that stores the full interaction log as structured, searchable memory instead of compressing it away.
Core idea
- Keep a complete record of everything the agent has seen
- Query that history on demand with coding-agent tooling
- Treat memory as a searchable database to avoid the usual tradeoff between saving too much and retrieving the right detail
Reported results
- On the full ARC-AGI-3 public game set, it improves over a base coding agent by 18.0 points on average across frontier models
- It matches or exceeds specialized harnesses at up to 76.1% pass@1 while using 4.2–5.8× fewer tokens
- With Fable 5, it reaches 97.4% best@2 at a total cost of $1,750
Related event: PRO-LONG Framework Optimizes Agent Context Management(4 posts)→
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