# How enabling two settings tripled our scores on the ARC-AGI-3 benchmark

Source: [OpenAI](https://openai.com/index/how-two-settings-tripled-our-arc-agi-3-scores)  
Feed7 permalink: https://feed7.dev/p/how-enabling-two-settings-tripled-our-scores-on-the-arc--cc8e132c61  
Published: 2026-07-29T00:00:00.000Z  
Trust: Official Source (official_source)

## Why Included

Record reasoning retention and compaction with the model name because runtime settings can materially alter agent eval results.

## Source Summary

Two API settings—reasoning retention and compaction—reportedly tripled GPT-5.6’s ARC-AGI-3 score. Agent evals should treat runtime configuration as part of the tested system.

## Practical Implication

Record these settings alongside the model name in agent evaluations. Configuration can materially affect results, so defaults and explicit settings should not be compared as equivalent systems.

## Agent-Ready Context

OpenAI says enabling **reasoning retention** and **compaction** produced **3× ARC-AGI-3 scores** for GPT-5.6 while also improving efficiency.

Record these settings alongside the model name in agent evaluations. Configuration can materially affect results, so defaults and explicit settings should not be compared as equivalent systems.

The supplied material provides no absolute scores, token usage, latency, or experimental detail, leaving the size and generality of the efficiency gain unclear.

## Context Map

- Layer: benchmark
- Domains: None
- Topics: agent-evals, benchmark-integrity, context-caching

## Uncertainty

- None recorded.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.