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    Reviewed current signal · 2026-07-29

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

    Reviewed through September 18, 2026

    2026-07-29 · Reviewed current signal

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

    Era
    Current reviewed signal
    Theme
    Agent development
    Evidence form
    Benchmark
    Source of record
    OpenAI
    Source tier
    A
    Impact
    High
    School / paradigm
    Not recorded — current signals carry no formal school
    Application
    Long-horizon reasoning agents
    Researchers
    Not recorded

    Understand

    Plain-language record, transferred from the reviewed source module.

    What changed. OpenAI reported that retaining reasoning state and enabling context compaction materially improved GPT-5.6 performance and efficiency on ARC-AGI-3.

    Technique / discovery. Reasoning-state retention and automatic context compaction.

    Apply

    Professional implication, only where the reviewed record states one.

    Why it matters. Agent capability can depend heavily on state management and harness configuration, not only the base model.

    Application. Long-horizon reasoning agents

    Verify

    Evidence status, stated limitations, and the external sources this record actually carries.

    Evidence maturity. Benchmark (source tier A)

    Identified bottleneck. Results may not generalize beyond one model and benchmark; exact harness choices can confound model comparisons.

    Caveat / evidence note. First-party benchmark analysis; use the settings as hypotheses to reproduce on your own tasks.

    Review status. Reviewed. User requested: Yes.

    Reproduce

    A reproduction tutorial is linked only when one exists for this exact record.

    A reproduction tutorial is not yet available for this entry. The closest reviewed material is Agent planning and cognitive architectures and Language models and representation.

    Cite or share

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