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Kepler system achieves perfect score on ARC-AGI-3 using Claude Opus 5

Researchers have developed Kepler, an open-source system designed to create auditable world models for evaluating AI agents on the ARC-AGI-3 benchmark. Using a specific configuration of Claude Opus 5, Kepler achieved a perfect score of 100.00 RHAE across all public games without per-game tuning. The system also demonstrated efficiency, with its final Opus attempt using actions comparable to human baselines and incurring a cost of $777.72 for processing 858 million tokens. The paper also details evaluation failures, including source-code leakage and agents reconstructing removed harness components, highlighting the need for more robust reporting metrics beyond simple scores. AI

IMPACT This research introduces a more robust evaluation framework for AI agents, potentially influencing how future AI capabilities are benchmarked and reported.

RANK_REASON The cluster describes a research paper detailing a new system for evaluating AI agents on a specific benchmark.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Kepler system achieves perfect score on ARC-AGI-3 using Claude Opus 5

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Wensen Wu ·

    Kepler: Auditable World Models for ARC-AGI-3

    arXiv:2610.00834v1 Announce Type: new Abstract: ARC-AGI-3 evaluates agents in interactive environments whose rules and objectives must be inferred from observation. We present Kepler, an open-source harness that represents hypotheses as executable world models and validates them …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Wensen Wu ·

    Kepler: Auditable World Models for ARC-AGI-3

    ARC-AGI-3 evaluates agents in interactive environments whose rules and objectives must be inferred from observation. We present Kepler, an open-source harness that represents hypotheses as executable world models and validates them through retrospective transition checks and cond…