Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1
Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures. We study a third regime: an open-weight model in non-thinking mode (DeepSeek V3.2) under a strict budget, with no ARC-specific fine-tuning. We study what is recoverable through architecture alone, building agentic harnesses that decompose pattern-discovery and program-synthesis stages explicitly. First, we introduce an Explorer-Definer Pipeline that separates pa...
| Authors | Kabir Moghe, Peter Chin |
|---|---|
| Primary category | cs.AI |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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