arXiv: cs.AI preprintsInternational5 November 2026
Rules, Traces and Routing Statistics: Training and Calibration Data for Open Models on ARC-AGI-2
This is an official announcement record
Firsthand records what arXiv: cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2611.06827v1 Announce Type: new Abstract: We study which training and calibration data help three open models solve ARC-AGI-2 tasks when the solver runs offline on four L4 GPUs for at most 12 hours. We compare data offline on public tasks and report 189 runs on the 120 hidden semi-private tasks. For the NVARC Qwen3-4B grid model, we built a corpus of 12,000 executable rules in a typed language and 95,993 tasks; a rule is admitted only if its behaviour on a fixed probe set differs from that of every admitted rule, and a task only if its pairs refute a shortlist of nearest competing rules
Read the official announcement
Opens arxiv.org
More from arXiv: cs.AI preprints
- Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation6 November 2026
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks6 November 2026
- BlockLock: Reproducible LLM Inference with Fixed Batch Geometry6 November 2026
- Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism6 November 2026
- The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record6 November 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.