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Sprint projectNov 2, 2025dubai

AI Sentinel — AGI Multi-Metric Forecast Framework

tanzeel shaikh, hardik patel, hitesh kaushik · Team gacgroup

Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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AI Sentinel is a transparent framework for forecasting the progress of artificial intelligence toward transformative milestones such as Artificial General Intelligence (AGI). It combines established scaling laws (Kaplan et al., 2020; Hoffmann et al., 2022) with modern time-series forecasting using Amazon Chronos-Bolt, a zero-shot forecasting model. Using Epoch AI’s dataset of 2783 models (2017–2025), it tracks key metrics including training compute, parameters, cost, and efficiency. The framework introduces composite indicators—Cognitive Efficiency Index (CEI), AGI Proximity Index (API), and Benchmark Growth Elasticity—to represent both scale and efficiency in AI progress. AI Sentinel provides visual, reproducible timelines for understanding capability trends and estimating proximity to milestone ofAGI threshold (10²⁷ FLOPs)(Memory and FLOPS Hardware limits to Prevent AGI?).

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Does the project meaningfully advance AI timeline prediction and capability forecasting? Does it clearly connect to measurable indicators of AI progress (compute, benchmarks, economic impacts, automation milestones)? Does it build on or challenge existing forecasting frameworks like biological anchors, scaling laws, or scenario planning? Does it offer novel methodologies, data sources, or empirical insights that could improve forecast accuracy? Is it grounded in observable trends rather than pure speculation?

Does this project inform critical decisions about AI development and preparedness? Does it help identify key uncertainties, decision points, or early warning indicators? How well does the project connect technical metrics to real-world impacts and policy needs? Could the output guide resource allocation, safety research priorities, or regulatory timelines? Does it reduce uncertainty around transformative AI milestones or capability emergence?

Is the project methodologically rigorous, reproducible, and technically sound? Is the forecasting approach well-calibrated with appropriate uncertainty quantification? Are the data sources, assumptions, and limitations clearly documented? Does the project demonstrate sound statistical methodology and honest treatment of model uncertainties? Would the tool, model, or framework be useful for ongoing forecasting efforts, research planning, or policy analysis?

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Cite this project

@misc{shaikh2025ai,
  title = {{AI Sentinel — AGI Multi-Metric Forecast Framework}},
  author = {tanzeel shaikh and hardik patel and hitesh kaushik},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/ai-sentinel-agi-multimetric-forecast-framework-h0uq}},
  url = {https://apartresearch.com/sprints/projects/ai-sentinel-agi-multimetric-forecast-framework-h0uq}
}

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