Pine AI

Pine AI is the research organization behind the IKP paper (arXiv 2604.24827, April 2026). The work is primarily authored by Bojie Li, who also maintains the companion website at 01.me/research/ikp and the GitHub repo at github.com/19PINE-AI/ikp.

The IKP paper is the group's first widely-cited contribution to the wiki. It is a methodologically careful piece — 1,400 probes across seven obscurity tiers, calibrated against six landmark open-weight models, validated via leave-one-out cross-validation against 89 open-weight models — and the results have load-bearing implications for capability forecasting:

• Factual capacity scales log-linearly with parameter count and does not compress; the Densing Law is falsified for stored knowledge at p < 10⁻¹⁵.
• Closed-model parameter counts can be estimated from black-box API access with median 1.59× error.
• GPT-5.5 is estimated at ~9.7T effective parameters, alone in a top band 1.4× above the next cluster.
• MoE total parameters predict knowledge capacity (R² = 0.79); active parameters do not (R² = 0.51).

The CTF-contest origin story Li tells in the introduction — running "What do you know about USTC Hackergame?" against every newly-released model and watching individual contest titles appear in model outputs in chronological order — frames the paper's methodological move: factual capacity is measurable in the open, every release lays down a new measurement point, and the trajectory across releases is itself a useful artifact.

Cross-references

• incompressible-knowledge-probes — the paper itself
• factual-capacity-scaling — the standalone concept
• mixture-of-experts — updated with the IKP total-vs-active capacity finding
• thinking-mode-rule-erosion — updated with IKP's thinking-mode evidence