I spent three hours every Friday morning copying invoice data between desktop apps. Tried setting up Sai to replay the routine while I closed the laptop. Watching my #SaiFleet handle it feels like getting my morning back. #Robosecretary
The fix is OPF (Orthogonal Predictive Factorization): K complementary subspaces, each predicted through its own pathway, then synthesized into a complete latent state for rollout, planning, and intervention.
Simply adding prediction heads did not produce the same structure in our capacity-matched CITRIS audit against unconstrained multiheads:
• Cross-factor overlap: 0.4550 → 5.18 × 10⁻¹⁶
• Synthesis condition number: 438.52 → 1.00005
• Transpose-synthesis NMSE with exact factors: 0.7886 → 2.98 × 10⁻¹⁴
One OPF design, with domain-specific adapters and encoders. Compared with standard JEPA, MSE fell on all nine prediction tasks in the matched dynamics benchmark. In a separate APEBench Burgers audit, held-out late-state error fell by 49.5%.
Across four tested molecular systems, JEPA-Anything had the lowest 100-step final-position error among the compared methods. On zero-shot PBMC clustering, AvgBIO rose from 0.7194 with Cell-JEPA to 0.7752.
Excited to have helped build the AItonomy Foundation — a nonprofit research organization advancing AI4Science and Science4AI.
We’re building the open infrastructure for the two to advance together, and connecting early-career scientists around the world to make it happen.
@SenalHQ not sure how the odds are set on Senal but if it's community-driven that could actually be more accurate than the bookies on some of these upsets 👀
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