2 months ago
- Poetiq's approach diverges from mainstream AI by focusing on a self-improving reasoning architecture rather than capital-intensive weight updates.
- The Metasystem autonomously generates harnesses (code, prompts, tools, search strategies) to achieve state-of-the-art (SOTA) results across diverse benchmarks without human intervention.
- Key benefits include autonomous SOTA generation, disruptive performance using older models, and a model-agnostic, compounding moat that decouples reasoning from model weights.
- Poetiq demonstrated SOTA on six complex benchmarks (e.g., ArXivMath, SciCode, Haladir) using various underlying models, often outperforming benchmarks held by newer models like Claude Fable 5.
- The system enables a Recursive Self-Improvement (RSI) loop that continuously optimizes itself by solving new problems, mapping model capabilities, and transferring knowledge across domains.
- Future directions include continuous RSI through enterprise partnerships, dynamic benchmark generation, and evolving beyond static benchmarks to 'living benchmarks' that adapt to model frontiers.