DigClaw said its Rhizome v1 forecasting framework took the No. 1, No. 3 and No. 7 spots on the FutureX evaluation platform in July, with the results produced separately on different foundation models including Kimi-K3 and DeepSeek-V4-Pro. The company argues the outcome supports a broader thesis: forecasting capability can accumulate outside the base model itself, while model upgrades mainly improve the system’s raw tools for language, reasoning and tool use. In the technical description released alongside the result, DigClaw said Rhizome is built around three engineering choices. The first is to separate search from reasoning, rather than relying on one model to do both. The second is to preserve full prediction trajectories before outcomes are known, then use settlement feedback, Brier Score-based aggregation and Platt scaling to calibrate future probabilities. The third is a causal-chain-aware Bayesian updating system meant to avoid double-counting signals that come from the same underlying driver. The article also links the research to investing. DigClaw said its forecasting stack underpins Newborn Ventures, an AI-native early-stage investment and incubation firm it launched, and that the same system is being used for internal investment practice while also being opened to industrial companies, financial institutions and government-guided funds.