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Post-training has been shown to significantly improve language models'performance on tasks with verifiable outcomes, including mathematical reasoning, software engineering, and computer use. However, whether the same approach can improve forecasting in financial markets is much less clear. Compared with tasks with veri...
The results show that stable recurrent depth requires residual scaling rules that account for parameter visits, not only nominal layer count, and DeepLoop is neutral when no physical block is revisited and improves validation loss and downstream accuracy once recurrent depth is activated.
AQuA, which comprises two separate language-model-driven research systems: one for symbolic factor discovery and one for trainable model development, is presented, which comprises two separate language-model-driven research systems that implement recursive self-improvement at the level of the research process.
This work presents a trust-boundary-centric survey of foundation-model-powered embodied-agent security, and shows that attack research is concentrated on multimodal perception and action interfaces, while defenses are especially concentrated on action-level and runtime protection.
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