AI 速览：提出分位数传播规划（QPP），用少量固定扰动分位数经动力学传播并积分代价曲线，在有限模型评估预算下同时评估均值与尾部风险。
Risk-aware planning must identify rare, costly outcomes using only a limited number of model evaluations. Monte Carlo rollouts spend this budget on noisy tail estimates, while planning from the mean can miss the tail entirely. We introduce quantile propagation planning (QPP), which propagates a small, fixed set of disturbance quantiles through the dynamics and integrates the resulting cost curve to evaluate both mean and tail risk. The method is exact at the propagated levels, converges as the quantile grid is refined, and admits a one-sided conservatism guarantee under stated structural conditions. In controlled overload and inventory tasks, QPP reduces realized tail cost and constraint violations relative to mean-rollout planning. On overload, it remains competitive with sampled evaluators that use substantially more trajectories. These results position QPP as a transparent, budget-controlled approach to tail-risk evaluation.
Factor models are central to asset pricing and portfolio construction, but neural factors are often difficult to interpret, and their effect on portfolio risk is unclear. A sparse autoencoder (SAE) may address both concerns by describing daily returns across many assets with only a few active features. We ask whether this sparse representation produces stable financial factors and better-diversified portfolios. We study cryptocurrency returns, use equities as a secondary test, and compare the SAE with common benchmarks in a time-ordered evaluation. We find consistently lower volatility in SAE portfolios and link this result to lower correlations among the selected assets. However, the SAE does not outperform a simple contrarian strategy on risk-adjusted return, and its factor identities are not stable across training runs. Our contribution is to show that sparse coding can support portfolio diversification, while clearly separating this risk effect from claims about alpha or stable factor identities.
Temporal models that maintain per-sequence latent states during training must reconstruct those states when the stored table is unavailable at inference. This creates a basic consistency question: do the reconstructed states agree with those formed during training? We study this question for State Discovery in World Machine, which updates stored states in parallel each epoch but performs inference by sequential rollout from an all-zero start. On Toy1D, we analyze two paired-checkpoint runs whose decoded-state MSE is 27 to 36 percent above the published value. We compare the stored table with rollouts under the best-validation checkpoint reloaded after training and the last-epoch weights that wrote the table. Matching the rollout to the table-writing weights removes 90 to 91 percent of the mean per-dimension Wasserstein-1 distance. Causal distillation and frozen SD updates do not satisfy their pre-specified criteria under either checkpoint. Overall, checkpoint reload accounts for most of the measured gap in these runs.