Remark. Virtual logical depth and scaling [ftip-00KK]
✍️source

Zhu et al. vary virtual logical depth by reusing weights and report reasoning gains at nearly fixed parameter count in Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws[zhu2025virtualdepth]. Their results motivate a depth coordinate in a scaling study, while leaving knowledge capacity, optimization, and transfer dependent on the declared training and evaluation protocol.

Remark. Virtual logical depth and scaling [ftip-00KK]
✍️source

Reference. Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws [zhu2025virtualdepth]

Depth reuse and latent recurrence [ftip-00KG]

Definition. Computation-axis record [ftip-00KH]

Remark. Looped Transformers as a source result [ftip-00KI]

Remark. Parameter sharing and width tradeoffs [ftip-00KJ]

Remark. Virtual logical depth and scaling [ftip-00KK]
✍️source

Remark. Recirculation as an inference intervention [ftip-00KL]

Remark. Latent feedback with a preserved Transformer interface [ftip-00KM]

Remark. Latent prediction as a training intervention [ftip-00KN]

Example. Equal parameters do not fix effective depth [ftip-00KO]

Definition. Depth-reuse model-instance record [ftip-00KP]

Example. Fixed-recipe depth comparison [ftip-00KQ]

Remark. Scaling surfaces rather than a single law [ftip-00KR]

Remark. Architecture and training are separable records [ftip-00KS]

Remark. Depth reuse across passes, parameters, and tasks [ftip-00KT]