Twitter/X

Randall Balestr argues that in asymmetric prediction settings like forecasting…

2025-11-21 · 18:14 UTC ·@randall_balestr ·0 min read

Brief

Randall Balestr makes a modeling point about sequential prediction: when the transition from one state to the next is explained by observed actions, those actions can serve as the conditioning signal instead of a latent variable. He says latent variables are mainly helpful when part of the transition is hidden or inherently stochastic.

Why it matters

Randall Balestr argues that in asymmetric prediction settings like forecasting the next frame from the current frame, observed actions that explain the transition can be used to condition the predictor directly, eliminating the need for a latent variable.

Key details

  • He claims latent variables become useful when the transition includes unobserved actions or genuine stochasticity, because the predictor otherwise lacks the information needed to explain frame-to-frame changes.
Source evidence

title: @randallbalestr: if you have asymmetry (e.g. next frame from current frame) and actions (explaining the transition be...
author: @randallbalestr
contenttype: tweet
publication: Twitter/X
published: 2025-11-21T18:14:07+00:00
sourceurl: https://x.com/randall_balestr/status/1991933157076316183

word_count: 44

if you have asymmetry (e.g. next frame from current frame) and actions (explaining the transition between frames) then you can condition the predictor on those actions without a latent variable. If you have unobserved action/stochastic transition then it may be useful to have it