Multimodal Futures and Calibrated Uncertainty in JEPA Predictors

A deterministic predictor in embedding space collapses multiple valid futures into their average, which can be a state that never occurs. We study whether a JEPA predictor can represent several futures, and whether its uncertainty is calibrated. We use simulated systems with known branching, and a language setting in which a prompt admits several correct continuations. A bimodal pilot shows the mixture interval attains its nominal coverage; the JEPA results are pending. Preprint. Results in this version come from small-scale pilot experiments; the full experimental protocol is described in the text and is pending.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-02
DOI
https://doi.org/10.5281/zenodo.23890661
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Multimodal Futures and Calibrated Uncertainty in JEPA Predictors

Dheiver Francisco Santos
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

Multimodal Futures and Calibrated Uncertainty in JEPA Predictors

Dheiver Francisco Santos
preprint en

Abstract

A deterministic predictor in embedding space collapses multiple valid futures into their average, which can be a state that never occurs. We study whether a JEPA predictor can represent several futures, and whether its uncertainty is calibrated. We use simulated systems with known branching, and a language setting in which a prompt admits several correct continuations. A bimodal pilot shows the mixture interval attains its nominal coverage; the JEPA results are pending. Preprint. Results in this version come from small-scale pilot experiments; the full experimental protocol is described in the text and is pending.

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
Multimodal Futures and Calibrated Uncertainty in JEPA Predictors — Dheiver Francisco Santos · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS