2026-10-26 (~3:20 AM ET — x.com/ylecun): the planning-representation watch gets a research datapoint — work LeCun co-authored introduces Temporal Straightening: a world model's internal representations can form curved paths making distance-to-goal misleading; training encourages straighter internal paths, yielding more stable gradient-based planning and higher success rates on goal-reaching tasks, code released. Third-party-amplified research (Simplifying AI explainer RT'd by LeCun), not a lab product — sits beside the MotionJEPA entry (Sept 24) in the V-JEPA lineage. Watch: paper venue/evals, adoption beyond the authors.
2026-10-16 (~3:24 PM ET, LeCun RT of NYU Center for Data Science — x.com/ylecun): AdaJEPA — NYU CDS PhD student Ying Wang with LeCun and Mengye Ren — a world model that "keeps learning after training ends," built "to adapt during planning" (Medium write-up), the JEPA lineage's continual-learning turn where it meets the Oak Lab thesis. Academic release, not a lab announcement; no benchmark claims graded in the window.
2026-10-10→11 (RT layer, via LeCun/Zholus): RoboJEPA — "the first scaling law for multi-embodiment world models"; JEPA
predictors scaled to 8B, curves extrapolate, compute correlates with planning performance; all checkpoints released
(x.com/ylecun). Development of the spatial-intelligence-as-base-category layer (the JEPA
stack cadence, Oct 10). Single-thread claim family; watch the paper artifact + whether the scaling law reproduces.
October 2026 · 2 updates
2026-10-07→09 (X layer, ai-social dig): the JEPA stack runs a
paper-cadence — six more entries in three days on LeCun's feed: an
alphaXiv page for H-JEPA with LeCun named advisor; RoboJEPA (8B
— "robot world models now have scaling laws," the lineage's first
scaling claim); LeAVJEPA (sound+vision self-supervised, shared
encoder); DSReg (provable recovery of individual latent
variables — the theory answer to why latent prediction works);
financial world models (Balestriero, NeurIPS — "financial data
is RIPE for JEPAs"); LeWAM (Oct 9) — JEPAs that "jointly
imagine what happens and what to do next": claimed 32.3× faster
planning, 19 ms/plan, success 28.6%→89.7% on contact-rich
manipulation. From critique-proof demos toward scaling-law and
theory papers — the consolidation move the transformer era made in
2017–2020. Academic/vendor-adjacent (LeCun-amplified by
construction); no independent replications in feed. ELLIS's
world-models sessions and the Jordan Libération interview are the
community layer. Watch: replications of the AntMaze/LeWAM numbers,
wire-grade pickup of the scaling-law claim.
2026-10-06 (X layer): H-JEPA (Balestriero, LeCun RT;
arXiv 2610.06805): "the first end-to-end learned hierarchical
world model for long-horizon visual planning," atop SIGReg/LeWM
— the academic JEPA layer thickens again after MotionJEPA and
AD-E2E-JEPA; no benchmark claims graded.
September 2026 · 2 updates
2026-09-29 (X layer, ai-social dig): AD-E2E-JEPA (arXiv
2609.34085; Haoran Zhu, joint work with LeCun and Choromanska,
~11:49 AM ET): a JEPA world model for end-to-end
autonomous driving — "can a world model drive without training a
driving policy?" The JEPA lineage's driving-native instantiation,
with LeCun's own name on the author line, six days after MotionJEPA
added a second academic entry to the same lineage.
2026-09-24 (ai-social dig): MotionJEPA released (Markus Karmann
RT, project page): "JEPA-style world models are biased towards
learning slow, simple features" — MotionJEPA built for a balanced
static+dynamic representation. Independent academic work in the
V-JEPA lineage, not a lab announcement; no benchmark claims to
grade yet. Watch for adoption beyond the authors.