AI Tool Profile
MolmoMotion: access, code, weights, datasets, and benchmarks
MolmoMotion forecasts language-conditioned 3D point trajectories from short visual histories and selected object points.
Verification & Sources
- Evidence state
- Recheck due
- Source links
- 5
- Freshness
- Needs recheck: checked July 10, 2026
- Last updated
- July 10, 2026
What this evidence state means
- Definition
- The claim was previously checked, but its review window expired or a material change may have invalidated it.
- Required provenance
- The prior evidence and check date are retained, together with the expiry or change signal that triggered recheck.
- Owner
- Kingy freshness queue owner and assigned editorial reviewer
- Freshness rule
- This is already outside its freshness rule. It must not be presented as current until reviewed against current evidence.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
MolmoMotion is an open vision-language model from Ai2 for forecasting how selected points on objects will move in three dimensions. It takes a short visual history, user-selected query points, and a natural-language action description, then predicts future 3D point trajectories.
What it does
Ai2 released autoregressive and flow-matching variants for different forecasting needs. The release also includes MolmoMotion-1M, a dataset of action-described 3D trajectories, and PointMotionBench, a human-validated benchmark. Ai2 describes robotics planning and trajectory-conditioned video generation as possible downstream research uses.
Access and limitations
The model weights, dataset, benchmark, and code are published through Ai2, Hugging Face, and GitHub. The model card describes MolmoMotion as a research and educational release and advises validating predicted trajectories before using them to drive an actuated system.
Who it is for
MolmoMotion is intended for researchers and developers working on 3D motion forecasting, robotics planning, vision-language systems, and controllable video research.
Official sources
The Kingy Brief
Follow The Kingy Brief.
One consequential launch, one pricing, limit, or shutdown change, one hands-on test, one exact prompt or Test Pack, and one try / watch / skip verdict.
Free · Choose your subjects · Double opt-in · Unsubscribe anytime
Tool Links
Launch History
MolmoMotion
Ai2 released MolmoMotion models, code, MolmoMotion-1M data, and PointMotionBench for language-guided 3D motion forecasting.
- Launch readiness
- 8.3 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-10.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Ai2 released MolmoMotion as a complete open stack — models, code, the MolmoMotion-1M dataset, and PointMotionBench for language-guided 3D motion forecasting (allenai.org). AI Researchers,…