ResearchResearch paper

Foresee uses generative previews to explain consequences and alternatives

Submitted October 12, Foresee combines step guidance, consequence previews, method comparison and cross-step access through rendering, image editing and video generation.

Why it matters

Interfaces should support understanding of choices, not just imitation of actions.

A closer look

A six-person formative study and 24-person main study cover painting, gameplay and cocktail making. Users sometimes ignored explanations or matched outcomes without understanding later implications.

Who this is for

Generative-UI, instructional-interaction and AR research teams

Original sources

Revisions & corrections (1)

— Directory-external research discovered this run; October 12 original submission verified and full-paper methods/limitations read. Historical backfill, not today's release; no general superiority claim.

Related events

Research

A second, independent VeriCUA turns GUI-task acceptance into active evidence gathering with abstention

A Google and University of Hong Kong team submitted a separate project also named VeriCUA on October 29. It inspects an environment or recording before constructing a task rubric, then gathers missing evidence through GUI, accessibility-tree, file and database reads, marking requirements met, unmet or unknown. In online experiments it reduces false positives against baselines on AndroidWorld, OSWorld and ScienceBoard.

Research

GUI‑JEPA replaces token-by-token action decoding with candidate-conditioned latent scoring

Submitted October 27, GUI‑JEPA targets single-step mobile GUI action selection. It transforms JEPA representations pretrained on unlabeled interface screenshots for each action and region candidate, then scores accessibility-tree candidates in one batch instead of asking a 7B vision-language model to decode an action token by token.

Research

TAM turns verified task advancement into online state and reusable operations for GUI agents

Submitted October 27, Task Advancement Memory (TAM) uses what has been reliably accomplished at two timescales: it maintains verified progress, remaining goals and visual evidence during a task, then extracts reusable operations from locally successful segments after the task, including segments inside an overall failed workflow.