Dynamic Abstractions: Building the Next Generation of Cognitive Tools and Interfaces
Bibliographic record
Abstract
This workshop provides a forum to discuss, brainstorm, and prototype the next generation of interfaces that leverage the dynamic experiences enabled by recent advances in AI and the generative capabilities of foundation models. These models simplify complex tasks by generating outputs in various representations (e.g., text, images, videos) through diverse input modalities like natural language, voice, and sketch. They interpret user intent to generate and transform representations, potentially changing how we interact with information and express ideas. Inspired by this potential, technologists, theorists, and researchers are exploring new forms of interaction by building demos and communities dedicated to concretizing and advancing the vision of working with dynamic abstractions. This UIST workshop provides a timely space to discuss AI’s impact on how we might design and use cognitive tools (e.g., languages, notations, diagrams). We will explore the challenges, critiques, and opportunities of this space by thinking through and prototyping use cases across various domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.034 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".