# Constraint-Safe Graph-Context Scoring for Stable Point-Feature Labels Under Text-Width and Accessibility-Inspired Profiles

arXiv: https://arxiv.org/abs/2609.19848 · HTML: https://mlsanity.com/paper/2609.19848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Temporal Map Labeling: A New Unified Framework with Experiments](https://mlsanity.com/paper/1609.06327/) (2016)
- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [An Algorithmic Study of Fully Dynamic Independent Sets for Map Labeling](https://mlsanity.com/paper/2002.07611/) (2020)
- [Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale](https://mlsanity.com/paper/2008.13556/) (2020)
- [Transitions in Dynamic Point Labeling](https://mlsanity.com/paper/2202.11562/) (2022)
- [An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models](https://mlsanity.com/paper/2211.17215/) (2022)
- [RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios](https://mlsanity.com/paper/2308.13540/) (2023)
- [AltGeoViz: Facilitating Accessible Geovisualization](https://mlsanity.com/paper/2406.13853/) (2024)

## Abstract (arXiv)

Point-feature label placement on interactive maps must reconcile geometric validity, display yield, local placement utility, and stability across camera motion. Accessibility and multilingual requirements further change label dimensions, yet algorithmic evaluations often collapse these concerns into overlap counts. We present LABELSENSE-Pilot, a reproducible prototype that generates eight compass candidates per feature, scores candidates with a multilayer perceptron over graph-context summaries, adds a previous-placement bonus, and selects a layout through mixed-integer optimization. The executed scorer is deliberately not described as a graph transformer. Every returned layout is checked for viewport containment, per-feature uniqueness, and pairwise clearance. Experiments use 2,500 airport coordinates and names spanning 155 countries, with country-grouped splits and generated density, camera, text-suffix, preference, and enlarged-font stressors. Across five seeds, LABELSENSE-Pilot displayed 85.62 percent of labels with 2.09 percent flicker and zero collisions. Versus a handcrafted-utility integer program, LABELSENSE-Pilot sacrificed 1.43 percentage points of display while reducing flicker by 12.04 points. Enlarged-box-aware layouts produced zero proxy violations, whereas standard geometry reevaluated at 1.5x violated 52.57 percent of selected placements. These results establish an auditable engineering trade-off, not human accessibility, multilingual usability, or preference. Official recent baselines and participant evidence remain required before submission.