Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even though many games feature complex 3D environments, 3D pathfinding remains a challenging problem. Representing large 3D maps can require a lot of memory, and pathfinding instances must be solved very quickly while the game is running. In this work we develop an efficient solution to 3D pathfinding by building a reduced, hierarchical grid representation within which we can extend traditional 2D navigation mesh (navmesh) pathing. Starting from an octree representation, we merge adjacent cells while preserving their convexity to obtain a coarser representation that greatly reduces path computation costs. We then build a navigation graph from this octree within which we can search for paths using the popular A* search algorithm. To increase the quality of the paths we obtain we implemented two forms of path refinement: a visibilitybased path pruning heuristic, and a 3D extension of the classic “funnel” algorithm that computes minimal homotopic paths. We further extend our work to handle dynamic environments with local and efficient updates to the octree and the movement graph. Experiments on a variety of scenarios show that our approach remains fast and efficient even for very large 3D maps and could be used for real-time pathfinding in video games. A detailed comparison with the state-of-the-art JPS-3D algorithm shows that our approach produces shorter path lengths while being faster on long path instances. We implemented our work in Unity, one of the most popular game engines, as an effort to make pathfinding in 3D environments accessible to game developers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it