paper: Update space section

paper/paper.md

@@ -101,29 +101,33 @@ Mesa 3 provides both discrete (cell-based) and continuous space implementations.
 Example grid creation:
 
 ```python
-    grid = OrthogonalVonNeumannGrid((width, height), torus=False, random=model.random)
+   grid = OrthogonalVonNeumannGrid((width, height), torus=False, random=model.random)
 ```
 
-Mesa provides specialized agent classes for spatial interactions in the discrete spaces:
+In Mesa 3, specialized agent classes for spatial interactions in discrete spaces were added:
 
 - `FixedAgent`: Is assigned to a cell, can access this cell, but cannot move to another cell.
 - `CellAgent`: Can move between cells
 - `Grid2DMovingAgent`: Extends `CellAgent` with directional movement methods
 
-All discrete spaces support PropertyLayers - efficient numpy-based arrays for storing cell-level properties:
+All discrete spaces support PropertyLayers - efficient numpy-based arrays for storing cell-level properties. This newly added feature allows for agents to interact with spatial properties of the cell more easily:
 
 ```python
-    grid.create_property_layer("elevation", default_value=10)
-    high_ground = grid.elevation.select_cells(lambda x: x > 50)
+   grid.create_property_layer("elevation", default_value=10)
+   high_ground = grid.elevation.select_cells(lambda x: x > 50)
 ```
 
 For models where agents need to move continuously through space rather than between discrete locations, `ContinuousSpace` allows agents to occupy any coordinate within defined boundaries:
 
 ```python
-    space = ContinuousSpace(x_max, y_max, torus=True)
-    space.move_agent(agent, (new_x, new_y))
+   space = ContinuousSpace(x_max, y_max, torus=True)
+   space.move_agent(agent, (new_x, new_y))
+```
+The space module is stable but under active development. The new cell-based spaces in Mesa 3 are currently being tested and considered feature-complete. The code snippets reflected in this paper are the future expected state of Mesa. Features not yet merged into core are imported from experimental:
+
+```python
+from mesa.experimental.cell_space ...
 ```
-The new cell-based spaces are feature complete but still in experimental state. A new ContinuousSpace implementation is in active development.
 
 ### Time advancement
 Typically, ABMs rely on incremental time progression or ticks. For each tick, the step method of the model is called. This activates the agents in some way. The most frequently implemented approach is shown below, which runs a model for 100 ticks.