diff --git a/.github/workflows/python_tests.yml b/.github/workflows/python_tests.yml
index 6e48461..979ccce 100644
--- a/.github/workflows/python_tests.yml
+++ b/.github/workflows/python_tests.yml
@@ -27,10 +27,11 @@ jobs:
         pip install ".[test]"
     - name: Run the tests (Linux/macOS)
       if: runner.os != 'Windows'
+      shell: bash
       run: |
         echo -e '## Pytest Results\n\n' >> "$GITHUB_STEP_SUMMARY"
         echo -e '```' >> "$GITHUB_STEP_SUMMARY"
-        pytest -v --cov=beamfit/ tests 2>&1 | tee -a "$GITHUB_STEP_SUMMARY"
+        pytest -v --cov=paretobench tests 2>&1 | tee -a "$GITHUB_STEP_SUMMARY"
         echo -e '```' >> "$GITHUB_STEP_SUMMARY"
     - name: Run the tests (Windows)
       if: runner.os == 'Windows'
diff --git a/src/paretobench/containers.py b/src/paretobench/containers.py
index 94a5241..bdbe082 100644
--- a/src/paretobench/containers.py
+++ b/src/paretobench/containers.py
@@ -1,7 +1,7 @@
 from datetime import datetime, timezone
 from functools import reduce
 from pydantic import BaseModel, Field, field_validator, ConfigDict, model_validator
-from typing import List, Dict, Union, Optional, Literal, Tuple, TYPE_CHECKING
+from typing import Literal, TYPE_CHECKING
 import h5py
 import numpy as np
 import random
@@ -15,6 +15,34 @@
     from .problem import Problem
 
 
+def _validate_1d_float_array(value, name: str, expected_len: int, expected_len_desc: str):
+    """
+    Checks that a field is a 1D float64 numpy array of the expected length.
+
+    Parameters
+    ----------
+    value : Any
+        The value of the field being validated.
+    name : str
+        Name of the field, used in the error messages.
+    expected_len : int
+        The length the array must have.
+    expected_len_desc : str
+        Human readable description of where the expected length comes from, used in the error message.
+    """
+    if not isinstance(value, np.ndarray):
+        raise ValueError(f"{name} must be a numpy array, got: {type(value)}")
+    if len(value.shape) != 1:
+        raise ValueError(f"{name} must be 1D, shape was: {value.shape}")
+    if len(value) != expected_len:
+        raise ValueError(
+            f"Length of {name} must match {expected_len_desc}. Got {len(value)} elements but "
+            f"{expected_len_desc} is {expected_len}"
+        )
+    if value.dtype != np.float64:
+        raise ValueError(f"{name} dtype must be {np.float64}. Got {value.dtype}")
+
+
 class Population(BaseModel):
     """
     Stores the individuals in a population for one reporting interval in a genetic algorithm. Conventional names are used for
@@ -28,6 +56,9 @@ class Population(BaseModel):
     to an objectve and '+' means maximize with '-' meaning minimize. The constraints are configured by the string of directions
     `constraint_directions` and the numpy array of targets `constraint_targets`. The string should contain either the '<' or '>'
     character for the constraint at that index to be satisfied when it is less than or greater than the target respectively.
+
+    The rectangular bounds the decision variables were drawn from are recorded in `var_lower_bounds` and `var_upper_bounds`.
+    When they are not specified, the variables are treated as unbounded and the arrays are filled with -inf and +inf.
     """
 
     # The decision vars, objectives, and constraints
@@ -37,10 +68,11 @@ class Population(BaseModel):
 
     # Total number of function evaluations performed during optimization after this population was completed
     fevals: int
+
     # Optional lists of names for decision variables, objectives, and constraints
-    names_x: Optional[List[str]] = None
-    names_f: Optional[List[str]] = None
-    names_g: Optional[List[str]] = None
+    names_x: list[str] | None = None
+    names_f: list[str] | None = None
+    names_g: list[str] | None = None
 
     # Configuration of objectives/constraints (minimization or maximization problem, direction of and target of constraint)
     obj_directions: str  # '+' means maximize, '-' means minimize
@@ -49,6 +81,10 @@ class Population(BaseModel):
     )
     constraint_targets: np.ndarray
 
+    # Rectangular bounds of the decision variables (-inf / +inf when unbounded)
+    var_lower_bounds: np.ndarray
+    var_upper_bounds: np.ndarray
+
     # Pydantic config
     model_config = ConfigDict(arbitrary_types_allowed=True, validate_assignment=True)
 
@@ -58,7 +94,8 @@ def set_default_vals(cls, values):
         """
         Handles automatic setting of `x`, `f`,  `g`, `fevals` when some are not specified. The arrays are set to an empty array
         with a zero length non-batch dimension. The number of function evaluations (`fevals`) is set to the number of individuals
-        in the population (assuming here that each was evaluated to get to this point).
+        in the population (assuming here that each was evaluated to get to this point). Unspecified decision variable bounds
+        are set to -inf / +inf.
         """
         # Determine the batch size from the first non-None array
         batch_size = next(
@@ -84,6 +121,12 @@ def set_default_vals(cls, values):
         if values.get("constraint_targets") is None:
             values["constraint_targets"] = np.zeros(values["g"].shape[1], dtype=float)
 
+        # Treat the decision variables as unbounded if no bounds were given
+        if values.get("var_lower_bounds") is None:
+            values["var_lower_bounds"] = np.full(values["x"].shape[1], -np.inf)
+        if values.get("var_upper_bounds") is None:
+            values["var_upper_bounds"] = np.full(values["x"].shape[1], np.inf)
+
         # Set fevals to number of individuals if not included
         if values.get("fevals") is None:
             values["fevals"] = batch_size
@@ -140,19 +183,29 @@ def validate_constraint_directions(self):
 
     @model_validator(mode="after")
     def validate_constraint_targets(self):
-        # Check the targets
-        attr = "constraint_targets"
-        if not isinstance(getattr(self, attr), np.ndarray):
-            raise ValueError(f"{attr} must be a numpy array, got: {type(getattr(self, attr))}")
-        if len(getattr(self, attr).shape) != 1:
-            raise ValueError(f"{attr} must be 1D, shape was: {getattr(self, attr).shape}")
-        if len(getattr(self, attr)) != self.g.shape[1]:
+        _validate_1d_float_array(
+            self.constraint_targets, "constraint_targets", self.g.shape[1], "number of constraints in g"
+        )
+        return self
+
+    @model_validator(mode="after")
+    def validate_var_bounds(self):
+        """
+        Checks that the decision variable bounds are correctly sized and that each lower bound is at or below its
+        corresponding upper bound.
+        """
+        _validate_1d_float_array(
+            self.var_lower_bounds, "var_lower_bounds", self.x.shape[1], "number of decision variables in x"
+        )
+        _validate_1d_float_array(
+            self.var_upper_bounds, "var_upper_bounds", self.x.shape[1], "number of decision variables in x"
+        )
+        bad_idx = np.nonzero(self.var_lower_bounds > self.var_upper_bounds)[0]
+        if bad_idx.size:
             raise ValueError(
-                f"Length of {attr} must match number of constraints in g. Got {len(getattr(self, attr))} "
-                f"elements and {self.g.shape[1]} constraints from g"
+                f"var_lower_bounds must be less than or equal to var_upper_bounds. Got lower > upper at indices "
+                f"{bad_idx.tolist()}"
             )
-        if getattr(self, attr).dtype != np.float64:
-            raise ValueError(f"{attr} dtype must be {np.float64}. Got {getattr(self, attr).dtype}")
         return self
 
     @field_validator("x", "f", "g")
@@ -189,6 +242,8 @@ def __eq__(self, other):
             and self.obj_directions == other.obj_directions
             and self.constraint_directions == other.constraint_directions
             and np.array_equal(self.constraint_targets, other.constraint_targets)
+            and np.array_equal(self.var_lower_bounds, other.var_lower_bounds)
+            and np.array_equal(self.var_upper_bounds, other.var_upper_bounds)
         )
 
     @property
@@ -228,6 +283,10 @@ def __add__(self, other: "Population") -> "Population":
             raise ValueError("constraint_directions are inconsistent between populations")
         if not np.array_equal(self.constraint_targets, other.constraint_targets):
             raise ValueError("constraint_targets are inconsistent between populations")
+        if not np.array_equal(self.var_lower_bounds, other.var_lower_bounds):
+            raise ValueError("var_lower_bounds are inconsistent between populations")
+        if not np.array_equal(self.var_upper_bounds, other.var_upper_bounds):
+            raise ValueError("var_upper_bounds are inconsistent between populations")
 
         # Concatenate the arrays along the batch dimension (axis=0)
         new_x = np.concatenate((self.x, other.x), axis=0)
@@ -255,9 +314,11 @@ def __add__(self, other: "Population") -> "Population":
             obj_directions=self.obj_directions,
             constraint_directions=self.constraint_directions,
             constraint_targets=self.constraint_targets,
+            var_lower_bounds=self.var_lower_bounds,
+            var_upper_bounds=self.var_upper_bounds,
         )
 
-    def __getitem__(self, idx: Union[slice, np.ndarray, List[int]]) -> "Population":
+    def __getitem__(self, idx: slice | np.ndarray | list[int]) -> "Population":
         """
         Indexing operator to select along the batch dimension in the arrays.
 
@@ -282,6 +343,8 @@ def __getitem__(self, idx: Union[slice, np.ndarray, List[int]]) -> "Population":
             obj_directions=self.obj_directions,
             constraint_directions=self.constraint_directions,
             constraint_targets=self.constraint_targets,
+            var_lower_bounds=self.var_lower_bounds,
+            var_upper_bounds=self.var_upper_bounds,
         )
 
     def get_nondominated_indices(self):
@@ -308,6 +371,7 @@ def from_random(
         fevals: int = 0,
         generate_names: bool = False,
         generate_obj_constraint_settings: bool = False,
+        generate_bounds: bool = False,
     ) -> "Population":
         """
         Generate a randomized instance of the Population class.
@@ -328,6 +392,8 @@ def from_random(
             Whether to include names for the decision variables, objectives, and constraints, by default False.
         generate_obj_constraint_settings : bool, optional
             Randomize the objective and constraint settings, default to minimization problem and g >= 0 constraint
+        generate_bounds : bool, optional
+            Randomize the decision variable bounds, by default the variables are left unbounded
 
         Returns
         -------
@@ -371,6 +437,14 @@ def from_random(
             constraint_directions = None
             constraint_targets = None
 
+        # Create randomized bounds which contain the decision variables
+        if generate_bounds:
+            var_lower_bounds = -np.random.rand(n_decision_vars)
+            var_upper_bounds = 1.0 + np.random.rand(n_decision_vars)
+        else:
+            var_lower_bounds = None
+            var_upper_bounds = None
+
         return cls(
             x=x,
             f=f,
@@ -382,6 +456,8 @@ def from_random(
             obj_directions=obj_directions,
             constraint_directions=constraint_directions,
             constraint_targets=constraint_targets,
+            var_lower_bounds=var_lower_bounds,
+            var_upper_bounds=var_upper_bounds,
         )
 
     def __len__(self):
@@ -448,19 +524,19 @@ def plot_obj_scatter(
         domination_filt: Literal["all", "dominated", "non-dominated"] = "all",
         feasibility_filt: Literal["all", "feasible", "infeasible"] = "all",
         show_points: bool = True,
-        problem: Optional[Union[str, "Problem"]] = None,
+        problem: "str | Problem | None" = None,
         n_pf: int = 1000,
-        pf_objectives: Optional[np.ndarray] = None,
+        pf_objectives: np.ndarray | None = None,
         show_attainment: bool = False,
         show_dominated_area: bool = False,
-        dominated_area_zorder: Optional[int] = -2,
-        ref_point: Optional[Tuple[float, float]] = None,
+        dominated_area_zorder: int | None = -2,
+        ref_point: tuple[float, float] | None = None,
         ref_point_padding: float = 0.05,
-        label: Optional[str] = None,
-        legend_loc: Optional[str] = None,
+        label: str | None = None,
+        legend_loc: str | None = None,
         show_names: bool = True,
-        color: Optional[str] = None,
-        scale: Optional[np.ndarray] = None,
+        color: str | None = None,
+        scale: np.ndarray | None = None,
         flip_objs: bool = False,
     ):
         """
@@ -491,7 +567,7 @@ def plot_obj_scatter(
             Plots the dominated region towards the larger values of each decision var
         dominated_area_zorder : int, optional
             What "zorder" to draw dominated region at. Mostly used internally to correctly show dominated area in history plots.
-        ref_point : Union[str, Tuple[float, float]], optional
+        ref_point : str | tuple[float, float], optional
             Where to stop plotting the dominated region / attainment surface. Must be a point to the upper right (increasing
             value of objectives in 3D) of all plotted points. By default, will set to right of max of each objective plus
             padding.
@@ -543,18 +619,19 @@ def plot_obj_scatter(
 
     def plot_dvar_pairs(
         self,
-        dvars: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
+        dvars: int | slice | list[int] | tuple[int, int] | None = None,
         fig=None,
         axes=None,
         domination_filt: Literal["all", "dominated", "non-dominated"] = "all",
         feasibility_filt: Literal["all", "feasible", "infeasible"] = "all",
-        hist_bins: Optional[int] = None,
+        hist_bins: int | None = None,
         show_names: bool = True,
-        problem: Optional[Union[str, "Problem"]] = None,
-        lower_bounds: Optional[np.ndarray] = None,
-        upper_bounds: Optional[np.ndarray] = None,
-        color: Optional[str] = None,
-        scale: Optional[np.ndarray] = None,
+        problem: "str | Problem | None" = None,
+        lower_bounds: np.ndarray | None = None,
+        upper_bounds: np.ndarray | None = None,
+        color: str | None = None,
+        scale: np.ndarray | None = None,
+        plot_bounds: bool = True,
     ):
         """
         Creates a pairs plot (scatter matrix) showing correlations between decision variables
@@ -562,7 +639,7 @@ def plot_dvar_pairs(
 
         Parameters
         ----------
-        dvars : int, slice, List[int], or Tuple[int, int], optional
+        dvars : int, slice, list[int], or tuple[int, int], optional
             Specifies which decision variables to plot. See `selection_to_indices` for more details.
         fig : matplotlib.figure.Figure, optional
             Figure to plot on. If None and axes is None, creates a new figure.
@@ -580,14 +657,16 @@ def plot_dvar_pairs(
         problem : str/Problem, optional
             The problem for plotting decision variable bounds
         lower_bounds : array-like, optional
-            Lower bounds for each decision variable
+            Lower bounds for each decision variable. Defaults to the bounds carried by the population.
         upper_bounds : array-like, optional
-            Upper bounds for each decision variable
+            Upper bounds for each decision variable. Defaults to the bounds carried by the population.
         color : str, optional
             What color should we use for the points. Defaults to selecting from matplotlib color cycler
         scale : array-like, optional
             Scale factors for each variable. Must have the same length as the number of decision vars.
             If None, no scaling is applied.
+        plot_bounds : bool, optional
+            Whether to plot the decision variable bounds, by default True. Infinite bounds are not plotted.
 
         Returns
         -------
@@ -611,6 +690,7 @@ def plot_dvar_pairs(
             upper_bounds=upper_bounds,
             color=color,
             scale=scale,
+            plot_bounds=plot_bounds,
         )
 
 
@@ -624,9 +704,9 @@ class History(BaseModel):
      - Objective/constraint settings and names, if used, must be consistent across populations
     """
 
-    reports: List[Population]
+    reports: list[Population]
     problem: str
-    metadata: Dict[str, Union[str, int, float, bool]] = Field(default_factory=dict)
+    metadata: dict[str, str | int | float | bool] = Field(default_factory=dict)
 
     @model_validator(mode="after")
     def validate_consistent_populations(self):
@@ -668,6 +748,14 @@ def validate_consistent_populations(self):
         if constraint_targets and len(set(constraint_targets)) != 1:
             raise ValueError(f"Inconsistent constraint_targets in reports: {constraint_targets}")
 
+        # Check the decision variable bounds
+        var_lower_bounds = [tuple(x.var_lower_bounds) for x in self.reports]
+        var_upper_bounds = [tuple(x.var_upper_bounds) for x in self.reports]
+        if var_lower_bounds and len(set(var_lower_bounds)) != 1:
+            raise ValueError(f"Inconsistent var_lower_bounds in reports: {var_lower_bounds}")
+        if var_upper_bounds and len(set(var_upper_bounds)) != 1:
+            raise ValueError(f"Inconsistent var_upper_bounds in reports: {var_upper_bounds}")
+
         return self
 
     def __eq__(self, other):
@@ -685,6 +773,7 @@ def from_random(
         pop_size: int,
         generate_names: bool = False,
         generate_obj_constraint_settings: bool = False,
+        generate_bounds: bool = False,
     ) -> "History":
         """
         Generate a randomized instance of the History class, including random problem name and metadata.
@@ -705,6 +794,8 @@ def from_random(
             Whether to include names for the decision variables, objectives, and constraints, by default False.
         generate_obj_constraint_settings : bool, optional
             Randomize the objective and constraint settings, default to minimization problem and g >= 0 constraint
+        generate_bounds : bool, optional
+            Randomize the decision variable bounds, by default the variables are left unbounded
 
         Returns
         -------
@@ -748,6 +839,14 @@ def from_random(
                 report.constraint_directions = constraint_directions
                 report.constraint_targets = constraint_targets
 
+        # Create randomized decision variable bounds (must be consistent between objects)
+        if generate_bounds:
+            var_lower_bounds = -np.random.rand(n_decision_vars)
+            var_upper_bounds = 1.0 + np.random.rand(n_decision_vars)
+            for report in reports:
+                report.var_lower_bounds = var_lower_bounds
+                report.var_upper_bounds = var_upper_bounds
+
         return cls(reports=reports, problem=problem, metadata=metadata)
 
     def _to_h5py_group(self, g: h5py.Group):
@@ -790,6 +889,8 @@ def _to_h5py_group(self, g: h5py.Group):
 
         # Save the configuration data
         if self.reports:
+            g["x"].attrs["lower_bounds"] = self.reports[0].var_lower_bounds
+            g["x"].attrs["upper_bounds"] = self.reports[0].var_upper_bounds
             g["f"].attrs["directions"] = self.reports[0].obj_directions
             g["g"].attrs["directions"] = self.reports[0].constraint_directions
             g["g"].attrs["targets"] = self.reports[0].constraint_targets
@@ -819,6 +920,10 @@ def _from_h5py_group(cls, grp: h5py.Group, file_version: str):
         constraint_directions = grp["g"].attrs.get("directions", None)
         constraint_targets = grp["g"].attrs.get("targets", None)
 
+        # Files written before version 1.2.0 have no decision variable bounds and are treated as unbounded
+        var_lower_bounds = grp["x"].attrs.get("lower_bounds", None)
+        var_upper_bounds = grp["x"].attrs.get("upper_bounds", None)
+
         # Before file version 1.1.0 which introduced explicit constraint directions,
         # the default constraint type was g(x) >= 0.0
         if file_version == "1.0.0":
@@ -845,6 +950,8 @@ def _from_h5py_group(cls, grp: h5py.Group, file_version: str):
                     obj_directions=obj_directions,
                     constraint_directions=constraint_directions,
                     constraint_targets=constraint_targets,
+                    var_lower_bounds=var_lower_bounds,
+                    var_upper_bounds=var_upper_bounds,
                 )
             )
             start_idx += pop_size
@@ -883,27 +990,27 @@ def pf_reduce(a, b):
 
     def plot_obj_scatter(
         self,
-        reports: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
+        reports: int | slice | list[int] | tuple[int, int] | None = None,
         fig=None,
         ax=None,
         domination_filt: Literal["all", "dominated", "non-dominated"] = "all",
         feasibility_filt: Literal["all", "feasible", "infeasible"] = "all",
         show_points: bool = True,
         n_pf: int = 1000,
-        pf_objectives: Optional[np.ndarray] = None,
+        pf_objectives: np.ndarray | None = None,
         show_attainment: bool = False,
         show_dominated_area: bool = False,
-        ref_point: Optional[Tuple[float, float]] = None,
+        ref_point: tuple[float, float] | None = None,
         ref_point_padding: float = 0.05,
-        legend_loc: Optional[str] = None,
-        scale: Optional[np.ndarray] = None,
+        legend_loc: str | None = None,
+        scale: np.ndarray | None = None,
         flip_objs: bool = False,
         show_names: bool = True,
         show_pf: bool = False,
         colormap: str = "viridis",
-        cmap_label: Optional[str] = None,
+        cmap_label: str | None = None,
         generation_mode: Literal["cmap", "cumulative"] = "cmap",
-        single_color: Optional[str] = None,
+        single_color: str | None = None,
         label_mode: Literal["index", "fevals"] = "index",
     ):
         """
@@ -912,7 +1019,7 @@ def plot_obj_scatter(
 
         Parameters
         ----------
-        reports : int, slice, List[int], or Tuple[int, int], optional
+        reports : int, slice, list[int], or tuple[int, int], optional
             Specifies which generations to plot. See `selection_to_indices` for more details.
         fig : matplotlib figure, optional
             Figure to plot on, by default None
@@ -933,7 +1040,7 @@ def plot_obj_scatter(
             Whether to plot the attainment surface, by default False
         show_dominated_area : bool, optional
             Plots the dominated region towards the larger values of each decision var
-        ref_point : Union[str, Tuple[float, float]], optional
+        ref_point : str | tuple[float, float], optional
             Where to stop plotting the dominated region / attainment surface. Must be a point to the upper right (increasing
             value of objectives in 3D) of all plotted points. By default, will set to right of max of each objective plus
             padding.
@@ -952,13 +1059,13 @@ def plot_obj_scatter(
             Whether to plot the Pareto front, by default True
         colormap : str, optional
             Name of the colormap to use for generation colors, by default 'viridis'
-        cmap_label: Optional[str] = "Generation"
+        cmap_label: str | None = "Generation"
             Label for colorbar (only used when generation_mode is 'cmap')
         generation_mode: Literal['cmap', 'cumulative'] = 'cmap'
             How to handle multiple generations:
             'cmap': Plot each generation separately with colors from colormap
             'cumulative': Merge all selected generations into single population
-        single_color: Optional[str] = None
+        single_color: str | None = None
             Color to use when generation_mode is 'cumulative'. If None, uses default color from matplotlib.
         label_mode: Literal['index', 'fevals'] = 'index'
             Whether to use report index or function evaluations (fevals) for labels
@@ -998,21 +1105,21 @@ def plot_obj_scatter(
 
     def plot_dvar_pairs(
         self,
-        reports: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
-        dvars: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
+        reports: int | slice | list[int] | tuple[int, int] | None = None,
+        dvars: int | slice | list[int] | tuple[int, int] | None = None,
         fig=None,
         axes=None,
         domination_filt: Literal["all", "dominated", "non-dominated"] = "all",
         feasibility_filt: Literal["all", "feasible", "infeasible"] = "all",
-        hist_bins: Optional[int] = None,
+        hist_bins: int | None = None,
         show_names: bool = True,
-        lower_bounds: Optional[np.ndarray] = None,
-        upper_bounds: Optional[np.ndarray] = None,
-        scale: Optional[np.ndarray] = None,
+        lower_bounds: np.ndarray | None = None,
+        upper_bounds: np.ndarray | None = None,
+        scale: np.ndarray | None = None,
         colormap: str = "viridis",
-        cmap_label: Optional[str] = None,
+        cmap_label: str | None = None,
         generation_mode: Literal["cmap", "cumulative"] = "cmap",
-        single_color: Optional[str] = None,
+        single_color: str | None = None,
         plot_bounds: bool = False,
         label_mode: Literal["index", "fevals"] = "index",
     ):
@@ -1022,9 +1129,9 @@ def plot_dvar_pairs(
 
         Parameters
         ----------
-        reports : int, slice, List[int], or Tuple[int, int], optional
+        reports : int, slice, list[int], or tuple[int, int], optional
             Specifies which generations to plot. See `selection_to_indices` for more details.
-        dvars : int, slice, List[int], or Tuple[int, int], optional
+        dvars : int, slice, list[int], or tuple[int, int], optional
             Which decision vars to plot. See `population_dvar_pairs` docstring for more details.
         fig : matplotlib figure, optional
             Figure to plot on, by default None
@@ -1047,13 +1154,13 @@ def plot_dvar_pairs(
             If None, no scaling is applied.
         colormap : str, optional
             Name of the colormap to use for generation colors, by default 'viridis'
-        cmap_label: Optional[str] = "Generation"
+        cmap_label: str | None = "Generation"
             Label for colorbar (only used when generation_mode is 'cmap')
         generation_mode: Literal['cmap', 'cumulative'] = 'cmap'
             How to handle multiple generations:
             'cmap': Plot each generation separately with colors from colormap
             'cumulative': Merge all selected generations into single population
-        single_color: Optional[str] = None
+        single_color: str | None = None
             Color to use when generation_mode is 'cumulative'. If None, uses default color from matplotlib.
         plot_bounds: bool = False
             Whether to plot bounds for the problem
@@ -1090,23 +1197,23 @@ def plot_dvar_pairs(
 
     def plot_obj_animation(
         self,
-        reports: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
+        reports: int | slice | list[int] | tuple[int, int] | None = None,
         interval: int = 200,
         domination_filt: Literal["all", "dominated", "non-dominated"] = "all",
         feasibility_filt: Literal["all", "feasible", "infeasible"] = "all",
         show_points: bool = True,
         n_pf: int = 1000,
-        pf_objectives: Optional[np.ndarray] = None,
+        pf_objectives: np.ndarray | None = None,
         show_attainment: bool = False,
         show_dominated_area: bool = False,
-        ref_point: Optional[Tuple[float, float]] = None,
+        ref_point: tuple[float, float] | None = None,
         ref_point_padding: float = 0.05,
-        legend_loc: Optional[str] = "upper right",
-        scale: Optional[np.ndarray] = None,
+        legend_loc: str | None = "upper right",
+        scale: np.ndarray | None = None,
         flip_objs: bool = False,
         show_names: bool = True,
         show_pf: bool = False,
-        single_color: Optional[str] = None,
+        single_color: str | None = None,
         dynamic_scaling: bool = False,
         cumulative: bool = False,
         scale_padding: float = 0.05,
@@ -1116,7 +1223,7 @@ def plot_obj_animation(
 
         Parameters
         ----------
-        reports : int, slice, List[int], or Tuple[int, int], optional
+        reports : int, slice, list[int], or tuple[int, int], optional
             Specifies which generations to animate. See `selection_to_indices` for more details.
         interval : int, optional
             Delay between frames in milliseconds, by default 200
@@ -1135,7 +1242,7 @@ def plot_obj_animation(
             Whether to plot the attainment surface, by default False
         show_dominated_area : bool, optional
             Plots the dominated region towards the larger values of each decision var
-        ref_point : Union[str, Tuple[float, float]], optional
+        ref_point : str | tuple[float, float], optional
             Where to stop plotting the dominated region / attainment surface. Must be a point to the upper right (increasing
             value of objectives in 3D) of all plotted points. By default, will set to right of max of each objective plus
             padding.
@@ -1152,7 +1259,7 @@ def plot_obj_animation(
             Whether to show the names of the objectives if provided by population
         show_pf : bool, optional
             Whether to plot the Pareto front, by default True
-        single_color: Optional[str] = None
+        single_color: str | None = None
             Color to use when generation_mode is 'cumulative'. If None, uses default color from matplotlib.
         dynamic_scaling : bool, optional
             If True, axes limits will update based on each frame's data.
@@ -1196,17 +1303,17 @@ def plot_obj_animation(
 
     def plot_dvar_animation(
         self,
-        reports: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
-        dvars: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
+        reports: int | slice | list[int] | tuple[int, int] | None = None,
+        dvars: int | slice | list[int] | tuple[int, int] | None = None,
         interval: int = 200,
         domination_filt: Literal["all", "dominated", "non-dominated"] = "all",
         feasibility_filt: Literal["all", "feasible", "infeasible"] = "all",
-        hist_bins: Optional[int] = None,
+        hist_bins: int | None = None,
         show_names: bool = True,
-        lower_bounds: Optional[np.ndarray] = None,
-        upper_bounds: Optional[np.ndarray] = None,
-        scale: Optional[np.ndarray] = None,
-        single_color: Optional[str] = None,
+        lower_bounds: np.ndarray | None = None,
+        upper_bounds: np.ndarray | None = None,
+        scale: np.ndarray | None = None,
+        single_color: str | None = None,
         plot_bounds: bool = False,
         dynamic_scaling: bool = False,
         cumulative: bool = False,
@@ -1217,9 +1324,9 @@ def plot_dvar_animation(
 
         Parameters
         ----------
-        reports : int, slice, List[int], or Tuple[int, int], optional
+        reports : int, slice, list[int], or tuple[int, int], optional
             Specifies which generations to animate. See `selection_to_indices` for more details.
-        dvars : int, slice, List[int], or Tuple[int, int], optional
+        dvars : int, slice, list[int], or tuple[int, int], optional
             Which decision vars to plot. See `population_dvar_pairs` docstring for more details.
         interval : int, optional
             Delay between frames in milliseconds, by default 200
@@ -1238,7 +1345,7 @@ def plot_dvar_animation(
         scale : array-like, optional
             Scale factors for each variable. Must have the same length as the number of decision vars.
             If None, no scaling is applied.
-        single_color: Optional[str] = None
+        single_color: str | None = None
             Color to use when generation_mode is 'cumulative'. If None, uses default color from matplotlib.
         plot_bounds: bool = False
             Whether to plot bounds for the problem
@@ -1335,14 +1442,14 @@ class Experiment(BaseModel):
     used to save the data.
     """
 
-    runs: List[History]
+    runs: list[History]
     name: str
     author: str = ""
     software: str = ""
     software_version: str = ""
     comment: str = ""
     creation_time: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
-    file_version: str = "1.1.0"
+    file_version: str = "1.2.0"
 
     def __eq__(self, other):
         if not isinstance(other, Experiment):
@@ -1384,6 +1491,7 @@ def from_random(
         pop_size: int,
         generate_names: bool = False,
         generate_obj_constraint_settings: bool = False,
+        generate_bounds: bool = False,
     ) -> "Experiment":
         """
         Generate a randomized instance of the Experiment class.
@@ -1406,6 +1514,8 @@ def from_random(
             Whether to include names for the decision variables, objectives, and constraints, by default False.
         generate_obj_constraint_settings : bool, optional
             Randomize the objective and constraint settings, default to minimization problem and g >= 0 constraint
+        generate_bounds : bool, optional
+            Randomize the decision variable bounds, by default the variables are left unbounded
 
         Returns
         -------
@@ -1422,6 +1532,7 @@ def from_random(
                 pop_size,
                 generate_names=generate_names,
                 generate_obj_constraint_settings=generate_obj_constraint_settings,
+                generate_bounds=generate_bounds,
             )
             for _ in range(n_histories)
         ]
@@ -1461,7 +1572,7 @@ def save(self, fname):
             f.attrs["software_version"] = self.software_version
             f.attrs["comment"] = self.comment
             f.attrs["creation_time"] = self.creation_time.isoformat()
-            f.attrs["file_version"] = "1.1.0"
+            f.attrs["file_version"] = "1.2.0"
             f.attrs["file_format"] = "ParetoBench Multi-Objective Optimization Data"
 
             # Calculate the necessary zero padding based on the number of runs
diff --git a/src/paretobench/ext/xopt.py b/src/paretobench/ext/xopt.py
index 0527a0a..58fc7e2 100644
--- a/src/paretobench/ext/xopt.py
+++ b/src/paretobench/ext/xopt.py
@@ -15,11 +15,24 @@
 # Handle xopt 2.x and 3.x constraint/objective accessor styles
 try:
     from xopt.vocs import GreaterThanConstraint
-    from gest_api.vocs import LessThanConstraint, MaximizeObjective, MinimizeObjective
+    from gest_api.vocs import (
+        ContinuousVariable,
+        DiscreteVariable,
+        LessThanConstraint,
+        MaximizeObjective,
+        MinimizeObjective,
+    )
 
     def _constraint_value(c):
         return c.value
 
+    def _variable_bounds(var):
+        if isinstance(var, ContinuousVariable):
+            return var.domain[0], var.domain[1]
+        elif isinstance(var, DiscreteVariable):
+            raise ValueError("DiscreteVariable is currently not supported by ParetoBench")
+        raise ValueError(f"Unrecognized variable type: {type(var)}")
+
     def _constraint_direction(c):
         if isinstance(c, GreaterThanConstraint):
             return ">"
@@ -39,6 +52,9 @@ def _objective_direction(obj):
     def _constraint_value(c):
         return c[1]
 
+    def _variable_bounds(var):
+        return var[0], var[1]
+
     def _constraint_direction(c):
         if c[0] == "GREATER_THAN":
             return ">"
@@ -160,6 +176,9 @@ def population_from_dataframe(df: pd.DataFrame, vocs: VOCS, errors_as_constraint
     Population
         Population object with the loaded data
     """
+    # Get the decision variable bounds. Note that vocs.bounds is not used here as its shape changed between xopt 2.x and 3.x
+    var_bounds = [_variable_bounds(vocs.variables[name]) for name in vocs.variable_names]
+
     # Get base constraints if they exist
     g = df[vocs.constraint_names].to_numpy() if vocs.constraints else None
     names_g = vocs.constraint_names
@@ -190,6 +209,8 @@ def population_from_dataframe(df: pd.DataFrame, vocs: VOCS, errors_as_constraint
         obj_directions="".join([_objective_direction(vocs.objectives[name]) for name in vocs.objective_names]),
         constraint_directions="".join(constraint_directions),
         constraint_targets=np.array(constraint_targets),
+        var_lower_bounds=np.array([b[0] for b in var_bounds], dtype=np.float64),
+        var_upper_bounds=np.array([b[1] for b in var_bounds], dtype=np.float64),
     )
 
 
diff --git a/src/paretobench/misc.py b/src/paretobench/misc.py
index 184ac37..f79a80c 100644
--- a/src/paretobench/misc.py
+++ b/src/paretobench/misc.py
@@ -204,7 +204,7 @@ def var_lower_bounds(self):
 
     @property
     def var_upper_bounds(self):
-        return np.array([[1.0, 5.0]])
+        return np.array([1.0, 5.0])
 
     @property
     def reference(self):
diff --git a/src/paretobench/plotting/history.py b/src/paretobench/plotting/history.py
index dd9746e..b65cfaa 100644
--- a/src/paretobench/plotting/history.py
+++ b/src/paretobench/plotting/history.py
@@ -298,6 +298,7 @@ def history_dvar_pairs(
         hist_bins=hist_bins,
         show_names=show_names,
         scale=scale,
+        plot_bounds=plot_bounds,
     )
 
     if generation_mode == "cumulative":
@@ -336,12 +337,14 @@ def history_dvar_pairs(
 
             # Only plot bounds on the last iteration if requested
             if plot_idx == len(indices) - 1:
+                plot_settings["plot_bounds"] = plot_bounds
                 if plot_bounds and user_specified_bounds:
                     plot_settings["lower_bounds"] = lower_bounds
                     plot_settings["upper_bounds"] = upper_bounds
                 elif plot_bounds and history.problem is not None:
                     plot_settings["problem"] = history.problem
             else:
+                plot_settings["plot_bounds"] = False
                 plot_settings["problem"] = None
                 plot_settings["lower_bounds"] = None
                 plot_settings["upper_bounds"] = None
diff --git a/src/paretobench/plotting/population.py b/src/paretobench/plotting/population.py
index fbd8ccd..4a0b6f2 100644
--- a/src/paretobench/plotting/population.py
+++ b/src/paretobench/plotting/population.py
@@ -8,8 +8,7 @@
 
 from ..containers import Population
 from ..exceptions import EmptyPopulationError, NoDecisionVarsError, NoObjectivesError
-from ..problem import Problem, ProblemWithFixedPF, ProblemWithPF
-from ..utils import get_problem_from_obj_or_str
+from ..problem import Problem, ProblemWithFixedPF, ProblemWithPF, get_problem_from_obj_or_str
 from .attainment import compute_attainment_surface_2d, compute_attainment_surface_3d
 from .utils import get_per_point_settings_population, alpha_scatter, selection_to_indices
 
@@ -291,6 +290,28 @@ def population_obj_scatter(
     return fig, ax
 
 
+def _draw_bound(line_fn, bounds, idx, scale, props):
+    """
+    Draws one decision variable bound onto an axis. Unset and infinite bounds are skipped as they have no location
+    on the plot and would ruin the axis limits.
+
+    Parameters
+    ----------
+    line_fn : callable
+        The axis method used to draw the line (`ax.axvline` or `ax.axhline`).
+    bounds : array-like or None
+        The lower or upper bounds of all decision variables.
+    idx : int
+        Index of the decision variable being drawn.
+    scale : float
+        Scale factor applied to this decision variable.
+    props : dict
+        Line properties passed through to `line_fn`.
+    """
+    if bounds is not None and np.isfinite(bounds[idx]):
+        line_fn(scale * bounds[idx], **props)
+
+
 def population_dvar_pairs(
     population: Population,
     dvars: Optional[Union[int, slice, List[int], Tuple[int, int]]] = None,
@@ -305,6 +326,7 @@ def population_dvar_pairs(
     upper_bounds: Optional[np.ndarray] = None,
     color: Optional[str] = None,
     scale: Optional[np.ndarray] = None,
+    plot_bounds: bool = True,
 ):
     """
     Creates a pairs plot (scatter matrix) showing correlations between decision variables
@@ -332,14 +354,16 @@ def population_dvar_pairs(
     problem : str/Problem, optional
         The problem for plotting decision variable bounds
     lower_bounds : array-like, optional
-        Lower bounds for each decision variable
+        Lower bounds for each decision variable. Defaults to the bounds carried by the population.
     upper_bounds : array-like, optional
-        Upper bounds for each decision variable
+        Upper bounds for each decision variable. Defaults to the bounds carried by the population.
     color : str, optional
         What color should we use for the points. Defaults to selecting from matplotlib color cycler
     scale : array-like, optional
         Scale factors for each variable. Must have the same length as the number of decision vars.
         If None, no scaling is applied.
+    plot_bounds : bool, optional
+        Whether to plot the decision variable bounds, by default True. Infinite bounds are not plotted.
 
     Returns
     -------
@@ -367,10 +391,6 @@ def population_dvar_pairs(
     var_indices = np.array(selection_to_indices(dvars, population.n))
     n_vars = len(var_indices)
 
-    # Default, don't show bounds
-    lower_bounds = None
-    upper_bounds = None
-
     # Handle user specified problem
     if problem is not None:
         if (lower_bounds is not None) or (upper_bounds is not None):
@@ -383,6 +403,16 @@ def population_dvar_pairs(
         lower_bounds = problem.var_lower_bounds
         upper_bounds = problem.var_upper_bounds
 
+    # Fall back onto the bounds carried by the population itself
+    elif plot_bounds and (lower_bounds is None) and (upper_bounds is None):
+        lower_bounds = population.var_lower_bounds
+        upper_bounds = population.var_upper_bounds
+
+    # The bounds are not plotted when the user asks us not to
+    if not plot_bounds:
+        lower_bounds = None
+        upper_bounds = None
+
     # Validate and convert bounds to numpy arrays if provided
     if lower_bounds is not None:
         lower_bounds = np.asarray(lower_bounds)
@@ -484,10 +514,8 @@ def population_dvar_pairs(
                     base_color = patches[0].get_facecolor()
 
                 # Add vertical bound lines to histograms
-                if lower_bounds is not None:
-                    ax.axvline(scale[var_indices[i]] * lower_bounds[var_indices[i]], **bound_props)
-                if upper_bounds is not None:
-                    ax.axvline(scale[var_indices[i]] * upper_bounds[var_indices[i]], **bound_props)
+                _draw_bound(ax.axvline, lower_bounds, var_indices[i], scale[var_indices[i]], bound_props)
+                _draw_bound(ax.axvline, upper_bounds, var_indices[i], scale[var_indices[i]], bound_props)
 
             # Off-diagonal plots (scatter plots)
             else:
@@ -505,12 +533,10 @@ def population_dvar_pairs(
                     base_color = scatter.get_facecolor()[0]  # Get the color that matplotlib assigned
 
                 # Add bound lines to scatter plots
-                if lower_bounds is not None:
-                    ax.axvline(scale[var_indices[j]] * lower_bounds[var_indices[j]], **bound_props)  # x-axis bound
-                    ax.axhline(scale[var_indices[i]] * lower_bounds[var_indices[i]], **bound_props)  # y-axis bound
-                if upper_bounds is not None:
-                    ax.axvline(scale[var_indices[j]] * upper_bounds[var_indices[j]], **bound_props)  # x-axis bound
-                    ax.axhline(scale[var_indices[i]] * upper_bounds[var_indices[i]], **bound_props)  # y-axis bound
+                _draw_bound(ax.axvline, lower_bounds, var_indices[j], scale[var_indices[j]], bound_props)
+                _draw_bound(ax.axhline, lower_bounds, var_indices[i], scale[var_indices[i]], bound_props)
+                _draw_bound(ax.axvline, upper_bounds, var_indices[j], scale[var_indices[j]], bound_props)
+                _draw_bound(ax.axhline, upper_bounds, var_indices[i], scale[var_indices[i]], bound_props)
             if i == n_vars - 1:
                 ax.set_xlabel(var_names[j])
             if j == 0:
diff --git a/src/paretobench/problem.py b/src/paretobench/problem.py
index 2ce5fb3..ed39105 100644
--- a/src/paretobench/problem.py
+++ b/src/paretobench/problem.py
@@ -1,6 +1,7 @@
 import numpy as np
 from pydantic import BaseModel
 
+from .containers import Population
 from .exceptions import DeserializationError, InputError
 from .factory import create_problem
 from .simple_serialize import dumps, loads
@@ -45,8 +46,7 @@ def __call__(self, x: np.ndarray, check_bounds=True):
                 raise InputError(msg)
             if check_bounds and ((x > self.var_upper_bounds).all() or (x < self.var_lower_bounds).all()):
                 raise InputError("Input lies outside of problem bounds.")
-            pop = self._call(x[None, :])
-            pop.x = np.reshape(x, (1, -1))
+            x = np.reshape(x, (1, -1))
 
         # If batched input is used
         elif len(x.shape) == 2:
@@ -57,15 +57,23 @@ def __call__(self, x: np.ndarray, check_bounds=True):
                 raise InputError(msg)
             if check_bounds and ((x > self.var_upper_bounds).all() or (x < self.var_lower_bounds).all()):
                 raise InputError("Input lies outside of problem bounds.")
-            pop = self._call(x)
-            pop.x = x
 
         # If user provided something not usable
         else:
             raise ValueError(f"Incompatible shape of input array x: {x.shape}")
 
-        # Set the decision variables
-        return pop
+        # Attach the decision variables and the problem's bounds to the evaluated population. These must be set together
+        # because the bounds are validated against the number of decision variables, so assigning them one at a time
+        # leaves the population in a state which does not validate.
+        pop = self._call(x)
+        return Population.model_validate(
+            {
+                **dict(pop),
+                "x": x,
+                "var_lower_bounds": np.asarray(self.var_lower_bounds, dtype=np.float64),
+                "var_upper_bounds": np.asarray(self.var_upper_bounds, dtype=np.float64),
+            }
+        )
 
     def _call(self, x: np.ndarray):
         """
@@ -193,6 +201,33 @@ def __str__(self):
         return self.__repr__()
 
 
+def get_problem_from_obj_or_str(obj_or_str: "str | Problem") -> "Problem":
+    """Convert input to Problem instance.
+
+    Parameters
+    ----------
+    obj_or_str : Problem or str
+        Input to convert. If already a Problem instance, returns as-is.
+        If string, creates Problem from line format.
+
+    Returns
+    -------
+    Problem
+        The resulting Problem instance.
+
+    Raises
+    ------
+    ValueError
+        If input is neither Problem nor str type.
+    """
+    if isinstance(obj_or_str, Problem):
+        return obj_or_str
+    elif isinstance(obj_or_str, str):
+        return Problem.from_line_fmt(obj_or_str)
+    else:
+        raise ValueError(f"Unrecognized input type: {type(obj_or_str)}")
+
+
 class ProblemWithPF:
     """
     Mixin class for problems with a defined Pareto front where you can request a certain number of points from it.
diff --git a/src/paretobench/utils.py b/src/paretobench/utils.py
index 0bd135d..a722e56 100644
--- a/src/paretobench/utils.py
+++ b/src/paretobench/utils.py
@@ -1,10 +1,7 @@
 from itertools import combinations, chain, count
 from math import comb
-from typing import Union
 import numpy as np
 
-from .problem import Problem
-
 
 def get_betas(m, p):
     """
@@ -171,33 +168,6 @@ def weighted_chunk_sizes(n, weights):
     return ns
 
 
-def get_problem_from_obj_or_str(obj_or_str: Union[str, Problem]) -> Problem:
-    """Convert input to Problem instance.
-
-    Parameters
-    ----------
-    obj_or_str : Problem or str
-        Input to convert. If already a Problem instance, returns as-is.
-        If string, creates Problem from line format.
-
-    Returns
-    -------
-    Problem
-        The resulting Problem instance.
-
-    Raises
-    ------
-    ValueError
-        If input is neither Problem nor str type.
-    """
-    if isinstance(obj_or_str, Problem):
-        return obj_or_str
-    elif isinstance(obj_or_str, str):
-        return Problem.from_line_fmt(obj_or_str)
-    else:
-        raise ValueError(f"Unrecognized input type: {type(obj_or_str)}")
-
-
 def binary_str_to_numpy(ss, pos_char, neg_char):
     """
     Convert the characters of the string ss into a numpy array with +1 being wherever
diff --git a/tests/ext/test_xopt.py b/tests/ext/test_xopt.py
index ed1427d..0efd0d5 100644
--- a/tests/ext/test_xopt.py
+++ b/tests/ext/test_xopt.py
@@ -182,6 +182,11 @@ def df_comp(df1, df2):
                     assert all(tp.constraint_targets == [0.0, 0.0])
                     assert tp.fevals == (idx + 1) * population_size
 
+                    # Confirm the decision variable bounds came from the VOCS
+                    vocs_bounds = [_variable_bounds(tnk_vocs.variables[name]) for name in tnk_vocs.variable_names]
+                    np.testing.assert_allclose(tp.var_lower_bounds, [b[0] for b in vocs_bounds])
+                    np.testing.assert_allclose(tp.var_upper_bounds, [b[1] for b in vocs_bounds])
+
                     # Confirm data is correct
                     df_comp(rx, pd.DataFrame(tp.x, columns=tp.names_x))
                     rf.columns = [x.removeprefix("objective_") for x in rf.columns]
diff --git a/tests/generate_file_version_data.py b/tests/generate_file_version_data.py
index 18b38a9..1ebdce7 100644
--- a/tests/generate_file_version_data.py
+++ b/tests/generate_file_version_data.py
@@ -39,6 +39,7 @@ def make_experiment():
             pop_size=25,
             generate_names=True,
             generate_obj_constraint_settings=True,
+            generate_bounds=True,
         )
         run.problem = problem
         runs.append(run)
@@ -84,12 +85,19 @@ def main():
     )
     parser.add_argument("--out-dir", type=Path, default=DEFAULT_OUT_DIR, help="directory to save the file into")
     parser.add_argument("--force", action="store_true", help="overwrite an existing file for this version")
-    parser.add_argument("--manifest", type=Path, help="rewrite the manifest of an existing file and exit")
+    parser.add_argument(
+        "--refresh-manifests",
+        type=Path,
+        nargs="*",
+        help="rewrite the manifests of files which already exist, defaulting to every file in the output directory",
+    )
     args = parser.parse_args()
 
-    # Refresh the manifest of a file which already exists (used to bootstrap files saved by older versions)
-    if args.manifest is not None:
-        print(f"Wrote {write_manifest(args.manifest)}")
+    # Rewrite the manifests of files which already exist. Needed whenever the contents of a manifest change, such as
+    # when a new field is added to the containers, and to bootstrap the manifests of files saved by older versions.
+    if args.refresh_manifests is not None:
+        for path in args.refresh_manifests or sorted(args.out_dir.glob("*.h5")):
+            print(f"Wrote {write_manifest(path)}")
         return
 
     # Save the data, then name the file after the version which actually ended up in it
diff --git a/tests/test_containers.py b/tests/test_containers.py
index 8026072..7557eb5 100644
--- a/tests/test_containers.py
+++ b/tests/test_containers.py
@@ -1,4 +1,5 @@
 from pydantic import ValidationError
+import h5py
 import numpy as np
 import os
 import pytest
@@ -8,7 +9,8 @@
 
 
 @pytest.mark.parametrize("generate_names", [False, True])
-def test_experiment_save_load(generate_names):
+@pytest.mark.parametrize("generate_bounds", [False, True])
+def test_experiment_save_load(generate_names, generate_bounds):
     """
     Make a randomized experiment, save it to disk, load it, and then confirm everything matches.
     """
@@ -22,6 +24,7 @@ def test_experiment_save_load(generate_names):
         pop_size=50,
         generate_names=generate_names,
         generate_obj_constraint_settings=True,
+        generate_bounds=generate_bounds,
     )
 
     # Use a temporary directory to save the file
@@ -266,6 +269,117 @@ def test_field_assignment_validation():
         pop.x = np.random.random((2))
 
 
+def test_save_load_var_bounds_backwards_compatible():
+    """
+    Files written before the bounds were added (file version 1.2.0) load as unbounded populations.
+    """
+    experiment = Experiment.from_random(
+        n_histories=2,
+        n_populations=3,
+        n_objectives=2,
+        n_decision_vars=4,
+        n_constraints=1,
+        pop_size=8,
+        generate_bounds=True,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        file_path = os.path.join(tmpdir, "test.h5")
+        experiment.save(file_path)
+
+        # Strip the bounds attributes to imitate a file written by an older version of ParetoBench
+        with h5py.File(file_path, mode="r+") as f:
+            f.attrs["file_version"] = "1.1.0"
+            for run_grp in [f[k] for k in f if k.startswith("run_")]:
+                del run_grp["x"].attrs["lower_bounds"]
+                del run_grp["x"].attrs["upper_bounds"]
+
+        loaded_experiment = Experiment.load(file_path)
+        assert loaded_experiment.file_version == "1.1.0"
+        for run in loaded_experiment.runs:
+            for report in run.reports:
+                np.testing.assert_array_equal(report.var_lower_bounds, np.full(report.n, -np.inf))
+                np.testing.assert_array_equal(report.var_upper_bounds, np.full(report.n, np.inf))
+
+
+def test_save_load_no_decision_vars():
+    """
+    Populations without decision variables load with empty bounds arrays.
+    """
+    experiment = Experiment.from_random(
+        n_histories=1,
+        n_populations=2,
+        n_objectives=2,
+        n_decision_vars=0,
+        n_constraints=1,
+        pop_size=8,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        file_path = os.path.join(tmpdir, "test.h5")
+        experiment.save(file_path)
+        loaded_experiment = Experiment.load(file_path)
+
+    assert experiment == loaded_experiment
+    assert loaded_experiment.runs[0].reports[0].var_lower_bounds.shape == (0,)
+
+
+def test_default_var_bounds():
+    """
+    Populations created without bounds treat the decision variables as unbounded.
+    """
+    pop = Population(x=np.random.random((16, 4)), f=np.random.random((16, 2)))
+    np.testing.assert_array_equal(pop.var_lower_bounds, np.full(4, -np.inf))
+    np.testing.assert_array_equal(pop.var_upper_bounds, np.full(4, np.inf))
+
+    # The default bounds must survive the operations which pass them along
+    assert (pop + pop).var_lower_bounds.shape == (4,)
+    np.testing.assert_array_equal(pop[:4].var_upper_bounds, np.full(4, np.inf))
+    assert pop == pop[:]
+
+
+def test_var_bounds_validation():
+    """
+    Bounds must be sized to the decision variables and ordered lower <= upper.
+    """
+    x = np.random.random((16, 3))
+
+    with pytest.raises(ValidationError, match="Length of var_lower_bounds must match number of decision variables"):
+        Population(x=x, var_lower_bounds=np.zeros(2))
+
+    with pytest.raises(ValidationError, match="var_upper_bounds must be 1D"):
+        Population(x=x, var_upper_bounds=np.ones((1, 3)))
+
+    with pytest.raises(ValidationError, match=r"lower > upper at indices \[1\]"):
+        Population(x=x, var_lower_bounds=np.array([0.0, 1.0, 0.0]), var_upper_bounds=np.array([1.0, 0.0, 1.0]))
+
+    # Equal lower and upper bounds are allowed (degenerate variable)
+    Population(x=x, var_lower_bounds=np.zeros(3), var_upper_bounds=np.zeros(3))
+
+
+def test_var_bounds_consistency():
+    """
+    Adding populations requires matching bounds and carries them into the result.
+    """
+    kwargs = dict(f=np.random.random((16, 2)), var_lower_bounds=np.zeros(3), var_upper_bounds=np.ones(3))
+    pop1 = Population(x=np.random.random((16, 3)), **kwargs)
+    pop2 = Population(x=np.random.random((16, 3)), **kwargs)
+    np.testing.assert_array_equal((pop1 + pop2).var_upper_bounds, np.ones(3))
+
+    pop3 = Population(
+        x=np.random.random((16, 3)),
+        f=np.random.random((16, 2)),
+        var_lower_bounds=np.zeros(3),
+        var_upper_bounds=2 * np.ones(3),
+    )
+    with pytest.raises(ValueError, match="var_upper_bounds are inconsistent between populations"):
+        pop1 + pop3
+
+    # Histories must also have consistent bounds across their reports
+    with pytest.raises(ValidationError, match="Inconsistent var_upper_bounds in reports"):
+        History(reports=[pop1, pop3], problem="")
+
+
 def test_overwrite():
     # Create a randomized Experiment object
     experiment1 = Experiment.from_random(
diff --git a/tests/test_data/file_versions/paretobench_file_format_v1.0.0.json b/tests/test_data/file_versions/paretobench_file_format_v1.0.0.json
index e6fccae..5848279 100644
--- a/tests/test_data/file_versions/paretobench_file_format_v1.0.0.json
+++ b/tests/test_data/file_versions/paretobench_file_format_v1.0.0.json
@@ -23,6 +23,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "3b2d74eed2b6600433de939b181f2856fd70cff92d1e32909ac7ac88a1f77c28"
         },
         {
@@ -39,6 +53,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "119795280e0e1fb558ecef619d4b6083f4aecbcc34cee074fce3fc6954aeae39"
         },
         {
@@ -55,6 +83,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "b37443ac45a3bde81bca6686a6944783486812472181ac3619e2ee855dec6d59"
         },
         {
@@ -71,6 +113,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "1630ebf46c0d910fff487a13b95c3072fd90f0ff1fe24b97c36efc63bca838b0"
         },
         {
@@ -87,6 +143,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "dd1e5b71e3d6215173745aae9e64219982ff4062d5a8345c977c20e649b5a5cb"
         },
         {
@@ -103,6 +173,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "be66f8a208a6bf59dc4478761cfac501d598fe3871587234d3cdeb1b275732e5"
         },
         {
@@ -119,6 +203,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "f991d73ee8b4949445a5f91f3dadde91552dd973a3f67bf833031a3b144241d2"
         },
         {
@@ -135,6 +233,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "09ab4b8ea994852ca3fa95538e26ee13239892b70c3c985ba1976950ee710cc9"
         }
       ]
@@ -157,6 +269,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "5129b12bc092720878f65ea8e8e64c9c8e10bc6525b860065f50f205af1aaa30"
         },
         {
@@ -173,6 +299,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "c80488e2e77c8c7fb07e192a8f9cd13b42f72f11d0fa163d719bc88f2bf1d4b7"
         },
         {
@@ -189,6 +329,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "ee1f2f7ef29d5187d13ee669f998237cafc28ea07ed456c5aa3a0d0bdf515e8c"
         },
         {
@@ -205,6 +359,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "3ece64d9ccb0fb86003eaafa4f6a7856af7be2adb4e245d9ea2c853f3d71e3d8"
         },
         {
@@ -221,6 +389,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "c0c5551a141602e30a80a6796623b695849388591c00fc82c8b34af0520fb661"
         },
         {
@@ -237,6 +419,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "42625c5e8f003a3d4a1b93de25a9b8fd26b3e2ab9886fa3a97269bcaf8bd02a2"
         },
         {
@@ -253,6 +449,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "d86d9f3cb6b714bf93d17bd33f7ee0a74498b52698e921a2f0438cf3a07c2df7"
         },
         {
@@ -269,6 +479,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "40aaaf70ad4952885313cd34311f415c24465187959559f82fe86f3aab5b825f"
         }
       ]
@@ -291,6 +515,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "6bbc30fdb0e0c1d7ec230cf6dc41d161fd6865f24c6df5df9aee491f36051a67"
         },
         {
@@ -307,6 +545,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "8651c618c24b08877aa5dcc7ef1ec4cf8d445b25c1dccc640bbe92ad7f4a16c9"
         },
         {
@@ -323,6 +575,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "1ceb5e1607a108f9ab4b93837b9901ddede725dda4d8502d713dd706dbbb6825"
         },
         {
@@ -339,6 +605,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "8ab0082734025e52fbfe729d76979a91b9c83e39c31320dbb264176f90e38514"
         },
         {
@@ -355,6 +635,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "22837213bf2a410f53800c2a28a20a6e650916366556d5f5bd47f2dfd13e7853"
         },
         {
@@ -371,6 +665,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "0acad307f3e412fbffc9c901a205f8931d569841749089bc8956539d7c24a281"
         },
         {
@@ -387,6 +695,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "c2be26ae7b20916e87308eaaab9fffc0462d05f28fe74bdc6c61eac8b1532991"
         },
         {
@@ -403,6 +725,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "df3e9b06e4777cfbadd0412e78484785b22d667a11dc906b1659f49b79894f4e"
         }
       ]
@@ -425,6 +761,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "240594fa871c1c47eaa65f8d603b06fad4a635e8a45ddc31a0029e2a6b2d4d1b"
         },
         {
@@ -441,6 +791,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "5ff66c434ffbd1aa9b95d04dd3bd39ce9403d91d0f1034ed30b9cfef08de24b7"
         },
         {
@@ -457,6 +821,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "20d33df82efff7fbfcb1657e6fa3bfeac1282030eae63e31511481710a1b4926"
         },
         {
@@ -473,6 +851,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "d86606ca44168d2515236f57472e6b1ae3731eb10ef7c201567682043228db3b"
         },
         {
@@ -489,6 +881,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity,
+            Infinity
+          ],
           "x_sha256": "6fdfff6603e039d843b46a1cfaf5b217b8c8d51c7978cbcfbbc541b1a5b8aa81"
         },
         {
@@ -505,6 +911,20 @@
           "names_x": null,
           "obj_directions": "--",
           "pop_size": 50,
+          "var_lower_bounds": [
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity,
+            -Infinity
+          ],
+          "var_upper_bounds": [
+            Infinity,
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diff --git a/tests/test_data/file_versions/paretobench_file_format_v1.1.0.json b/tests/test_data/file_versions/paretobench_file_format_v1.1.0.json
index bfbb218..5966078 100644
--- a/tests/test_data/file_versions/paretobench_file_format_v1.1.0.json
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+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+--",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.19490100382675557,
+            -0.7412316659101856,
+            -0.9369278798585663,
+            -0.6592678664279883
+          ],
+          "var_upper_bounds": [
+            1.7249940940432136,
+            1.1644884116937722,
+            1.7482239682739769,
+            1.4973568393326229
+          ],
+          "x_sha256": "680dd84014c285805436d17cf40b17f2150326c745189599f103163a01ce7222"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "author": "SK1WJ",
+        "date": "2024-03-19",
+        "description": "Randomly generated metadata",
+        "version": 2.81949251193648
+      },
+      "problem": "CTP1 (n=4)",
+      "reports": [
+        {
+          "constraint_directions": "<>",
+          "constraint_targets": [
+            0.607741847385851,
+            0.4877645594977067
+          ],
+          "f_sha256": "19a5a9571032626303fc6d1c9c1480c348ed10fa9dbf898174586f2137127af2",
+          "fevals": 25,
+          "g_sha256": "52f0669483cf200f107551b33af830e70c9b0c25f85df49aea6560cc86a2a707",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.01331611324646731,
+            -0.6062619062265414,
+            -0.9890880753568537,
+            -0.818101050024029
+          ],
+          "var_upper_bounds": [
+            1.340604643884352,
+            1.1520470250444221,
+            1.784058614467154,
+            1.7439378206317415
+          ],
+          "x_sha256": "c720a83b3a64c9cecc82fd4e11210e1b61550a76af11ec996a0e89e0020e3833"
+        },
+        {
+          "constraint_directions": "<>",
+          "constraint_targets": [
+            0.607741847385851,
+            0.4877645594977067
+          ],
+          "f_sha256": "37219e5328d5b2d206078ea266e8c3306bb1bb95047bc262bdbd1dbb5899a8e4",
+          "fevals": 50,
+          "g_sha256": "6f22bedd12aafd66accb8108bc64c62edb0f322e3bcac50567761a67144812ca",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.01331611324646731,
+            -0.6062619062265414,
+            -0.9890880753568537,
+            -0.818101050024029
+          ],
+          "var_upper_bounds": [
+            1.340604643884352,
+            1.1520470250444221,
+            1.784058614467154,
+            1.7439378206317415
+          ],
+          "x_sha256": "0a13930c21ef2b7ae02ef6e38b896efc62b59a49eebe7fb9b9e419b829cd8df7"
+        },
+        {
+          "constraint_directions": "<>",
+          "constraint_targets": [
+            0.607741847385851,
+            0.4877645594977067
+          ],
+          "f_sha256": "3d03ac0b6ee6325de60f74b06c819a01019317bd8f339c6a1437afe19c707856",
+          "fevals": 75,
+          "g_sha256": "97b2a7d7bdf621396c6d9998087d1d6d1e3e94c3ccd3918cc36840370f523d94",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.01331611324646731,
+            -0.6062619062265414,
+            -0.9890880753568537,
+            -0.818101050024029
+          ],
+          "var_upper_bounds": [
+            1.340604643884352,
+            1.1520470250444221,
+            1.784058614467154,
+            1.7439378206317415
+          ],
+          "x_sha256": "2fe7b02c23b60d22fbd9f6a1f0f13a4d21b28e362a58cff23ab9c75b89120e18"
+        },
+        {
+          "constraint_directions": "<>",
+          "constraint_targets": [
+            0.607741847385851,
+            0.4877645594977067
+          ],
+          "f_sha256": "d2bd488ca7322083b2c96575440115c537388ea5a6ec0543b39d5b0c1f77630f",
+          "fevals": 100,
+          "g_sha256": "d848fa2f9da8c622a3ebcc1153f83510248deeb313eae197bcb7bd2fc22bb197",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.01331611324646731,
+            -0.6062619062265414,
+            -0.9890880753568537,
+            -0.818101050024029
+          ],
+          "var_upper_bounds": [
+            1.340604643884352,
+            1.1520470250444221,
+            1.784058614467154,
+            1.7439378206317415
+          ],
+          "x_sha256": "cb2ae15fa3453a2abd76a0ccad796d590d29aff7b38bd222b01c46d0752ba079"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "author": "06YQD",
+        "date": "2024-04-27",
+        "description": "Randomly generated metadata",
+        "version": 1.8683436709075674
+      },
+      "problem": "TNK",
+      "reports": [
+        {
+          "constraint_directions": "<<",
+          "constraint_targets": [
+            0.6771508915552242,
+            0.12116145688982427
+          ],
+          "f_sha256": "73c33b07b4370567b7eeaea24bb48613637d7b42ed3f49faafba14bbccd82c8c",
+          "fevals": 25,
+          "g_sha256": "231dac39841834d56c527e33db4e7058814c8720667e7c6ee15ae36e1cfa247f",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.17943296290248112,
+            -0.9701078696904671,
+            -0.6268266868350327,
+            -0.25397189765420736
+          ],
+          "var_upper_bounds": [
+            1.8997467379749975,
+            1.3495577757673076,
+            1.1547233189353576,
+            1.9803220340371652
+          ],
+          "x_sha256": "0776fd1a019e7ec4076ebd849e60871fb4de035d43605ddf698dd54f4e7cb50a"
+        },
+        {
+          "constraint_directions": "<<",
+          "constraint_targets": [
+            0.6771508915552242,
+            0.12116145688982427
+          ],
+          "f_sha256": "11761959216f04859bd6025b44d475ee56f5e0162b645553068f9b3c9effa10d",
+          "fevals": 50,
+          "g_sha256": "65d89d3192c392d4080e35ac9f2926ff2c9c30546378d2f1d60550311157b3f6",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.17943296290248112,
+            -0.9701078696904671,
+            -0.6268266868350327,
+            -0.25397189765420736
+          ],
+          "var_upper_bounds": [
+            1.8997467379749975,
+            1.3495577757673076,
+            1.1547233189353576,
+            1.9803220340371652
+          ],
+          "x_sha256": "3626650e225175e2d4091065ec88524683da20ff67eb19fd84998ea8942931d2"
+        },
+        {
+          "constraint_directions": "<<",
+          "constraint_targets": [
+            0.6771508915552242,
+            0.12116145688982427
+          ],
+          "f_sha256": "3e733579029c985a702dbfa5e75131b5b5ffc2400c314788526a86c77945197e",
+          "fevals": 75,
+          "g_sha256": "b59ff3007c59639b5ac7f78e6052ebf653ff26101de32ceebe5f8d4dfbc160aa",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.17943296290248112,
+            -0.9701078696904671,
+            -0.6268266868350327,
+            -0.25397189765420736
+          ],
+          "var_upper_bounds": [
+            1.8997467379749975,
+            1.3495577757673076,
+            1.1547233189353576,
+            1.9803220340371652
+          ],
+          "x_sha256": "7dcdec8bda5bceedaeeef36cad1b640e9561388816c6042ecf4115750f4206b1"
+        },
+        {
+          "constraint_directions": "<<",
+          "constraint_targets": [
+            0.6771508915552242,
+            0.12116145688982427
+          ],
+          "f_sha256": "dc3ca3cdce31564a610fc2e4c046fe3d5700fd5ab04a296570031d71db3b843b",
+          "fevals": 100,
+          "g_sha256": "0a05f1539664d7add8fd6276dca68b26027554563d0fb48a2ac0b7df03bf5451",
+          "m": 3,
+          "n": 4,
+          "n_constraints": 2,
+          "names_f": [
+            "f1",
+            "f2",
+            "f3"
+          ],
+          "names_g": [
+            "g1",
+            "g2"
+          ],
+          "names_x": [
+            "x1",
+            "x2",
+            "x3",
+            "x4"
+          ],
+          "obj_directions": "+++",
+          "pop_size": 25,
+          "var_lower_bounds": [
+            -0.17943296290248112,
+            -0.9701078696904671,
+            -0.6268266868350327,
+            -0.25397189765420736
+          ],
+          "var_upper_bounds": [
+            1.8997467379749975,
+            1.3495577757673076,
+            1.1547233189353576,
+            1.9803220340371652
+          ],
+          "x_sha256": "12ede9a8819bfd99ab2f50e67b13d3a7bcd888ae36768cbad2a580c839efab1f"
+        }
+      ]
+    }
+  ],
+  "software": "ParetoBench",
+  "software_version": "0.0.0"
+}
\ No newline at end of file
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index d8381cb..04d0922 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -231,6 +231,79 @@ def test_population_dvar_pairs_selection():
     plt.close(fig)
 
 
+def get_bound_lines(ax):
+    """
+    Returns the x locations of the vertical bound lines drawn onto one of the histogram axes of a dvar pairs plot.
+    """
+    return sorted(line.get_xdata()[0] for line in ax.get_lines() if line.get_linestyle() == "--")
+
+
+def test_population_dvar_pairs_bounds():
+    """
+    The population's own bounds are used for the bound lines unless the caller overrides or disables them.
+    """
+    pop = Population(x=np.random.random((5, 3)), f=np.random.random((5, 2)))
+    bounded = Population(x=pop.x, f=pop.f, var_lower_bounds=-np.ones(3), var_upper_bounds=2 * np.ones(3))
+
+    # The unbounded population draws nothing since its bounds are infinite
+    fig, axes = population_dvar_pairs(pop)
+    assert get_bound_lines(axes[0, 0]) == []
+    plt.close(fig)
+
+    # The bounded population draws its own bounds
+    fig, axes = population_dvar_pairs(bounded)
+    assert get_bound_lines(axes[0, 0]) == [-1.0, 2.0]
+    plt.close(fig)
+
+    # Which can be turned off
+    fig, axes = population_dvar_pairs(bounded, plot_bounds=False)
+    assert get_bound_lines(axes[0, 0]) == []
+    plt.close(fig)
+
+    # User specified bounds take precedence over the population's
+    fig, axes = population_dvar_pairs(bounded, lower_bounds=np.zeros(3), upper_bounds=np.ones(3))
+    assert get_bound_lines(axes[0, 0]) == [0.0, 1.0]
+    plt.close(fig)
+
+    # A problem may also be used as the source of bounds
+    fig, axes = population_dvar_pairs(bounded, problem="ZDT1 (n=3)")
+    assert get_bound_lines(axes[0, 0]) == [0.0, 1.0]
+    plt.close(fig)
+
+    # Only one source of bounds may be specified
+    with pytest.raises(ValueError, match="Only specify one of problem or the upper/lower bounds"):
+        population_dvar_pairs(bounded, problem="ZDT1 (n=3)", lower_bounds=np.zeros(3))
+
+
+def test_population_dvar_pairs_partial_bounds():
+    """
+    Bound lines are skipped for the individual variables which are unbounded.
+    """
+    pop = Population(
+        x=np.random.random((5, 2)),
+        f=np.random.random((5, 2)),
+        var_lower_bounds=np.array([-1.0, -np.inf]),
+        var_upper_bounds=np.array([2.0, np.inf]),
+    )
+
+    fig, axes = population_dvar_pairs(pop)
+    assert get_bound_lines(axes[0, 0]) == [-1.0, 2.0]
+    assert get_bound_lines(axes[1, 1]) == []
+    plt.close(fig)
+
+
+def test_history_dvar_pairs_bounds():
+    hist = History.from_random(3, 2, 3, 1, 10, generate_bounds=True)
+
+    fig, axes = hist.plot_dvar_pairs()
+    assert get_bound_lines(axes[0, 0]) == []
+    plt.close(fig)
+
+    fig, axes = hist.plot_dvar_pairs(plot_bounds=True, lower_bounds=np.zeros(3), upper_bounds=np.ones(3))
+    assert get_bound_lines(axes[0, 0]) == [0.0, 1.0]
+    plt.close(fig)
+
+
 def test_population_dvar_pairs_errors():
     """Test error cases"""
     pop = Population.from_random(n_objectives=2, n_decision_vars=3, n_constraints=1, pop_size=5)
diff --git a/tests/utils.py b/tests/utils.py
index 779a43a..1e6da95 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -79,6 +79,8 @@ def experiment_to_manifest(exp):
                         "obj_directions": report.obj_directions,
                         "constraint_directions": report.constraint_directions,
                         "constraint_targets": report.constraint_targets.tolist(),
+                        "var_lower_bounds": report.var_lower_bounds.tolist(),
+                        "var_upper_bounds": report.var_upper_bounds.tolist(),
                         "x_sha256": array_digest(report.x),
                         "f_sha256": array_digest(report.f),
                         "g_sha256": array_digest(report.g),