diff --git a/ci/conditional-test-policy.json b/ci/conditional-test-policy.json
index 043bde4..f5e5b9c 100644
--- a/ci/conditional-test-policy.json
+++ b/ci/conditional-test-policy.json
@@ -131,6 +131,7 @@
       "coverage_paths": [
         "obliteratus/device.py",
         "obliteratus/models/loader.py",
+        "obliteratus/models/offload_surgery.py",
         "obliteratus/models/quant_dequant.py"
       ]
     },
diff --git a/ci/test-risk-map.json b/ci/test-risk-map.json
index 658f106..85c3fd5 100644
--- a/ci/test-risk-map.json
+++ b/ci/test-risk-map.json
@@ -50,6 +50,7 @@
       ],
       "paths": [
         "obliteratus/abliterate.py",
+        "obliteratus/models/offload_surgery.py",
         "obliteratus/persistence_contracts.py",
         "obliteratus/auto_obliterate.py",
         "obliteratus/bayesian_optimizer.py",
@@ -74,8 +75,10 @@
         "tests/test_sweep_contracts.py",
         "tests/test_tourney_contracts.py",
         "tests/test_checkpoint_atomicity.py",
+        "tests/test_offload_surgery.py",
         "tests/test_persistence_contracts.py",
-        "tests/test_persistence_pipeline.py"
+        "tests/test_persistence_pipeline.py",
+        "tests/conditional/test_cuda_runtime.py"
       ]
     },
     {
@@ -458,6 +461,18 @@
         "cuda-runtime"
       ]
     },
+    {
+      "path": "obliteratus/models/offload_surgery.py",
+      "risk_class": "mixed-runtime",
+      "risk": "transactional logical-parameter mutation across live, CPU-offloaded, disk-backed, and CUDA execution layouts",
+      "required_tests": [
+        "tests/test_offload_surgery.py",
+        "tests/conditional/test_cuda_runtime.py"
+      ],
+      "conditional_gates": [
+        "cuda-runtime"
+      ]
+    },
     {
       "path": "obliteratus/mlx_backend.py",
       "risk_class": "mixed-runtime",
diff --git a/obliteratus/abliterate.py b/obliteratus/abliterate.py
index ca5322c..d77921f 100644
--- a/obliteratus/abliterate.py
+++ b/obliteratus/abliterate.py
@@ -66,6 +66,14 @@ def _has_fused_quant_scale(container: nn.Module, name: str) -> bool:
 dev.configure_cuda_alloc()
 
 from obliteratus.models.loader import ModelHandle, load_model  # noqa: E402
+from obliteratus.models.offload_surgery import (  # noqa: E402
+    LogicalParameterTransaction,
+    OffloadSurgeryError,
+    UnsupportedOffloadLayoutError,
+    logical_module_device,
+    resolve_logical_parameter,
+    validate_offloaded_parameters,
+)
 from obliteratus.analysis.numerical_contracts import (  # noqa: E402
     orthogonalize_subspace_rows,
     project_weight_against_direction,
@@ -3572,6 +3580,71 @@ class AbliterationPipeline:
         total_neurons_masked = 0
         total_sae_projections = 0
 
+        # Resolve every selected layer's meta-resident parameter before any
+        # surgery. Unknown or unsupported Accelerate layouts therefore fail
+        # closed without leaving an earlier layer partially modified.
+        offloaded_selected = False
+        for layer_index in self._strong_layers:
+            layer = layers[layer_index]
+            meta_parameters = [
+                (name, parameter)
+                for name, parameter in layer.named_parameters()
+                if parameter.device.type == "meta"
+            ]
+            if meta_parameters:
+                offloaded_selected = True
+                fused_names = [
+                    name for name, parameter in meta_parameters if parameter.dim() > 2
+                ]
+                if fused_names:
+                    raise UnsupportedOffloadLayoutError(
+                        "offloaded fused expert tensors are not yet a supported "
+                        f"surgery layout: {', '.join(fused_names)}"
+                    )
+            validate_offloaded_parameters(layer)
+
+        if offloaded_selected:
+            unsupported_modes = [
+                name
+                for enabled, name in (
+                    (getattr(self, "use_lora_ablation", False), "LoRA ablation"),
+                    (getattr(self, "attention_head_surgery", False), "attention-head surgery"),
+                    (getattr(self, "safety_neuron_masking", False), "safety-neuron masking"),
+                    (getattr(self, "expert_transplant", False), "expert transplant"),
+                )
+                if enabled
+            ]
+            if unsupported_modes:
+                raise UnsupportedOffloadLayoutError(
+                    "Accelerate-offloaded surgery does not yet support: "
+                    + ", ".join(unsupported_modes)
+                )
+
+        if self.handle:
+            model = self.handle.model
+            for head_name in ("lm_head", "embed_out", "output"):
+                head = getattr(model, head_name, None)
+                weight = getattr(head, "weight", None)
+                if weight is not None and weight.device.type == "meta":
+                    resolve_logical_parameter(head, search_roots=(model,))
+            if self.project_embeddings:
+                for embedding_path in (
+                    "model.embed_tokens",
+                    "model.language_model.embed_tokens",
+                    "transformer.wte",
+                    "model.embed_in",
+                    "gpt_neox.embed_in",
+                ):
+                    embedding = model
+                    for path_component in embedding_path.split("."):
+                        embedding = getattr(embedding, path_component, None)
+                        if embedding is None:
+                            break
+                    weight = getattr(embedding, "weight", None)
+                    if weight is not None and weight.device.type == "meta":
+                        resolve_logical_parameter(embedding, search_roots=(model,))
+                        break
+
         # ── Bayesian optimization pre-pass ─────────────────────────────
         # When enabled, run Optuna TPE to find optimal per-layer regularization
         # before the standard projection loop.  The found values override the
@@ -3724,7 +3797,7 @@ class AbliterationPipeline:
 
             for idx in self._strong_layers:
                 subspace = self.refusal_subspaces[idx]
-                device = next(layers[idx].parameters()).device
+                device = logical_module_device(layers[idx])
 
                 # Layer-adaptive regularization: scale projection per-layer
                 layer_reg = self.regularization
@@ -3827,9 +3900,15 @@ class AbliterationPipeline:
                                 norm_preserve=dir_norm_preserve,
                                 regularization=attn_reg,
                                 projection_row_fraction=self.projection_row_fraction,
+                                offload_roots=(layers[idx],),
                             )
                             if self.project_biases:
-                                count += self._project_bias(attn, d, _attn_names)
+                                count += self._project_bias(
+                                    attn,
+                                    d,
+                                    _attn_names,
+                                    offload_roots=(layers[idx],),
+                                )
 
                             # Additional head surgery: second-pass precision targeting
                             # on the top safety heads to remove residual refusal signal.
@@ -3848,6 +3927,8 @@ class AbliterationPipeline:
                                     norm_preserve=dir_norm_preserve,
                                     regularization=0.0,  # full removal of residual
                                 )
+                        except OffloadSurgeryError:
+                            raise
                         except (AttributeError, RuntimeError) as e:
                             warnings.warn(
                                 f"Layer {idx}: attention projection failed ({type(e).__name__}: {e}). "
@@ -3870,6 +3951,7 @@ class AbliterationPipeline:
                                 norm_preserve=dir_norm_preserve,
                                 regularization=mlp_reg,
                                 projection_row_fraction=self.projection_row_fraction,
+                                offload_roots=(layers[idx],),
                             )
                             if ffn_count == 0:
                                 # MoE path
@@ -3884,6 +3966,7 @@ class AbliterationPipeline:
                                         norm_preserve=dir_norm_preserve,
                                         regularization=mlp_reg,
                                         project_biases=self.project_biases,
+                                        offload_roots=(layers[idx],),
                                     )
                                 elif self.invert_refusal and idx in self._expert_safety_scores:
                                     # Selective MoE inversion: router reflected, safety
@@ -3892,6 +3975,7 @@ class AbliterationPipeline:
                                         ffn, d, idx,
                                         norm_preserve=dir_norm_preserve,
                                         project_biases=self.project_biases,
+                                        offload_roots=(layers[idx],),
                                     )
                                 else:
                                     ffn_count = self._project_moe_experts(
@@ -3900,10 +3984,16 @@ class AbliterationPipeline:
                                         regularization=mlp_reg,
                                         project_biases=self.project_biases,
                                         projection_row_fraction=self.projection_row_fraction,
+                                        offload_roots=(layers[idx],),
                                     )
                             elif self.projection_target == "output":
                                 if self.project_biases:
-                                    ffn_count += self._project_bias(ffn, d, _FFN_OUT_NAMES)
+                                    ffn_count += self._project_bias(
+                                        ffn,
+                                        d,
+                                        _FFN_OUT_NAMES,
+                                        offload_roots=(layers[idx],),
+                                    )
                             else:
                                 # Dense model: also project FFN input projections
                                 # (up_proj, gate_proj carry refusal signal too)
@@ -3912,10 +4002,14 @@ class AbliterationPipeline:
                                     norm_preserve=dir_norm_preserve,
                                     regularization=mlp_reg,
                                     projection_row_fraction=self.projection_row_fraction,
+                                    offload_roots=(layers[idx],),
                                 )
                                 if self.project_biases:
                                     ffn_count += self._project_bias(
-                                        ffn, d, _FFN_OUT_NAMES + _FFN_IN_NAMES,
+                                        ffn,
+                                        d,
+                                        _FFN_OUT_NAMES + _FFN_IN_NAMES,
+                                        offload_roots=(layers[idx],),
                                     )
 
                             # Safety-neuron masking (applied after projection for
@@ -3936,6 +4030,8 @@ class AbliterationPipeline:
                                 total_neurons_masked += n_masked
 
                             count += ffn_count
+                        except OffloadSurgeryError:
+                            raise
                         except (AttributeError, RuntimeError) as e:
                             warnings.warn(
                                 f"Layer {idx}: FFN projection failed ({type(e).__name__}: {e}). "
@@ -4023,7 +4119,10 @@ class AbliterationPipeline:
                                     norm_preserve=self.norm_preserve,
                                     regularization=sae_reg,
                                     projection_row_fraction=self.projection_row_fraction,
+                                    offload_roots=(layers[idx],),
                                 )
+                            except OffloadSurgeryError:
+                                raise
                             except (AttributeError, RuntimeError):
                                 pass
                         if sae_ffn is not None:
@@ -4033,6 +4132,7 @@ class AbliterationPipeline:
                                     norm_preserve=self.norm_preserve,
                                     regularization=sae_reg,
                                     projection_row_fraction=self.projection_row_fraction,
+                                    offload_roots=(layers[idx],),
                                 )
                                 if fc == 0:
                                     fc = self._project_moe_experts(
@@ -4041,8 +4141,11 @@ class AbliterationPipeline:
                                         regularization=sae_reg,
                                         project_biases=False,
                                         projection_row_fraction=self.projection_row_fraction,
+                                        offload_roots=(layers[idx],),
                                     )
                                 sae_count += fc
+                            except OffloadSurgeryError:
+                                raise
                             except (AttributeError, RuntimeError):
                                 pass
                         del sd
@@ -4107,7 +4210,7 @@ class AbliterationPipeline:
             model = self.handle.model
             if last_strong in self.refusal_subspaces:
                 subspace = self.refusal_subspaces[last_strong]
-                lm_device = self._get_model_device(model)
+                lm_device = logical_module_device(model)
                 # Pre-transfer subspace and resolve lm_head module once
                 subspace_on_device = subspace.to(lm_device)
                 lm_head_name = None
@@ -4131,9 +4234,11 @@ class AbliterationPipeline:
                         and lm_head_obj is not None
                         and hasattr(lm_head_obj, "weight")
                     )
-                    lm_original_norm = 0.0
-                    if lm_multi_dir:
-                        lm_original_norm = lm_head_obj.weight.data.norm().item()
+                    lm_saved_norms = (
+                        self._capture_layer_weight_norms(lm_head_obj)
+                        if lm_multi_dir
+                        else {}
+                    )
                     for dir_idx in range(subspace_on_device.shape[0]):
                         d = subspace_on_device[dir_idx].unsqueeze(-1)
                         lm_head_count += self._project_out_advanced(
@@ -4144,14 +4249,8 @@ class AbliterationPipeline:
                         )
                         del d
                     # Restore lm_head norm once after all directions
-                    if lm_multi_dir and lm_original_norm > 0 and lm_head_obj is not None:
-                        new_norm = lm_head_obj.weight.data.norm().item()
-                        if new_norm > 0 and not math.isnan(new_norm) and not math.isinf(new_norm):
-                            ratio = lm_original_norm / new_norm
-                            if ratio > _MAX_NORM_RATIO:
-                                ratio = _MAX_NORM_RATIO
-                            if abs(ratio - 1.0) > 1e-6:
-                                lm_head_obj.weight.data.mul_(ratio)
+                    if lm_multi_dir and lm_saved_norms and lm_head_obj is not None:
+                        self._restore_layer_weight_norms(lm_head_obj, lm_saved_norms)
                 del subspace_on_device
         if lm_head_count > 0:
             total_modified += lm_head_count
@@ -4207,6 +4306,7 @@ class AbliterationPipeline:
                             norm_preserve=True,  # always norm-preserve embeds
                             regularization=emb_reg,
                             projection_row_fraction=self.projection_row_fraction,
+                            offload_roots=(model,),
                         )
                         break
                 del d
@@ -4930,7 +5030,13 @@ class AbliterationPipeline:
             if identity in seen:
                 continue
             param_name = f"{module_name}.weight" if module_name else "weight"
-            data, _requires_replacement = AbliterationPipeline._dequantize_weight(module)
+            if weight is not None and weight.device.type == "meta":
+                data = resolve_logical_parameter(
+                    module,
+                    search_roots=(layer,),
+                ).tensor
+            else:
+                data, _requires_replacement = AbliterationPipeline._dequantize_weight(module)
             norms[param_name] = data.float().norm().item()
             seen.add(identity)
         return norms
@@ -4974,7 +5080,16 @@ class AbliterationPipeline:
                 continue
 
             original_norm = saved_norms[param_name]
-            data, requires_replacement = AbliterationPipeline._dequantize_weight(module)
+            transaction = None
+            if weight is not None and weight.device.type == "meta":
+                transaction = resolve_logical_parameter(
+                    module,
+                    search_roots=(layer,),
+                )
+                data = transaction.tensor
+                requires_replacement = False
+            else:
+                data, requires_replacement = AbliterationPipeline._dequantize_weight(module)
             ratio = norm_restoration_ratio(
                 original_norm,
                 data.float().norm().item(),
@@ -4985,7 +5100,9 @@ class AbliterationPipeline:
                 continue
 
             with torch.no_grad():
-                if storage_kind == "integer":
+                if transaction is not None:
+                    transaction.commit(data.mul(ratio))
+                elif storage_kind == "integer":
                     # Integer storage has no scale/zero-point contract. Casting
                     # back can erase the update, so materialize logical float
                     # values while preserving Parameter identity and ties.
@@ -5020,6 +5137,7 @@ class AbliterationPipeline:
         norm_preserve: bool = False,
         regularization: float = 0.0,
         projection_row_fraction: float = 1.0,
+        offload_roots: tuple[nn.Module, ...] = (),
     ) -> int:
         """Advanced projection with norm preservation and regularization.
 
@@ -5039,16 +5157,45 @@ class AbliterationPipeline:
         in-place operations on packed NF4 storage are silent no-ops.
         """
         count = 0
+        candidates: list[
+            tuple[nn.Module, LogicalParameterTransaction | None, torch.Tensor, bool]
+        ] = []
 
         for name in candidate_names:
             proj = getattr(module, name, None)
             if proj is None or not hasattr(proj, "weight"):
                 continue
 
-            W, is_quantized = AbliterationPipeline._dequantize_weight(proj)
+            if proj.weight.device.type == "meta":
+                transaction = resolve_logical_parameter(
+                    proj,
+                    search_roots=(module, *offload_roots),
+                )
+                candidates.append((proj, transaction, transaction.tensor, False))
+            else:
+                W, is_quantized = AbliterationPipeline._dequantize_weight(proj)
+                transaction = (
+                    None
+                    if is_quantized
+                    else resolve_logical_parameter(proj)
+                )
+                candidates.append((proj, transaction, W, is_quantized))
+
+        # Resolve every backing store before the first mutation. An unsupported
+        # later candidate therefore cannot leave an earlier projection changed.
+        updates: list[
+            tuple[
+                nn.Module,
+                LogicalParameterTransaction | None,
+                torch.Tensor,
+                bool,
+                torch.Tensor,
+            ]
+        ] = []
+        for proj, transaction, W, is_quantized in candidates:
             result = project_weight_against_direction(
                 W,
-                direction,
+                direction.to(W.device),
                 norm_preserve=norm_preserve,
                 regularization=regularization,
                 projection_row_fraction=projection_row_fraction,
@@ -5057,11 +5204,23 @@ class AbliterationPipeline:
             if not result.projected:
                 continue
 
-            W.copy_(result.weight)
-            if is_quantized:
-                AbliterationPipeline._replace_quantized_weight(proj, W)
+            updates.append((proj, transaction, W, is_quantized, result.weight))
 
-            count += 1
+        committed: list[LogicalParameterTransaction] = []
+        try:
+            for proj, transaction, W, is_quantized, updated in updates:
+                if transaction is not None:
+                    transaction.commit(updated)
+                    committed.append(transaction)
+                else:
+                    W.copy_(updated)
+                if transaction is None and is_quantized:
+                    AbliterationPipeline._replace_quantized_weight(proj, W)
+                count += 1
+        except Exception:
+            for transaction in reversed(committed):
+                transaction.rollback()
+            raise
 
         return count
 
@@ -5070,6 +5229,7 @@ class AbliterationPipeline:
         module: nn.Module,
         direction: torch.Tensor,
         candidate_names: list[str],
+        offload_roots: tuple[nn.Module, ...] = (),
     ) -> int:
         """Project the refusal direction out of bias terms.
 
@@ -5081,6 +5241,9 @@ class AbliterationPipeline:
         Gabliteration, grimjim) do not project biases.
         """
         count = 0
+        candidates: list[
+            tuple[LogicalParameterTransaction | None, torch.Tensor]
+        ] = []
         for name in candidate_names:
             proj = getattr(module, name, None)
             if proj is None or not hasattr(proj, "bias"):
@@ -5088,16 +5251,40 @@ class AbliterationPipeline:
             if proj.bias is None:
                 continue
 
-            b = proj.bias.data
+            if proj.bias.device.type == "meta":
+                transaction = resolve_logical_parameter(
+                    proj,
+                    "bias",
+                    search_roots=(module, *offload_roots),
+                )
+                candidates.append((transaction, transaction.tensor))
+            else:
+                transaction = resolve_logical_parameter(proj, "bias")
+                candidates.append((transaction, transaction.tensor))
+
+        updates: list[tuple[LogicalParameterTransaction, torch.Tensor]] = []
+        for transaction, b in candidates:
             d = direction.to(device=b.device, dtype=b.dtype).squeeze()  # (hidden_dim,)
 
             if b.shape[0] == d.shape[0]:
                 # Bias is (out_features,) = (hidden_dim,) for output projections
                 component = (b @ d).unsqueeze(0) * d  # scalar * direction
-                proj.bias.data = b - component.squeeze()
-                count += 1
+                updated = b - component.squeeze()
+                assert transaction is not None
+                updates.append((transaction, updated))
             # else: dimension mismatch — expected for GQA k/v projections,
             # fused QKV (c_attn), and MoE routers. Skip silently.
+
+        committed: list[LogicalParameterTransaction] = []
+        try:
+            for transaction, updated in updates:
+                transaction.commit(updated)
+                committed.append(transaction)
+                count += 1
+        except Exception:
+            for transaction in reversed(committed):
+                transaction.rollback()
+            raise
         return count
 
     @staticmethod
@@ -5246,7 +5433,10 @@ class AbliterationPipeline:
         return 0
 
     @staticmethod
-    def _stabilize_router_weights(ffn_module: nn.Module):
+    def _stabilize_router_weights(
+        ffn_module: nn.Module,
+        offload_roots: tuple[nn.Module, ...] = (),
+    ):
         """Clamp router weights after projection to prevent extreme routing.
 
         After projecting the refusal direction from router weights, modified
@@ -5260,11 +5450,23 @@ class AbliterationPipeline:
         for rname in _ROUTER_NAMES:
             gate = getattr(ffn_module, rname, None)
             if gate is not None and hasattr(gate, "weight"):
-                W = gate.weight.data
+                transaction = None
+                if gate.weight.device.type == "meta":
+                    transaction = resolve_logical_parameter(
+                        gate,
+                        search_roots=(ffn_module, *offload_roots),
+                    )
+                    W = transaction.tensor
+                else:
+                    W = gate.weight.data
                 std = W.std()
                 if std > 0:
                     mean = W.mean()
-                    gate.weight.data = W.clamp(mean - 3 * std, mean + 3 * std)
+                    stabilized = W.clamp(mean - 3 * std, mean + 3 * std)
+                    if transaction is not None:
+                        transaction.commit(stabilized)
+                    else:
+                        W.copy_(stabilized)
                 return
         # Auto-detect fallback
         if getattr(ffn_module, "experts", None) is not None:
@@ -5273,12 +5475,25 @@ class AbliterationPipeline:
                     continue
                 if not hasattr(child, "weight"):
                     continue
-                W = child.weight
-                if W.shape[0] < 512 and W.shape[0] != W.shape[-1]:
+                weight = child.weight
+                if weight.shape[0] < 512 and weight.shape[0] != weight.shape[-1]:
+                    transaction = None
+                    if weight.device.type == "meta":
+                        transaction = resolve_logical_parameter(
+                            child,
+                            search_roots=(ffn_module, *offload_roots),
+                        )
+                        W = transaction.tensor
+                    else:
+                        W = weight.data
                     std = W.data.std()
                     if std > 0:
                         mean = W.data.mean()
-                        child.weight.data = W.data.clamp(mean - 3 * std, mean + 3 * std)
+                        stabilized = W.data.clamp(mean - 3 * std, mean + 3 * std)
+                        if transaction is not None:
+                            transaction.commit(stabilized)
+                        else:
+                            W.copy_(stabilized)
                     return
 
     @staticmethod
@@ -5289,6 +5504,7 @@ class AbliterationPipeline:
         regularization: float = 0.0,
         project_biases: bool = False,
         projection_row_fraction: float = 1.0,
+        offload_roots: tuple[nn.Module, ...] = (),
     ) -> int:
         """Project refusal direction from all MoE components.
 
@@ -5329,10 +5545,11 @@ class AbliterationPipeline:
                     norm_preserve=norm_preserve,
                     regularization=regularization,
                     projection_row_fraction=projection_row_fraction,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
                     count += AbliterationPipeline._project_bias(
-                        ffn_module, direction, [rname],
+                        ffn_module, direction, [rname], offload_roots,
                     )
                 router_found = True
                 break  # only one router per MoE block
@@ -5363,10 +5580,11 @@ class AbliterationPipeline:
                         norm_preserve=norm_preserve,
                         regularization=regularization,
                         projection_row_fraction=projection_row_fraction,
+                        offload_roots=offload_roots,
                     )
                     if project_biases:
                         count += AbliterationPipeline._project_bias(
-                            ffn_module, direction, [child_name],
+                            ffn_module, direction, [child_name], offload_roots,
                         )
                     router_found = True
                     break
@@ -5386,6 +5604,7 @@ class AbliterationPipeline:
                     norm_preserve=norm_preserve,
                     regularization=regularization,
                     projection_row_fraction=projection_row_fraction,
+                    offload_roots=offload_roots,
                 )
                 # Input projections
                 count += AbliterationPipeline._project_out_advanced(
@@ -5393,13 +5612,14 @@ class AbliterationPipeline:
                     norm_preserve=norm_preserve,
                     regularization=regularization,
                     projection_row_fraction=projection_row_fraction,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
                     count += AbliterationPipeline._project_bias(
-                        shared, direction, _FFN_OUT_NAMES,
+                        shared, direction, _FFN_OUT_NAMES, offload_roots,
                     )
                     count += AbliterationPipeline._project_bias(
-                        shared, direction, _FFN_IN_NAMES,
+                        shared, direction, _FFN_IN_NAMES, offload_roots,
                     )
                 break
 
@@ -5411,6 +5631,7 @@ class AbliterationPipeline:
             norm_preserve=norm_preserve,
             regularization=regularization,
             projection_row_fraction=projection_row_fraction,
+            offload_roots=offload_roots,
         )
 
         # ── Routed expert projection ──────────────────────────────────────
@@ -5449,6 +5670,7 @@ class AbliterationPipeline:
                     norm_preserve=norm_preserve,
                     regularization=regularization,
                     projection_row_fraction=projection_row_fraction,
+                    offload_roots=offload_roots,
                 )
                 # Input projections (up_proj, gate_proj, w1, w3, etc.)
                 expert_count += AbliterationPipeline._project_out_advanced(
@@ -5456,13 +5678,14 @@ class AbliterationPipeline:
                     norm_preserve=norm_preserve,
                     regularization=regularization,
                     projection_row_fraction=projection_row_fraction,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
                     expert_count += AbliterationPipeline._project_bias(
-                        expert, direction, _FFN_OUT_NAMES,
+                        expert, direction, _FFN_OUT_NAMES, offload_roots,
                     )
                     expert_count += AbliterationPipeline._project_bias(
-                        expert, direction, _FFN_IN_NAMES,
+                        expert, direction, _FFN_IN_NAMES, offload_roots,
                     )
 
         count += expert_count
@@ -5470,7 +5693,9 @@ class AbliterationPipeline:
         # Stabilize router weights after projection to prevent extreme logits
         # that cause CUDA illegal memory access during generation.
         if count > 0:
-            AbliterationPipeline._stabilize_router_weights(ffn_module)
+            AbliterationPipeline._stabilize_router_weights(
+                ffn_module, offload_roots
+            )
 
         return count
 
@@ -5481,6 +5706,7 @@ class AbliterationPipeline:
         layer_idx: int,
         norm_preserve: bool = False,
         project_biases: bool = False,
+        offload_roots: tuple[nn.Module, ...] = (),
     ) -> int:
         """MoE excision with selective inversion (refusal reflection).
 
@@ -5531,9 +5757,12 @@ class AbliterationPipeline:
                     ffn_module, direction, [rname],
                     norm_preserve=norm_preserve,
                     regularization=router_reg,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
-                    count += self._project_bias(ffn_module, direction, [rname])
+                    count += self._project_bias(
+                        ffn_module, direction, [rname], offload_roots
+                    )
                 break
 
         # Router auto-detection fallback
@@ -5550,6 +5779,7 @@ class AbliterationPipeline:
                         ffn_module, direction, [child_name],
                         norm_preserve=norm_preserve,
                         regularization=router_reg,
+                        offload_roots=offload_roots,
                     )
                     break
 
@@ -5563,9 +5793,15 @@ class AbliterationPipeline:
                     shared, direction, _FFN_OUT_NAMES + _FFN_IN_NAMES,
                     norm_preserve=norm_preserve,
                     regularization=reflect_reg,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
-                    count += self._project_bias(shared, direction, _FFN_OUT_NAMES + _FFN_IN_NAMES)
+                    count += self._project_bias(
+                        shared,
+                        direction,
+                        _FFN_OUT_NAMES + _FFN_IN_NAMES,
+                        offload_roots,
+                    )
                 break
 
         # ── Routed experts: selective inversion ───────────────────────
@@ -5581,9 +5817,15 @@ class AbliterationPipeline:
                     expert, direction, _FFN_OUT_NAMES + _FFN_IN_NAMES,
                     norm_preserve=norm_preserve,
                     regularization=reg,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
-                    count += self._project_bias(expert, direction, _FFN_OUT_NAMES + _FFN_IN_NAMES)
+                    count += self._project_bias(
+                        expert,
+                        direction,
+                        _FFN_OUT_NAMES + _FFN_IN_NAMES,
+                        offload_roots,
+                    )
         else:
             # Fused 3D: per-expert differentiation via per-slice processing.
             # Safety experts get reflected, capability experts get standard removal.
@@ -5609,7 +5851,7 @@ class AbliterationPipeline:
         # Stabilize router weights after reflection to prevent extreme logits
         # that cause CUDA illegal memory access during generation.
         if count > 0:
-            self._stabilize_router_weights(ffn_module)
+            self._stabilize_router_weights(ffn_module, offload_roots)
 
         return count
 
@@ -5621,6 +5863,7 @@ class AbliterationPipeline:
         norm_preserve: bool = False,
         regularization: float = 0.0,
         project_biases: bool = False,
+        offload_roots: tuple[nn.Module, ...] = (),
     ) -> int:
         """Expert-Granular Abliteration: per-expert direction projection.
 
@@ -5648,9 +5891,12 @@ class AbliterationPipeline:
                     ffn_module, direction, [rname],
                     norm_preserve=norm_preserve,
                     regularization=regularization,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
-                    count += self._project_bias(ffn_module, direction, [rname])
+                    count += self._project_bias(
+                        ffn_module, direction, [rname], offload_roots
+                    )
                 router_found = True
                 break
         if not router_found:
@@ -5662,6 +5908,7 @@ class AbliterationPipeline:
                             ffn_module, direction, [child_name],
                             norm_preserve=norm_preserve,
                             regularization=regularization,
+                            offload_roots=offload_roots,
                         )
                         break
 
@@ -5673,16 +5920,22 @@ class AbliterationPipeline:
             count += self._project_out_advanced(
                 shared, direction, _FFN_OUT_NAMES + _FFN_IN_NAMES,
                 norm_preserve=norm_preserve, regularization=regularization,
+                offload_roots=offload_roots,
             )
             if project_biases:
-                count += self._project_bias(shared, direction, _FFN_OUT_NAMES + _FFN_IN_NAMES)
+                count += self._project_bias(
+                    shared,
+                    direction,
+                    _FFN_OUT_NAMES + _FFN_IN_NAMES,
+                    offload_roots,
+                )
             break
 
         # ── Routed experts: per-expert directions ──
         experts = getattr(ffn_module, "experts", None)
         if experts is None:
             if count > 0:
-                self._stabilize_router_weights(ffn_module)
+                self._stabilize_router_weights(ffn_module, offload_roots)
             return count
 
         expert_count = 0
@@ -5699,14 +5952,21 @@ class AbliterationPipeline:
                     expert, ed, _FFN_OUT_NAMES,
                     norm_preserve=norm_preserve,
                     regularization=regularization,
+                    offload_roots=offload_roots,
                 )
                 expert_count += self._project_out_advanced(
                     expert, ed, _FFN_IN_NAMES,
                     norm_preserve=norm_preserve,
                     regularization=regularization,
+                    offload_roots=offload_roots,
                 )
                 if project_biases:
-                    expert_count += self._project_bias(expert, ed, _FFN_OUT_NAMES + _FFN_IN_NAMES)
+                    expert_count += self._project_bias(
+                        expert,
+                        ed,
+                        _FFN_OUT_NAMES + _FFN_IN_NAMES,
+                        offload_roots,
+                    )
         else:
             # Fused 3D: process per-expert with individual directions
             expert_count += self._project_fused_3d_granular(
@@ -5726,7 +5986,7 @@ class AbliterationPipeline:
 
         count += expert_count
         if count > 0:
-            self._stabilize_router_weights(ffn_module)
+            self._stabilize_router_weights(ffn_module, offload_roots)
         return count
 
     @staticmethod
diff --git a/obliteratus/models/offload_surgery.py b/obliteratus/models/offload_surgery.py
new file mode 100644
index 0000000..e09cdaf
--- /dev/null
+++ b/obliteratus/models/offload_surgery.py
@@ -0,0 +1,376 @@
+"""Transactional access to Accelerate-offloaded logical parameters.
+
+Accelerate represents an offloaded parameter with a live ``meta`` tensor and
+keeps the logical value in an ``AlignDevicesHook.weights_map``.  Surgery must
+therefore update that backing map without leaving a materialized live tensor or
+silently depending on an unknown hook layout.
+
+This module is the only OBLITERATUS compatibility boundary allowed to write to
+Accelerate backing stores.  Callers receive a detached logical tensor and commit
+the completed update atomically; exceptions before commit leave both the live
+module and the backing store unchanged.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, MutableMapping, Sequence
+from dataclasses import dataclass, field
+from importlib.metadata import PackageNotFoundError, version
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from obliteratus.runtime_contracts import classify_weight_storage
+
+
+class OffloadSurgeryError(RuntimeError):
+    """Base error for unsupported or inconsistent offloaded surgery state."""
+
+
+class UnsupportedOffloadLayoutError(OffloadSurgeryError):
+    """Accelerate exposed a layout that this compatibility adapter cannot write."""
+
+
+class UnsupportedOffloadedQuantizationError(OffloadSurgeryError):
+    """An offloaded packed or quantized parameter cannot be updated safely."""
+
+
+def _accelerate_release() -> tuple[int, int, int]:
+    try:
+        raw = version("accelerate").split("+", 1)[0]
+    except PackageNotFoundError as exc:  # pragma: no cover - dependency contract
+        raise UnsupportedOffloadLayoutError(
+            "Accelerate is required to resolve a meta-resident parameter"
+        ) from exc
+
+    numeric: list[int] = []
+    for part in raw.split(".")[:3]:
+        digits = "".join(character for character in part if character.isdigit())
+        numeric.append(int(digits or 0))
+    while len(numeric) < 3:
+        numeric.append(0)
+    release = tuple(numeric)
+    if not ((release[0] == 0 and release[1] >= 24) or release[0] == 1):
+        raise UnsupportedOffloadLayoutError(
+            f"Accelerate {raw} is outside the tested offload-surgery range "
+            "(>=0.24,<2); refusing compatibility access"
+        )
+    return release
+
+
+def _align_hooks(hook: Any) -> list[Any]:
+    """Return supported offloading hooks from direct or sequential layouts."""
+    from accelerate.hooks import AlignDevicesHook, SequentialHook
+
+    if isinstance(hook, AlignDevicesHook):
+        return [hook] if hook.offload else []
+    if isinstance(hook, SequentialHook):
+        hooks: list[Any] = []
+        for child in hook.hooks:
+            hooks.extend(_align_hooks(child))
+        return hooks
+    return []
+
+
+def _relative_module_path(owner: nn.Module, target: nn.Module) -> str | None:
+    for name, candidate in owner.named_modules():
+        if candidate is target:
+            return name
+    return None
+
+
+@dataclass(frozen=True)
+class _WriteTarget:
+    """A validated readable/writable view of one backing-store key."""
+
+    container: Any
+    key: str
+    loader: bool = False
+
+    def read(self) -> torch.Tensor:
+        value = self.container[self.key]
+        if not isinstance(value, torch.Tensor):
+            raise UnsupportedOffloadLayoutError(
+                f"Accelerate backing value {self.key!r} is not a tensor"
+            )
+        return value
+
+    def write(self, value: torch.Tensor) -> None:
+        if self.loader:
+            self.container.state_dict[self.key] = value
+            if self.key not in self.container.all_keys:
+                self.container.all_keys.append(self.key)
+            return
+        self.container[self.key] = value
+
+
+def _write_target(weights_map: Mapping[str, torch.Tensor], key: str) -> _WriteTarget:
+    """Resolve public mappings and the two version-gated Accelerate wrappers."""
+    from accelerate.utils.offload import OffloadedWeightsLoader, PrefixedDataset
+
+    if isinstance(weights_map, PrefixedDataset):
+        return _write_target(weights_map.dataset, f"{weights_map.prefix}{key}")
+    if isinstance(weights_map, OffloadedWeightsLoader):
+        return _WriteTarget(weights_map, key, loader=True)
+    if isinstance(weights_map, MutableMapping):
+        return _WriteTarget(weights_map, key)
+    raise UnsupportedOffloadLayoutError(
+        "Accelerate weights_map is read-only and has no supported writable "
+        f"backing store ({type(weights_map).__module__}.{type(weights_map).__name__})"
+    )
+
+
+def _same_storage(left: torch.Tensor, right: torch.Tensor) -> bool:
+    if left is right:
+        return True
+    if left.device.type == "meta" or right.device.type == "meta":
+        return False
+    return (
+        left.shape == right.shape
+        and left.dtype == right.dtype
+        and left.data_ptr() == right.data_ptr()
+    )
+
+
+def _alias_targets(primary: _WriteTarget, source: torch.Tensor) -> list[_WriteTarget]:
+    """Find inexpensive in-memory aliases so tied backing values stay tied."""
+    targets = [primary]
+    if primary.loader:
+        candidates = primary.container.state_dict.items()
+    elif isinstance(primary.container, MutableMapping):
+        candidates = primary.container.items()
+    else:  # pragma: no cover - construction guarantees one of the above
+        candidates = ()
+
+    for key, value in candidates:
+        if key == primary.key or not isinstance(value, torch.Tensor):
+            continue
+        if _same_storage(value, source):
+            targets.append(_WriteTarget(primary.container, key, loader=primary.loader))
+    return targets
+
+
+@dataclass
+class LogicalParameterTransaction:
+    """A detached logical parameter with an atomic commit operation."""
+
+    module: nn.Module
+    parameter_name: str
+    tensor: torch.Tensor
+    offloaded: bool
+    backing_key: str | None = None
+    _targets: list[_WriteTarget] = field(default_factory=list, repr=False)
+    _parameter_identity: int = field(init=False, repr=False)
+    _committed: bool = field(default=False, init=False, repr=False)
+    _rollback_values: list[tuple[_WriteTarget | None, torch.Tensor]] = field(
+        default_factory=list,
+        init=False,
+        repr=False,
+    )
+
+    def __post_init__(self) -> None:
+        self._parameter_identity = id(getattr(self.module, self.parameter_name))
+
+    def commit(self, updated: torch.Tensor) -> None:
+        """Commit a complete logical value without exposing partial mutation."""
+        parameter = getattr(self.module, self.parameter_name)
+        if id(parameter) != self._parameter_identity:
+            raise OffloadSurgeryError(
+                f"{self.parameter_name} identity changed during logical surgery"
+            )
+        if updated.shape != self.tensor.shape:
+            raise OffloadSurgeryError(
+                f"logical update changed shape from {tuple(self.tensor.shape)} "
+                f"to {tuple(updated.shape)}"
+            )
+        if not updated.is_floating_point():
+            raise UnsupportedOffloadedQuantizationError(
+                "logical surgery requires a floating-point result"
+            )
+
+        if not self.offloaded:
+            original = parameter.data.detach().clone()
+            with torch.no_grad():
+                parameter.data.copy_(updated.to(parameter.device, parameter.dtype))
+            self._rollback_values = [(None, original)]
+            self._committed = True
+            return
+
+        if parameter.device.type != "meta":
+            raise OffloadSurgeryError(
+                "offloaded parameter was unexpectedly materialized before commit"
+            )
+
+        committed = updated.detach().to(device="cpu", dtype=self.tensor.dtype).clone()
+        originals = [(target, target.read()) for target in self._targets]
+        try:
+            for target, _original in originals:
+                target.write(committed)
+        except Exception as exc:
+            for target, original in reversed(originals):
+                try:
+                    target.write(original)
+                except Exception:
+                    pass
+            raise OffloadSurgeryError(
+                f"failed to commit logical parameter {self.backing_key!r}; "
+                "the backing store was rolled back"
+            ) from exc
+
+        if parameter.device.type != "meta" or id(parameter) != self._parameter_identity:
+            for target, original in reversed(originals):
+                target.write(original)
+            raise OffloadSurgeryError(
+                "offload invariant changed during commit; backing update rolled back"
+            )
+        self._rollback_values = originals
+        self._committed = True
+
+    def rollback(self) -> None:
+        """Restore the value captured by the most recent successful commit."""
+        if not self._committed:
+            return
+        parameter = getattr(self.module, self.parameter_name)
+        if id(parameter) != self._parameter_identity:
+            raise OffloadSurgeryError(
+                f"cannot roll back changed {self.parameter_name} identity"
+            )
+        if self.offloaded:
+            for target, original in reversed(self._rollback_values):
+                if target is not None:
+                    target.write(original)
+            if parameter.device.type != "meta":
+                raise OffloadSurgeryError(
+                    "offloaded parameter was materialized during rollback"
+                )
+        else:
+            original = self._rollback_values[0][1]
+            with torch.no_grad():
+                parameter.data.copy_(original)
+        self._rollback_values = []
+        self._committed = False
+
+
+def resolve_logical_parameter(
+    module: nn.Module,
+    parameter_name: str = "weight",
+    *,
+    search_roots: Sequence[nn.Module] = (),
+) -> LogicalParameterTransaction:
+    """Resolve a live or Accelerate-offloaded parameter for safe surgery.
+
+    ``search_roots`` supplies known ancestors such as the decoder layer.  This
+    is required when Accelerate attaches one ``place_submodules`` hook to a
+    parent and stores keys such as ``self_attn.o_proj.weight``.
+    """
+    parameter = getattr(module, parameter_name, None)
+    if parameter is None or not isinstance(parameter, (nn.Parameter, torch.Tensor)):
+        raise OffloadSurgeryError(
+            f"module has no tensor parameter named {parameter_name!r}"
+        )
+    if parameter.device.type != "meta":
+        return LogicalParameterTransaction(
+            module=module,
+            parameter_name=parameter_name,
+            tensor=parameter.data,
+            offloaded=False,
+        )
+
+    _accelerate_release()
+    roots: list[nn.Module] = []
+    for root in (module, *search_roots):
+        if all(root is not existing for existing in roots):
+            roots.append(root)
+
+    candidates: list[tuple[int, nn.Module, str, Any]] = []
+    inspected_hooks = 0
+    for root in roots:
+        for _owner_name, owner in root.named_modules():
+            relative = _relative_module_path(owner, module)
+            if relative is None:
+                continue
+            hook = getattr(owner, "_hf_hook", None)
+            if hook is None:
+                continue
+            align_hooks = _align_hooks(hook)
+            inspected_hooks += len(align_hooks)
+            key = f"{relative}.{parameter_name}" if relative else parameter_name
+            for align_hook in align_hooks:
+                candidates.append((relative.count("."), owner, key, align_hook))
+
+    # A hook closest to the target has the shortest relative key and is the
+    # least ambiguous source of truth.
+    candidates.sort(key=lambda candidate: candidate[0])
+    attempted: list[str] = []
+    for _depth, _owner, key, hook in candidates:
+        weights_map = getattr(hook, "weights_map", None)
+        if weights_map is None:
+            continue
+        attempted.append(key)
+        try:
+            source = weights_map[key]
+        except (KeyError, IndexError):
+            continue
+        if not isinstance(source, torch.Tensor):
+            raise UnsupportedOffloadLayoutError(
+                f"Accelerate backing value {key!r} is not a tensor"
+            )
+
+        storage_kind = classify_weight_storage(
+            module_class_name=module.__class__.__name__,
+            parameter_class_name=parameter.__class__.__name__,
+            has_quant_state=hasattr(parameter, "quant_state"),
+            data_is_floating_point=source.is_floating_point(),
+        )
+        if storage_kind != "float":
+            raise UnsupportedOffloadedQuantizationError(
+                f"offloaded {storage_kind} parameter {key!r} cannot be "
+                "safely updated before supported quantized write-back exists"
+            )
+
+        primary = _write_target(weights_map, key)
+        return LogicalParameterTransaction(
+            module=module,
+            parameter_name=parameter_name,
+            tensor=source,
+            offloaded=True,
+            backing_key=key,
+            _targets=_alias_targets(primary, source),
+        )
+
+    attempted_text = ", ".join(dict.fromkeys(attempted)) or parameter_name
+    if inspected_hooks == 0:
+        raise UnsupportedOffloadLayoutError(
+            f"meta-resident parameter {parameter_name!r} has no supported "
+            "offloading AlignDevicesHook in the supplied module roots"
+        )
+    raise UnsupportedOffloadLayoutError(
+        f"no authoritative Accelerate backing value found for meta parameter; "
+        f"attempted keys: {attempted_text}"
+    )
+
+
+def validate_offloaded_parameters(root: nn.Module) -> None:
+    """Fail closed on every meta parameter before a surgery pass mutates data."""
+    seen: set[int] = set()
+    for _module_name, module in root.named_modules():
+        for parameter_name, parameter in module.named_parameters(recurse=False):
+            if parameter.device.type != "meta" or id(parameter) in seen:
+                continue
+            resolve_logical_parameter(module, parameter_name, search_roots=(root,))
+            seen.add(id(parameter))
+
+
+def logical_module_device(root: nn.Module) -> torch.device:
+    """Return a usable compute device even when a layer starts on ``meta``."""
+    for _module_name, module in root.named_modules():
+        for parameter_name, parameter in module.named_parameters(recurse=False):
+            if parameter.device.type != "meta":
+                return parameter.device
+            return resolve_logical_parameter(
+                module,
+                parameter_name,
+                search_roots=(root,),
+            ).tensor.device
+    raise OffloadSurgeryError("module has no parameters from which to select a device")
diff --git a/tests/conditional/test_cuda_runtime.py b/tests/conditional/test_cuda_runtime.py
index 2ac4bee..0600c59 100644
--- a/tests/conditional/test_cuda_runtime.py
+++ b/tests/conditional/test_cuda_runtime.py
@@ -4,8 +4,11 @@ from __future__ import annotations
 
 import pytest
 import torch
+import torch.nn as nn
+from accelerate.hooks import AlignDevicesHook, add_hook_to_module
 
 from obliteratus import device
+from obliteratus.abliterate import AbliterationPipeline
 
 
 pytestmark = pytest.mark.gpu
@@ -23,6 +26,31 @@ def test_cuda_discovery_dtype_placement_and_operation():
     assert torch.isfinite(result).all()
 
 
+def test_cuda_execution_observes_offloaded_surgery_and_restores_meta_state():
+    if not torch.cuda.is_available():
+        pytest.skip("requires a CUDA runner; set ENABLE_CUDA_GATE and attach the cuda label")
+
+    module = nn.Module()
+    module.proj = nn.Linear(4, 4, bias=False)
+    original = module.proj.weight.detach().clone()
+    hook = AlignDevicesHook(execution_device="cuda", offload=True)
+    add_hook_to_module(module.proj, hook)
+
+    count = AbliterationPipeline._project_out_advanced(
+        module,
+        torch.tensor([[1.0], [0.0], [0.0], [0.0]], device="cuda"),
+        ["proj"],
+    )
+    output = module.proj(torch.ones(1, 4, device="cuda"))
+
+    expected = original.clone()
+    expected[:, 0] = 0
+    assert count == 1
+    assert output.device.type == "cuda"
+    assert module.proj.weight.device.type == "meta"
+    torch.testing.assert_close(hook.weights_map["weight"], expected)
+
+
 def test_bitsandbytes_quantization_operation():
     if not torch.cuda.is_available():
         pytest.skip("requires a CUDA runner; set ENABLE_CUDA_GATE and attach the cuda label")
diff --git a/tests/test_offload_surgery.py b/tests/test_offload_surgery.py
new file mode 100644
index 0000000..b002ea2
--- /dev/null
+++ b/tests/test_offload_surgery.py
@@ -0,0 +1,391 @@
+"""CPU-only contracts for transactional Accelerate offload surgery."""
+
+from __future__ import annotations
+
+from collections.abc import MutableMapping
+
+import pytest
+import torch
+import torch.nn as nn
+from accelerate.big_modeling import disk_offload
+from accelerate.hooks import (
+    AlignDevicesHook,
+    ModelHook,
+    SequentialHook,
+    add_hook_to_module,
+)
+from accelerate.utils.modeling import get_state_dict_offloaded_model
+
+import obliteratus.models.offload_surgery as offload_surgery
+from obliteratus.abliterate import AbliterationPipeline
+from obliteratus.models.offload_surgery import (
+    OffloadSurgeryError,
+    UnsupportedOffloadLayoutError,
+    UnsupportedOffloadedQuantizationError,
+    logical_module_device,
+    resolve_logical_parameter,
+    validate_offloaded_parameters,
+)
+
+
+def _direct_offload(module: nn.Module) -> AlignDevicesHook:
+    hook = AlignDevicesHook(execution_device="cpu", offload=True)
+    add_hook_to_module(module, hook)
+    return hook
+
+
+def test_direct_align_hook_updates_backing_weight_and_preserves_meta_state():
+    module = nn.Linear(3, 2, bias=False)
+    original = module.weight.detach().clone()
+    hook = _direct_offload(module)
+    parameter_identity = id(module.weight)
+
+    transaction = resolve_logical_parameter(module)
+    transaction.commit(transaction.tensor + 2)
+
+    assert module.weight.device.type == "meta"
+    assert id(module.weight) == parameter_identity
+    torch.testing.assert_close(hook.weights_map["weight"], original + 2)
+
+
+def test_sequential_hook_finds_nested_align_devices_hook():
+    module = nn.Linear(3, 2, bias=False)
+    original = module.weight.detach().clone()
+    align = AlignDevicesHook(execution_device="cpu", offload=True)
+    add_hook_to_module(module, SequentialHook(ModelHook(), align))
+
+    transaction = resolve_logical_parameter(module)
+    transaction.commit(transaction.tensor - 1)
+
+    assert module.weight.device.type == "meta"
+    torch.testing.assert_close(align.weights_map["weight"], original - 1)
+
+
+def test_parent_prefixed_weight_map_resolves_nested_parameter():
+    block = nn.Module()
+    block.self_attn = nn.Module()
+    block.self_attn.o_proj = nn.Linear(3, 2, bias=False)
+    original = block.self_attn.o_proj.weight.detach().clone()
+    hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        place_submodules=True,
+    )
+    add_hook_to_module(block, hook)
+
+    transaction = resolve_logical_parameter(
+        block.self_attn.o_proj,
+        search_roots=(block,),
+    )
+    assert transaction.backing_key == "self_attn.o_proj.weight"
+    transaction.commit(transaction.tensor * 0.5)
+
+    assert block.self_attn.o_proj.weight.device.type == "meta"
+    torch.testing.assert_close(
+        hook.weights_map["self_attn.o_proj.weight"],
+        original * 0.5,
+    )
+
+
+def test_disk_backed_prefixed_loader_uses_updated_authoritative_value(tmp_path):
+    model = nn.Sequential(nn.Linear(3, 2, bias=False))
+    original = model[0].weight.detach().clone()
+    disk_offload(model, tmp_path, execution_device=torch.device("cpu"))
+
+    transaction = resolve_logical_parameter(model[0], search_roots=(model,))
+    transaction.commit(transaction.tensor + 3)
+
+    assert model[0].weight.device.type == "meta"
+    hook = model[0]._hf_hook
+    torch.testing.assert_close(hook.weights_map["weight"], original + 3)
+    gathered = get_state_dict_offloaded_model(model)
+    assert all(tensor.device.type != "meta" for tensor in gathered.values())
+    torch.testing.assert_close(gathered["0.weight"], original + 3)
+
+
+def test_save_reload_observes_surgery_instead_of_stale_backing_weight():
+    model = nn.Sequential(nn.Linear(3, 2, bias=False))
+    original = model[0].weight.detach().clone()
+    _direct_offload(model[0])
+
+    transaction = resolve_logical_parameter(model[0], search_roots=(model,))
+    transaction.commit(transaction.tensor - 4)
+    state_dict = get_state_dict_offloaded_model(model)
+
+    reloaded = nn.Sequential(nn.Linear(3, 2, bias=False))
+    reloaded.load_state_dict(state_dict)
+    torch.testing.assert_close(reloaded[0].weight, original - 4)
+    assert reloaded[0].weight.device.type == "cpu"
+
+
+def test_tied_backing_values_retain_identity_and_consistent_values():
+    block = nn.Module()
+    block.first = nn.Linear(3, 3, bias=False)
+    block.second = nn.Linear(3, 3, bias=False)
+    shared = block.first.weight.detach().clone()
+    shared_meta = nn.Parameter(torch.empty_like(shared, device="meta"))
+    block.first.weight = shared_meta
+    block.second.weight = shared_meta
+    hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        weights_map={"first.weight": shared, "second.weight": shared},
+        place_submodules=True,
+    )
+    block._hf_hook = hook
+    live_identity = id(block.first.weight)
+
+    transaction = resolve_logical_parameter(block.first, search_roots=(block,))
+    transaction.commit(transaction.tensor + 1)
+
+    first = hook.weights_map["first.weight"]
+    second = hook.weights_map["second.weight"]
+    assert first is second
+    torch.testing.assert_close(first, shared + 1)
+    assert block.first.weight is block.second.weight
+    assert id(block.first.weight) == live_identity
+
+
+class _FailOnceAliasMap(MutableMapping[str, torch.Tensor]):
+    def __init__(self, shared: torch.Tensor):
+        self.values = {"first.weight": shared, "second.weight": shared}
+        self.failed = False
+
+    def __getitem__(self, key: str) -> torch.Tensor:
+        return self.values[key]
+
+    def __setitem__(self, key: str, value: torch.Tensor) -> None:
+        if key == "second.weight" and not self.failed:
+            self.failed = True
+            raise OSError("simulated backing-store failure")
+        self.values[key] = value
+
+    def __delitem__(self, key: str) -> None:
+        del self.values[key]
+
+    def __iter__(self):
+        return iter(self.values)
+
+    def __len__(self) -> int:
+        return len(self.values)
+
+
+def test_commit_failure_rolls_back_all_backing_aliases_and_leaves_meta_state():
+    block = nn.Module()
+    block.first = nn.Linear(3, 3, bias=False)
+    original = block.first.weight.detach().clone()
+    block.first.weight = nn.Parameter(torch.empty_like(original, device="meta"))
+    backing = _FailOnceAliasMap(original)
+    block._hf_hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        weights_map=backing,
+        place_submodules=True,
+    )
+
+    transaction = resolve_logical_parameter(block.first, search_roots=(block,))
+    with pytest.raises(OffloadSurgeryError, match="rolled back"):
+        transaction.commit(transaction.tensor + 5)
+
+    torch.testing.assert_close(backing["first.weight"], original)
+    torch.testing.assert_close(backing["second.weight"], original)
+    assert block.first.weight.device.type == "meta"
+
+
+def test_quantized_offloaded_weight_fails_before_any_mutation():
+    module = nn.Linear(3, 2, bias=False, device="meta")
+    backing = {"weight": torch.ones(2, 3, dtype=torch.uint8)}
+    module._hf_hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        weights_map=backing,
+    )
+
+    with pytest.raises(UnsupportedOffloadedQuantizationError):
+        resolve_logical_parameter(module)
+
+    assert module.weight.device.type == "meta"
+    assert backing["weight"].dtype == torch.uint8
+    assert torch.equal(backing["weight"], torch.ones(2, 3, dtype=torch.uint8))
+
+
+def test_read_only_unknown_mapping_fails_during_resolution():
+    module = nn.Linear(3, 2, bias=False, device="meta")
+
+    class MappingOnly:
+        def __getitem__(self, key):
+            return torch.ones(2, 3)
+
+        def __iter__(self):
+            return iter(("weight",))
+
+        def __len__(self):
+            return 1
+
+    # Registering through Mapping is deliberately avoided: this object models
+    # a future wrapper with read access but no supported write contract.
+    hook = AlignDevicesHook(execution_device="cpu", offload=True)
+    hook.weights_map = MappingOnly()
+    module._hf_hook = hook
+
+    with pytest.raises(UnsupportedOffloadLayoutError, match="read-only"):
+        resolve_logical_parameter(module)
+
+
+def test_validation_resolves_all_meta_parameters_before_surgery():
+    block = nn.Module()
+    block.proj = nn.Linear(3, 2, bias=False)
+    hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        place_submodules=True,
+    )
+    add_hook_to_module(block, hook)
+
+    validate_offloaded_parameters(block)
+
+    hook.weights_map = {}
+    with pytest.raises(UnsupportedOffloadLayoutError, match="attempted keys"):
+        validate_offloaded_parameters(block)
+
+
+def test_advanced_projection_updates_offloaded_logical_weight():
+    module = nn.Module()
+    module.proj = nn.Linear(3, 2, bias=False)
+    original = module.proj.weight.detach().clone()
+    hook = _direct_offload(module.proj)
+    direction = torch.tensor([[1.0], [0.0], [0.0]])
+
+    count = AbliterationPipeline._project_out_advanced(
+        module,
+        direction,
+        ["proj"],
+    )
+
+    assert count == 1
+    assert module.proj.weight.device.type == "meta"
+    expected = original.clone()
+    expected[:, 0] = 0
+    torch.testing.assert_close(hook.weights_map["weight"], expected)
+
+
+def test_advanced_projection_resolves_all_candidates_before_mutation():
+    block = nn.Module()
+    block.first = nn.Linear(3, 2, bias=False)
+    first_original = block.first.weight.detach().clone()
+    first_hook = _direct_offload(block.first)
+    block.second = nn.Linear(3, 2, bias=False, device="meta")
+    block.second._hf_hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        weights_map={"weight": torch.ones(2, 3, dtype=torch.uint8)},
+    )
+
+    with pytest.raises(UnsupportedOffloadedQuantizationError):
+        AbliterationPipeline._project_out_advanced(
+            block,
+            torch.tensor([[1.0], [0.0], [0.0]]),
+            ["first", "second"],
+        )
+
+    torch.testing.assert_close(first_hook.weights_map["weight"], first_original)
+    assert block.first.weight.device.type == "meta"
+
+
+def test_logical_module_device_uses_authoritative_backing_device():
+    module = nn.Linear(3, 2, bias=False)
+    _direct_offload(module)
+
+    assert logical_module_device(module) == torch.device("cpu")
+
+
+def test_unknown_accelerate_major_version_fails_closed(monkeypatch):
+    module = nn.Linear(3, 2, bias=False)
+    original = module.weight.detach().clone()
+    hook = _direct_offload(module)
+    monkeypatch.setattr(offload_surgery, "version", lambda _name: "2.0.0")
+
+    with pytest.raises(UnsupportedOffloadLayoutError, match="outside the tested"):
+        resolve_logical_parameter(module)
+
+    assert module.weight.device.type == "meta"
+    torch.testing.assert_close(hook.weights_map["weight"], original)
+
+
+def test_bias_projection_updates_parent_prefixed_backing_value():
+    block = nn.Module()
+    block.proj = nn.Linear(3, 3, bias=True)
+    original = block.proj.bias.detach().clone()
+    hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        place_submodules=True,
+    )
+    add_hook_to_module(block, hook)
+
+    count = AbliterationPipeline._project_bias(
+        block,
+        torch.tensor([[1.0], [0.0], [0.0]]),
+        ["proj"],
+        offload_roots=(block,),
+    )
+
+    assert count == 1
+    assert block.proj.bias.device.type == "meta"
+    expected = original.clone()
+    expected[0] = 0
+    torch.testing.assert_close(hook.weights_map["proj.bias"], expected)
+
+
+class _FailSecondParameterMap(MutableMapping[str, torch.Tensor]):
+    def __init__(self, first: torch.Tensor, second: torch.Tensor):
+        self.values = {"first.weight": first, "second.weight": second}
+        self.failed = False
+
+    def __getitem__(self, key: str) -> torch.Tensor:
+        return self.values[key]
+
+    def __setitem__(self, key: str, value: torch.Tensor) -> None:
+        if key == "second.weight" and not self.failed:
+            self.failed = True
+            raise OSError("simulated second-parameter failure")
+        self.values[key] = value
+
+    def __delitem__(self, key: str) -> None:
+        del self.values[key]
+
+    def __iter__(self):
+        return iter(self.values)
+
+    def __len__(self) -> int:
+        return len(self.values)
+
+
+def test_multi_parameter_projection_rolls_back_prior_commits():
+    block = nn.Module()
+    block.first = nn.Linear(3, 2, bias=False)
+    block.second = nn.Linear(3, 2, bias=False)
+    first = block.first.weight.detach().clone()
+    second = block.second.weight.detach().clone()
+    block.first.weight = nn.Parameter(torch.empty_like(first, device="meta"))
+    block.second.weight = nn.Parameter(torch.empty_like(second, device="meta"))
+    backing = _FailSecondParameterMap(first, second)
+    block._hf_hook = AlignDevicesHook(
+        execution_device="cpu",
+        offload=True,
+        weights_map=backing,
+        place_submodules=True,
+    )
+
+    with pytest.raises(OffloadSurgeryError, match="rolled back"):
+        AbliterationPipeline._project_out_advanced(
+            block,
+            torch.tensor([[1.0], [0.0], [0.0]]),
+            ["first", "second"],
+            offload_roots=(block,),
+        )
+
+    torch.testing.assert_close(backing["first.weight"], first)
+    torch.testing.assert_close(backing["second.weight"], second)
+    assert block.first.weight.device.type == "meta"
+    assert block.second.weight.device.type == "meta"