Core Space Merging Algorithm | Feature Request

README.md

@@ -272,6 +272,7 @@ For detailed explanations, parameter descriptions, and use cases for each method
 | [**Multi-SLERP** (`multislerp`)](docs/merge_methods.md#multi-slerp-multislerp)                                          | Barycentric SLERP for multiple models.                               |    ≥2    |  *   | Spherical interpolation for >2 models.                          |
 | [**Karcher Mean** (`karcher`)](docs/merge_methods.md#karcher-mean-karcher)                                              | Riemannian barycenter of model parameters.                           |    ≥2    |  -   | Geometrically sound averaging on manifolds.                     |
 | [**Task Arithmetic** (`task_arithmetic`)](docs/merge_methods.md#task-arithmetic-task_arithmetic)                      | Linearly combine "task vectors" (differences from a base).           |    ≥2    |  ✓   | Transferring/combining fine-tuned skills.                       |
+| [**Core Space** (`core_space`)](docs/merge_methods.md#core-space-core_space)                                            | SVD-aligned LoRA merging in compact core subspace.                   |    ≥2    |  ✓   | Efficient LoRA merging, heterogeneous ranks, subspace alignment.|
 | [**TIES** (`ties`)](docs/merge_methods.md#ties-merging-ties)                                                          | Task arithmetic + sparsification & sign consensus.                   |    ≥2    |  ✓   | Merging many models, reducing interference.                     |
 | [**DARE** (`dare_linear`, `dare_ties`)](docs/merge_methods.md#dare-dare_linear-dare_ties)                               | Task arithmetic + random pruning & rescaling.                        |    ≥2    |  ✓   | Robust skill retention, similar to TIES.                        |
 | [**DELLA** (`della`, `della_linear`)](docs/merge_methods.md#della-della-della_linear)                                   | Task arithmetic + adaptive magnitude-based pruning.                  |    ≥2    |  ✓   | Prioritizing important changes, reducing interference.          |

docs/merge_methods.md

@@ -12,6 +12,7 @@
   - [Karcher Mean (`karcher`)](#karcher-mean-karcher)
 - [Task Vector Methods](#task-vector-methods)
   - [Task Arithmetic (`task_arithmetic`)](#task-arithmetic-task_arithmetic)
+  - [Core Space (`core_space`)](#core-space)
   - [TIES-Merging (`ties`)](#ties-merging-ties)
   - [DARE (`dare_linear`, `dare_ties`)](#dare-dare_linear-dare_ties)
   - [DELLA (`della`, `della_linear`)](#della-della-della_linear)
@@ -149,6 +150,46 @@ This guide provides detailed information about the various model merging algorit
 
 **Reference:** [Editing Models with Task Arithmetic](https://arxiv.org/abs/2212.04089)
 
+### Core Space (`core_space`)
+
+**Concept**: Merges LoRA-adapted models by projecting them into a shared, aligned core space using SVD-based reference bases. Operates in a compact subspace for efficiency while preserving information.
+
+**Algorithm**:
+
+1. Extract LoRA matrices (B, A) from each model where ΔW = B @ A
+2. Compute reference bases via SVD: concatenate all B matrices horizontally and A matrices vertically, then compute orthonormal bases U_B and V_A
+3. Project to core space: Core_i = U_B^T @ B_i @ A_i @ V_A
+4. Merge in core space using weighted average
+5. Reconstruct: ΔW_merged = U_B @ Core_merged @ V_A^T, then W_final = W_base + ΔW_merged
+
+**Inputs**: Requires 2 or more models, plus one `base_model`.
+
+**Parameters**:
+
+- `weight` (per-model, float, default: 1.0): Weight for each model. Currently uses equal weights.
+
+**Use Cases**:
+
+- Efficiently merging multiple LoRA adapters
+- Multi-task model creation from specialized adapters
+- When adapters have different ranks
+- Resource-constrained environments
+
+**Example**:
+
+```yaml
+models:
+  - model: meta-llama/Llama-2-7b-hf
+  - model: username/llama2-lora-math
+  - model: username/llama2-lora-code
+
+merge_method: core_space
+base_model: meta-llama/Llama-2-7b-hf
+dtype: bfloat16
+```
+
+**Reference**: [Accurate and Efficient Low-Rank Model Merging in Core Space](https://arxiv.org/abs/2509.17786) (Panariello et al., NeurIPS 2025)
+
 ### TIES-Merging (`ties`)
 
 **Concept:** Builds on Task Arithmetic by sparsifying task vectors and applying a sign consensus algorithm. This helps to resolve interference when merging multiple models and retain more of their individual strengths.

examples/core_space.yml

@@ -0,0 +1,11 @@
+models:
+  - model: gpt2
+    parameters:
+      weight: 0.5
+  - model: gpt2
+    parameters:
+      weight: 1.0
+
+merge_method: core_space
+base_model: gpt2
+dtype: float32