diff --git a/backlog/features/2025-08-15_lfm-kernel-code-review.md b/backlog/features/2025-08-15_lfm-kernel-code-review.md
index 8c53d7ff..c93a9cd0 100644
--- a/backlog/features/2025-08-15_lfm-kernel-code-review.md
+++ b/backlog/features/2025-08-15_lfm-kernel-code-review.md
@@ -1,7 +1,7 @@
 ---
 id: "lfm-kernel-code-review"
 title: "Review existing LFM kernel implementations"
-status: "Ready"
+status: "In Progress"
 priority: "High"
 created: "2025-08-15"
 last_updated: "2025-08-15"
@@ -26,8 +26,8 @@ Conduct a comprehensive review of existing LFM (Latent Force Model) kernel imple
 - Need to understand differences and identify modernization opportunities
 
 ## Tasks
-- [ ] Review `GPy/kern/src/eq_ode1.py` and `eq_ode2.py` implementations
-- [ ] Analyze MATLAB LFM implementation structure and patterns
+- [x] Review `GPy/kern/src/eq_ode1.py` and `eq_ode2.py` implementations
+- [x] Analyze MATLAB LFM implementation structure and patterns
 - [ ] Document current limitations and inconsistencies
 - [ ] Identify reusable components and design patterns
 - [ ] Compare parameter handling approaches
@@ -50,3 +50,26 @@ Conduct a comprehensive review of existing LFM (Latent Force Model) kernel imple
 ## Related
 - CIP: 0001 (LFM kernel implementation)
 - Papers: Álvarez et al. (2009, 2012), Lawrence et al. (2006)
+
+## Progress Updates
+
+### 2025-08-15
+Started code review task. Initial findings:
+
+**GPy Implementations:**
+- `EQ_ODE1`: First-order differential equation kernel with decay rates and sensitivities
+- `EQ_ODE2`: Second-order differential equation kernel with spring/damper constants
+- Both use GPy's multioutput approach with output index as second input dimension
+- Complex kernel computation with multiple covariance types (Kuu, Kfu, Kuf, Kusu)
+- Uses `@Cache_this` decorator for performance optimization
+
+**MATLAB Implementation:**
+- More complete framework with `lfmCreate`, `lfmKernCompute`, `lfmKernParamInit`
+- Uses multi-kernel approach with parameter tying
+- Supports multiple displacements driven by multiple forces
+- Cleaner separation of concerns with dedicated model creation
+
+**Key Differences:**
+- GPy uses single kernel class per ODE order, MATLAB uses multi-kernel composition
+- GPy has more complex index handling for multioutput
+- MATLAB has better parameter organization and tying mechanisms