Refactor TitansMAG forward logic and fix memory state handling

[ryancinsight]

This PR refactors the TitansMAG forward pass implementation to ensure correctness and maintainability.

Key changes:

1. State Consistency: Added reset_memory() at the start of forward. In the Titans architecture, the neural memory is typically treated as "fast weights" learned over the context window. The gradient computation (compute_gradients) assumes the memory starts at init_memory. Without an explicit reset in forward, the memory state would persist across calls (e.g., across training batches), leading to a disconnect between the forward pass state and the backward pass assumptions. This fixes potential bugs in training loops.
2. Code Deduplication: Removed the local mlp_forward helper function in mag.rs and exposed the existing implementation in NeuralMemory (neural.rs) to the crate. This eliminates code duplication.
3. Verification: Added a regression test test_titans_mag_deterministic_forward to confirm that the module is stateless across calls, as expected for a standard layer in this context. All existing tests passed.

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PR created automatically by Jules for task 6565573961390046446 started by @ryancinsight

High-level PR Summary

This PR refactors the TitansMAG forward pass to fix memory state handling and eliminate code duplication. The key fix adds reset_memory() at the start of forward to ensure the memory state is reset between calls, preventing state persistence across training batches that would violate backward pass assumptions. The refactor also removes a duplicated mlp_forward helper function and exposes the existing implementation from NeuralMemory as pub(crate) instead. A new test verifies that forward passes are now deterministic across calls.

⏱️ Estimated Review Time: 5-15 minutes

💡 Review Order Suggestion

Order	File Path
1	src/memory/titans/neural.rs
2	src/memory/titans/mag.rs

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[gemini-code-assist]

Summary of Changes

Hello @ryancinsight, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refactors the TitansMAG module to enhance its correctness and maintainability. It addresses a critical issue where neural memory state could persist across forward passes, leading to inconsistencies between forward and backward computations, by implementing an explicit memory reset. Additionally, it improves code hygiene by centralizing the mlp_forward function and includes a new test to validate the module's deterministic behavior.

Highlights

• Memory State Handling: The TitansMAG::forward method now explicitly calls self.memory.reset_memory() at its beginning. This ensures that the neural memory state does not persist across different forward passes (e.g., training batches), aligning the forward pass behavior with the backward pass assumptions and preventing potential training bugs.
• MLP Forward Function Refactoring: The mlp_forward helper function, previously defined locally within src/memory/titans/mag.rs, has been removed. Instead, the existing mlp_forward method in NeuralMemory (from src/memory/titans/neural.rs) has been made pub(crate) and is now used directly, eliminating redundant code.
• Added Deterministic Forward Pass Test: A new regression test, test_titans_mag_deterministic_forward, has been added to src/memory/titans/mag.rs. This test verifies that the TitansMAG module's forward pass is deterministic and stateless across calls, confirming the intended behavior after the memory reset.

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[recurseml]

Review by RecurseML

🔍 Review performed on a004bfd..7f24bef

✨ No bugs found, your code is sparkling clean

✅ Files analyzed, no issues (2)

• src/memory/titans/mag.rs
• src/memory/titans/neural.rs

[gemini-code-assist]

Code Review

This pull request introduces a critical fix to the TitansMAG forward pass by ensuring the memory state is reset between calls. This prevents state leakage across batches and aligns the forward pass with the assumptions of the backward pass, resolving a potential bug in training loops. The changes also include a valuable refactoring that removes duplicated code by centralizing the mlp_forward logic into the NeuralMemory implementation. A new regression test has been added to verify that the forward pass is now deterministic. The changes are well-implemented, clearly explained, and improve both the correctness and maintainability of the code.