Toward Semantic Memory Injection: Establishing Controllability in Mamba-2 States
Phase 1 of the ALSI project has successfully established the technical feasibility of latent state steering in Mamba-2. We have moved from theoretical hypothesis to a working engineering foundation (Functional Mamba Engine).
While we can now force target tokens with Rank 1 accuracy, the Semantic Gap (injecting facts) and the Coherence Gap (maintaining grammar) remain the primary research frontiers for Phase 2.
- Differentiable Recurrence: Re-implemented Mamba-2 functionals to bypass autograd blockers.
- Manifold Probing: Falsified linear steering; validated non-linear projection.
- Optimal Targeting: Identified Layer 16 as the "Sweet Spot" for latent intervention.
- Trajectory Stabilisation: Proved that multi-step BPTT can mitigate limit cycles (loops).
We adopt a discipline of transparent retraction. The following reports document early misconceptions that led to technical breakthroughs:
- The Refusal Artifact: Debunking the "Safety Reflex" theory as a cache misalignment bug.
- Linear Failure: Proving that Transformer-style linear steering does not translate to SSMs.
- Functional Control Breakthrough
- Trajectory Shaping Success
- The Sweet Spot Analysis
- MockCache Failure Analysis
- Optimization Autograd Blocker
- Distributed Control Analysis
- Limitations and Risk Analysis
Final Status (Phase 1): FOUNDATIONS ESTABLISHED. The steering wheel exists. Phase 2 will focus on the GPS (Semantic Encoding).