Recursive Mamba loops hidden states for small-model reasoning

Using temporal recursion to decouple compute depth from parameter count is a genuinely clever idea — but the "cognitive static" ceiling reveals a fundamental tension in small SSMs between representational capacity and reasoning depth.

• –At N=3 recursive passes, the 8-layer 150M model can hold abstract transitive variables across passes — a promising signal that SSMs are viable reasoning substrates beyond simple next-token prediction
• –At N=10 (80 effective layers), linguistic circuits collapse into semantic noise, suggesting the latent space simply lacks the capacity to simultaneously encode deep logic and vocabulary
• –The entropy-based Auto-N scaler is an interesting meta-controller idea — similar in spirit to adaptive compute approaches like PonderNet or mixture-of-depths, but applied to SSM hidden state loops
• –BoolQ at 33% is expected for a 150M model with no world knowledge, but the abstract variable mapping result is the real signal worth watching
• –The core open question — whether latent space collapse in recursive SSMs is an architectural dead end or solvable with better training objectives — is worth real experimental follow-up