MeSH: Memory-as-State-Highways for Recursive Transformers
This work addresses a specific problem in recursive transformer architectures for AI researchers, offering an incremental improvement with measurable gains.
The paper tackled the performance gap in recursive transformers by identifying bottlenecks of undifferentiated computation and information overload, and introduced the MeSH scheme which improved average downstream accuracy by +1.06% with 33% fewer parameters compared to non-recursive counterparts at the 1.4B scale.
Recursive transformers reuse parameters and iterate over hidden states multiple times, decoupling compute depth from parameter depth. However, under matched compute, recursive models with fewer parameters often lag behind non-recursive counterparts. By probing hidden states, we trace this performance gap to two primary bottlenecks: undifferentiated computation, where the core is forced to adopt a similar computational pattern at every iteration, and information overload, where long-lived and transient information must coexist in a single hidden state. To address the issues, we introduce a Memory-as-State-Highways (MeSH) scheme, which externalizes state management into an explicit memory buffer and employs lightweight routers to dynamically diversify computation across iterations. Probing visualizations confirm that MeSH successfully resolves the pathologies by inducing functional specialization across iterations. On the Pythia suite (160M-1.4B), MeSH-enhanced recursive transformers consistently improve over recursive baselines and outperforms its larger non-recursive counterpart at the 1.4B scale, improving average downstream accuracy by +1.06% with 33% fewer non-embedding parameters. Our analysis establishes MeSH as a scalable and principled architecture for building stronger recursive models.