Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models
We present a feedforward graph architecture in which heterogeneous frozen large language models serve as computational nodes, communicating through a shared continuous latent space via learned linear projections. Building on recent work demonstrating geometric compatibility between independently trained LLM latent spaces~\cite{armstrong2026thinking}, we extend this finding from static two-model steering to end-to-end trainable multi-node graphs, where projection matrices are optimized jointly via backpropagation through residual stream injection hooks. Three small frozen models (Llama-3.2-1B, Qwen2.5-1.5B, Gemma-2-2B) encode the input into a shared latent space whose aggregate signal is i...
| Authors | Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 11 September 2026 |
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