[2605.11167] The Bicameral Model: Bidirectional Hidden-State Coupling Between Parallel Language Models
[Submitted on 11 May 2026]
Title:The Bicameral Model: Bidirectional Hidden-State Coupling Between Parallel Language Models
View a PDF of the paper titled The Bicameral Model: Bidirectional Hidden-State Coupling Between Parallel Language Models, by Cedric Flamant and 2 other authors
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Abstract:Existing multi-model and tool-augmented systems communicate by generating text, serializing every exchange through the output vocabulary. Can two pretrained language models instead coordinate through a continuous, concurrent channel? The Bicameral Model couples two frozen language models through a trainable neural interface on their intermediate hidden states. At every generation step, both models run in lockstep: a primary model drives the task while an auxiliary model operates tools, solves constraints, or executes code, with both conditioning on each other's activations through a translation network and a learned suppression gate ($\sim$1\% of combined parameters). The gate learns a selective communication protocol from task loss alone, without a prescribed format. We demonstrate the mechanism across three tool backends. On arithmetic, coupling two 0.5B models with a calculator raises accuracy from 36\% to 96\%. On logic grid puzzles, coupling two 0.6B models with a Z3 solver achieves $1.7\times$ the unaugmented baseline on ZebraLogic. On mathematical reasoning, coupling with a Python sandbox enables the auxiliary to generate problem-specific code from hidden-state signals alone, without ever seeing the problem text.
Comments: 9 pages main text, 5 figures, 24 pages appendix
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.11167 [cs.CL]
(or arXiv:2605.11167v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.11167
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Cedric Flamant [view email] [v1] Mon, 11 May 2026 19:25:39 UTC (465 KB)
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