arXiv:2606.03280v1 Announce Type: new Abstract: Recent work shows that language models can transmit behavioural traits through hidden signals in generated data during training. We ask whether a more direct and stricter channel is also viable: can one language model communicate useful intermediate reasoning state to another at inference time by translating and injecting hidden activations, rather than by passing natural-language text? We test this question in a controlled Pythia-160M to Pythia-410M multi-hop reasoning setting. A linear translation layer learns a strong normalized-space map betw
This research emerges as models become increasingly complex, making the efficiency and fidelity of inter-model communication a critical concern for performance and scalability.
The ability to directly transfer intermediate reasoning states between language models could significantly enhance the capabilities of AI agents and multi-model systems, accelerating AI development.
Current methods of inter-model communication primarily rely on natural-language text; direct activation transfer would open a new, potentially more efficient, 'brain-to-brain' communication channel for AI.
- · AI developers
- · Companies building multi-agent AI systems
- · Research institutions focused on AI architecture
It directly tests a novel mechanism for inter-model intelligence transfer beyond natural language.
If successful, this could lead to more efficient and sophisticated AI reasoning pipelines composed of specialized models.
This could accelerate the development of advanced AI agents by enabling complex cognitive architectures and real-time knowledge sharing.
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Read at arXiv cs.AI