Share the Symbol, Not the Vector: Training-Free Fact Communication Across Language Models
We introduce SYNC (Symbol-Indexed Neural Communication), a word-indexed interface that connects heterogeneous models without fine-tuning or a learned translator.
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Key points
- Multi-agent systems allow language models to solve tasks collaboratively by sharing complementary information.
- These systems typically communicate through text, while recent approaches also explore exchanging internal model states.
- However, sharing these states across architectures often requires learned translators or assumptions about model compatibility.
- These results show that shared word indices and receiver-local directions can support selective fact use across model families without additional training.
Sources (2)
- [1]Share the Symbol, Not the Vector: Training-Free Fact Communication Across Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:19 AM
We introduce SYNC (Symbol-Indexed Neural Communication), a word-indexed interface that connects heterogeneous models without fine-tuning or a learned translator.
Multi-agent systems allow language models to solve tasks collaboratively by sharing complementary information.
- [2]Share the Symbol, Not the Vector: Training-Free Fact Communication Across Language ModelsHugging Face Daily Papers · Oct 14, 07:19 AM · same content
Extractive summary: sentences quoted from the sources.
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