Latent Communication Between Language Model Agents: Channels, Alignment, and the Limits of Text
3.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.14103.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryResearch paper testing whether LLM agents communicating via latent channels retain more information than text channels. Finds SAE-sparse channels achieve 99.4% probe accuracy at 28x compression over dense latent, and that text serialization destroys 88% of SAE features, but these lost features encode surface form, not task-relevant semantics.
Why it mattersQuantifies the information cost of text-based inter-agent communication and reports a negative result: latent channels preserve internal features but offer no task-level gain over text in current evaluations.
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