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.

Research 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.

Quantifies 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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