# J-Comm preprint reports agent performance gains with fewer generated text tokens

_Published Monday, October 26, 2026 at 10:32 AM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers report in a preprint that J-Comm, a framework for communication between language models, improves benchmark performance by 1.29-8.59 percentage points while reducing generated text tokens by 19.2-74.8% compared with TextMAS.

The framework lets a receiving model use intermediate reasoning signals from a sending model. It reconstructs those signals using the receiver's own dictionary and inserts them into intermediate processing layers, without requiring identical model widths or shared key-value caches.

In tests connecting Qwen3-8B to Qwen3-4B, J-Comm achieved the highest mean score across seven benchmarks. The reported token reduction concerns generated text; the interface also transfers intermediate reasoning signals.

## Sources

- [cs.CL updates on arXiv.org](https://arxiv.org/abs/2610.32871)

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Canonical: https://techandbusiness.org/newswire/8bevkHF70xRGUCKeuCcFPy
Published: 2026-10-26T14:32:48.992Z
Story chronology: 2026-10-26T04:00:00.000Z
Retrieved: 2026-10-26T16:58:43.449Z
Publisher: Tech & Business (techandbusiness.org)