Latent communication in large language model (LLM)-based multi-agent systems (MAS) transmits continuous internal representations instead of text, but greater representational capacity does not establish that the receiver uses task-relevant information. End-task performance alone also cannot reveal whether an observed effect depends on message presence, content generated for the evaluated example, or information supplied by a separate agent.
arXiv:2608. 04893v1 Announce Type: cross Abstract: Multi-agent LLM systems relay key--value caches instead of text and credit their gains to exchanged ``latent thoughts''.
By Jiaming Cheng, Subhransu Das, Rajiv Ramnath
arXiv:2609.36855v1 Announce Type: new
Abstract: Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful inf...
By Yaxin Gong, Gangyi Zhang, Chongming Gao, Leyang Shen, Chenxiao Fan, Jiakai Wang, Dong Wang, Yang Liu, Wenjie Wang, Xiangnan He
The paper introduces Independent–Communicate–Revise (ICR), a framework that isolates communication effects in large language model multi‑agent systems by fixing initial reasoning and measuring how messages influence answer revision. ICR evaluates correction, preservation, and selectivity across four reasoning benchmarks, revealing that similar overall accuracy can mask divergent revision behaviors. The study shows that richer messages can both improve and harm outcomes, and that receiver policies can shift preservation and correction dynamics differently across tasks.
By Shixuan Li, Wei Yang, Peiyu Zhang, Anzhe Cheng, Heng Ping, Paul Bogdan
arXiv:2608. 11676v1 Announce Type: new Abstract: Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redundant reasoning patterns.
By Wooseong Yang, Wei-Chieh Huang, Weizhi Zhang, Yu Wang, Philip S. Yu, Junhyun Lee
The paper introduces Verifiable Latent Alignments (VLA), a framework that monitors and steers hidden communication channels between language‑model agents. VLA links private latent states to public actions via event identifiers, enabling causal analysis. Experiments on a multi‑agent auction benchmark show high detection accuracy and effective mitigation of collusion, even without training on attack examples.
By Ramneet Kaur, Pradyumna Chari, Ramesh Raskar, Jugad Singh, Sumit Kumar Jha, Anirban Roy
arXiv:2609.37017v1 Announce Type: new
Abstract: LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natu...
By Shinan Zhang, Tao Zhang, Qihui Zhu, Mengjie Zhang, Dong Jin, Yunpeng Hou, Shuangwu Chen, Xiaobin Tan, Quan Zheng, Jian Yang
arXiv:2607. 03598v1 Announce Type: cross Abstract: When a person shares something with a language model, the model often answers the surface of the message rather than what the sender was doing by sending it: share a finished project and it critiques the code; share a raw late-night line and it runs a wellness check.
By Alex Kwon
The study investigates how different message formats affect the fidelity of information as it passes through multiple LLM agent relays. Using a controlled testbed, the authors encode twelve atomic facts in five formats (free natural language, precision‑instructed NL, JSON, triples, key‑value) across six hops and evaluate recall against programmatic ground truth. Results show that strong relays maintain near‑lossless recall for all formats, while weaker relays exhibit significant format‑dependent recall loss, and that any injected error is faithfully propagated across all formats without causing collateral damage.
By Sicheng Zeng
arXiv:2604. 13349v2 Announce Type: replace Abstract: Communication in Large Language Model (LLM)-based multi-agent systems is moving beyond discrete tokens to preserve richer context.
By Yiping Li, Zhiyu An, Wan Du
arXiv:2607. 04419v3 Announce Type: replace Abstract: When evaluator-derived step rewards are pooled or compared across scoring channels, their sign is treated as transportable.
By Andrew Zhang, Chengzhan Li
arXiv:2607. 16133v1 Announce Type: cross Abstract: LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks.
By Wendi Yu, Lianhao Zhou, Xiangjue Dong, Sai Sudarshan Barath, Declan Staunton, Byung-Jun Yoon, Xiaoning Qian, James Caverlee, Shuiwang Ji