arXiv:2606. 28839v1 Announce Type: new Abstract: We introduce the Contagion Tensor, a measurement framework for quantifying how large language model (LLM) output distributions couple across modalities, agents, and time steps.
By Zewen Liu
The paper introduces a new way to detect drift in stateful language‑model pipelines by treating the sequence of prompt, response, and next prompt as a single unit of analysis. It defines two metrics—communication closure and normalized conditional action contribution—to quantify how well a response aligns with the subsequent prompt and how much it resolves the next reply. Experiments on over 2,200 dialogues show that swapping a response drastically reduces measured contribution, indicating that drift can be detected without labels or predefined rules.
By Wael Hafez, Amir Nazeri, Chenan Wei
arXiv:2606. 23195v2 Announce Type: replace Abstract: Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence.
By Zewen Liu
arXiv:2606. 16682v3 Announce Type: replace Abstract: When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge.
By Zewen Liu
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. 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