The paper investigates whether a large language model (LLM) possesses a notion of self by distinguishing between two biological analogues: a self‑index that separates the model’s own material from external material, and self‑reference that allows the model to recognize its own content. Using Qwen3.8‑27B, the authors demonstrate that the model’s internal representations consistently differentiate its own messages across all layers, that the model claims ownership of its messages with a measurable correlation to the self‑index, and that manipulating internal states can reverse or diminish this self‑attribution. The study further shows that the model’s self‑reference ability is enhanced when it engages in a “thinking mode” and that the self‑index influences the model’s performance on arithmetic tasks.
By Yuchen Li, Xintong Li, Zongqi Fan, Ken-tye Yong, Nguyen H. Tran
arXiv:2610.19696v1 Announce Type: new
Abstract: Multi-agent systems allow language models to solve tasks collaboratively by sharing complementary information. These systems typically communicate thro...
By Xin Li, Mengbing Liu, Hao Jiang, Xin Gao, Annan Wang, Xingwei Qu, Yichi Zhang, Chau Yuen
arXiv:2609.24146v1 Announce Type: new
Abstract: Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one anot...
By Cong Li, Cheng Chen, Thomas Fung, Alex Rossi, Yi Li
The paper investigates how to efficiently share internal states between agents built from frozen language models. It proposes a shared pool approach where each agent publishes its internal state once, allowing peers to read it via a single mechanism, thereby avoiding the need for a dedicated transfer bridge for every agent pair. The study finds that augmenting the key‑value cache and replacing activations—rather than swapping them—yields better accuracy with minimal training, and that a single adapter can generalize across multiple publishers when their internal states are similar to the receiver’s.
By Adam Deng, Shicheng Liu, Yada Zhu
arXiv:2610.14958v1 Announce Type: new
Abstract: Activation steering has recently been used as a memory for skills: past experiences of an agent are stored as steering vectors and retrieved later. Fac...
By Vedant Jumle
arXiv:2610.20179v1 Announce Type: cross
Abstract: Probes on activation vectors are widely used to detect misbehavior in language models, but their success relies on the assumption that models cannot...
By Davide Baldelli, Robyn Kozierok, Mirko Bronzi, Damiano Fornasiere