Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a fundamental constraint: language is a compressed and capacity-limited interface for conveying task information.
arXiv:2602. 05463v2 Announce Type: replace-cross Abstract: Modern AI systems achieve remarkable capabilities at the cost of substantial energy consumption.
By Koichi Takahashi, Yusuke Hayashi
The paper derives an information‑theoretic bound for a shared‑state cognitive architecture that uses an auxiliary variable to mediate context. It shows that the residual dependence of observable behavior on context, given the shared state, is bounded by the information carried by the auxiliary variable and its conditional entropy. A recognition‑memory example illustrates how to compute and compare this bound across different representational choices, providing a framework for analyzing context‑memory‑control trade‑offs in cognitive models and artificial agents.
By Song-Ju Kim
The paper introduces Embodied Semantic Communication (ESC), a new paradigm that redefines information transmission for autonomous agents by embedding multimodal perceptual states, hardware capabilities, and collaborative intents into unified, action‑oriented semantic representations. ESC enables heterogeneous agents to parse, align, and ground shared information directly into local motor control, addressing the limitations of traditional communication approaches that focus solely on bit delivery or single‑task optimization. The tutorial outlines ESC’s conceptual boundaries, system characteristics, and technical pathways, mapping relevant mathematical tools such as semantic information theory, world models, and multi‑agent decision theory, and concludes with a roadmap of open challenges like semantic reliability, dynamic interaction, and bandwidth‑adaptive transmission.
By Yizheng Huang, Wensheng Lin, Lixin Li, Qinghe Du, Wenchi Cheng, Zhu Han
arXiv:2609.37994v1 Announce Type: cross
Abstract: The classical information bottleneck (IB) measures the relevance of a representation $U$ of $X$ to a target $Y$ by $I(U;Y)$, which does not directly...
By Dier Tang, Guangyue Han
arXiv:2607. 17232v1 Announce Type: cross Abstract: Classical rate-distortion (RD) theory has long established the fundamental limits of lossy compression by quantifying the minimum number of bits required to represent a source under a prescribed distortion constraint.
By Photios A. Stavrou, Giuseppe Serra, Marios Kountouris