arXiv:2607. 22758v1 Announce Type: cross Abstract: The integration of iterative LLMs within multi-agent diagnostic frameworks requires a rigorous quantitative reevaluation of underlying communication topologies.
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Future 6G networks will rely on Large Language Model (LLM) agents to manage the Radio Access Network (RAN). However, current architectures assume inter-agent messages convey objective facts. A message...
The paper proposes that future 6G networks will use Large Language Model agents to manage the Radio Access Network, but current designs mistakenly treat inter‑agent messages as objective facts. It argues that messages are actually traces of the sender’s reasoning, carrying subjective conclusions that can propagate hallucinations and cause outages. By modeling these interactions as cognitive channels on a cellular sheaf, the authors derive five design principles—treating messages as evidence of hidden reasoning, defining trust as a continuous cognitive Signal‑to‑Noise Ratio, computing network consistency via the sheaf’s Laplacian, limiting peer‑modeling to two levels, and bounding credible capacity by goal alignment—and validate them with a signaling‑storm study on 1B‑parameter telecom language models.
arXiv:2608. 11252v1 Announce Type: new Abstract: Agentic AI systems routinely transport conclusions across biological, clinical and financial contexts, and the emerging safeguard is local verification: checking at each step that the entity is representable in the chosen tool, that parameters are compatible, and that outputs cohere with the plan.
arXiv:2608.21466v1 Announce Type: new Abstract: We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. For...
arXiv:2607. 17146v1 Announce Type: cross Abstract: We present a continuous geometric framework that models the discrete algebraic operations of the Transformer architecture as an integro-differential equation (IDE) on a semantic fiber bundle $\calE = \calM \times \R^d$.
arXiv:2606. 30512v1 Announce Type: cross Abstract: Why overparameterised deep networks generalise so remarkably well remains one of the most stubborn open questions in machine learning theory.