arXiv:2606. 12721v1 Announce Type: new Abstract: Inferring others' beliefs requires more than reading surface signals; it requires tracking who told them what, in what order, and how credibly.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606.12721v3 Announce Type: replace Abstract: Inferring another person's beliefs requires reconstructing their information access history: what they encountered, in what order, from whom, and w...
arXiv:2608. 02704v1 Announce Type: new Abstract: Predictive processing theories portray the brain as a hierarchical prediction engine that minimizes prediction error, yet they lack operational definitions for the structure of a "prediction," the standardized response to a prediction error, and the mechanism that maintains consistency across successive updates.
arXiv:2605. 24528v2 Announce Type: replace Abstract: Real world decision-making requires constructing mental models under uncertainty over evidence, over the underlying causal rules, and over the state of the world itself.
arXiv:2608. 09638v1 Announce Type: new Abstract: Theory of Mind (ToM) is essential for agent interactions, yet existing evaluations either rely on static scenarios that oversimplify mental-state reasoning or interactive settings that provide limited diagnostic insight.
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.