← 研踪 PaperTrailarXiv:2610.34357 · v2
6G Reasoning Agent Societies: Composition, Scaling, and Consistency-Time Laws
首次提交 2026-10-25 23:06
版本更新 2026-10-28 17:22
雷达观测 2026-10-30 10:22
摘要
Emerging 6G control planes would compose Large Language Model (LLM) agents that exchange interpreted conclusions, so hallucinations can propagate even over error-free transport. Recent architectures audit each message locally against the sender's observation mask, but isolated audits miss collective behavior. Modeling the agent society as a cellular sheaf, we establish three laws valid on arbitrary topologies. First, the composition law: network-wide divergence exceeds the sum of per-agent audit scores by exactly their hidden total correlation. This blind spot masks up to 23% of joint divergence and, because the declared signal cancels in the cognitive signal-to-noise ratio (SNR), overstates it by $1.14$ dB purely through topology; the resulting trust inflation preserves valid consensus states but slows convergence. Second, the scaling law: reconciliation decouples across metric counters, so the worst-connected dimension sets the pace. Consistency time grows for a society size $n$ as $Ω(n^{2/d})$ on polynomial-growth topologies of degree $d$, and paths breach a $1$ s near-real-time budget at $n^\ast=13$ agents, whereas expanders ($Θ(1)$) and radius-3 local audits ($O(1)$, $\approx300$ ms) stay bounded. Third, the consistency-time law: the timing error from omitted correlations is one-sided and capped by the worst-measured link. These laws, validated on simulated topologies and deployed LLM societies, give provable worst-case latency ceilings for near-real-time operation.
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v2 · 2026-10-28 17:22 · 最新观测版本
6G Reasoning Agent Societies: Composition, Scaling, and Consistency-Time Laws
Emerging 6G control planes would compose Large Language Model (LLM) agents that exchange interpreted conclusions, so hallucinations can propagate even over error-free transport. Recent architectures audit each message locally against the sender's observation mask, but isolated audits miss collective behavior. Modeling the agent society as a cellular sheaf, we establish three laws valid on arbitrary topologies. First, the composition law: network-wide divergence exceeds the sum of per-agent audit scores by exactly their hidden total correlation. This blind spot masks up to 23% of joint divergence and, because the declared signal cancels in the cognitive signal-to-noise ratio (SNR), overstates it by $1.14$ dB purely through topology; the resulting trust inflation preserves valid consensus states but slows convergence. Second, the scaling law: reconciliation decouples across metric counters, so the worst-connected dimension sets the pace. Consistency time grows for a society size $n$ as $Ω(n^{2/d})$ on polynomial-growth topologies of degree $d$, and paths breach a $1$ s near-real-time budget at $n^\ast=13$ agents, whereas expanders ($Θ(1)$) and radius-3 local audits ($O(1)$, $\approx300$ ms) stay bounded. Third, the consistency-time law: the timing error from omitted correlations is one-sided and capped by the worst-measured link. These laws, validated on simulated topologies and deployed LLM societies, give provable worst-case latency ceilings for near-real-time operation.
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