English

S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination

Machine Learning 2026-05-25 v2 Artificial Intelligence Distributed, Parallel, and Cluster Computing Multiagent Systems

Abstract

We address concurrency control for LLM agents sharing mutable state over HTTP, where agents cannot be modified to declare read sets. S-Bus is an HTTP middleware whose central mechanism, a server-side DeliveryLog, reconstructs each agent's read set at commit time from observed HTTP GET traffic. The consistency property it provides -- Observable-Read Isolation (ORI), a partial causal consistency over the HTTP-observable read projection -- prevents Structural Race Conditions in dedicated-shard topologies. Three contributions. (C1) DeliveryLog mechanism with three-tier mechanised evidence: TLAPS proves ReadSetSoundness and ORICommitSafety (modulo one typing axiom); exhaustive TLC at N=3 explores 20,763,484 states with zero violations; Dafny discharges 9 inductive lemmas. (C2) Empirical safety parity against PostgreSQL 17 SERIALIZABLE and Redis 7 WATCH/MULTI: zero Type-I corruptions across 884,110 commit attempts (427,308 under active contention). (C3) ORI is semantically neutral in dedicated-shard workloads but harmful in single-shard collaborative writing because preservation propagates concurrent contradictions. v2 update: the PH-3 LLM judge is now independently validated against a human annotator (Zahid Hussain, Mindgigs Peshawar) on 400 (step, shard) pairs at strict kappa=0.93 (n=93, 96.8% raw agreement). Inter-LLM-judge agreement is kappa=0.46 (boundary variance). Agent self-reports over-claim shard usage by 32% (LLM judge) to 49% (human annotator). The SJ-v4 semantic-quality rubric remains single-judge LLM-only. Source code, formal proofs, harness, annotation data: https://github.com/sajjadanwar0/sbus

Keywords

Cite

@article{arxiv.2605.17076,
  title  = {S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination},
  author = {Sajjad Khan},
  journal= {arXiv preprint arXiv:2605.17076},
  year   = {2026}
}

Comments

v2: LLM judge validated against human annotator (Zahid Hussain, Mindgigs Peshawar) on PH-3 at strict kappa=0.93 (n=93, 96.8% agreement); over-claim refined to 32% (LLM) / 49% (human). Adds Exp.PG-Comparison Rust-Native and Workload-B chi2=1094.98. 24 pages, 23 tables. Annotation data attached as arXiv ancillary files