mlsanityData to 14 Oct 2026

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

arXiv 2605.17076 ↗First posted 16 May 2026cs.LG cs.AI cs.DCcited by 8

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

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

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 6 of its 51 references that are in the map. First posted on arXiv 16 May 2026.

Builds on

6 of 51 references.

References · 6

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  3. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  4. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  5. 2024AgentScope: A Flexible yet Robust Multi-Agent Platformcited by 121
  6. 2023Verus: Verifying Rust Programs using Linear Ghost Types (extended version)cited by 53

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems
  2. 2026Position: Multi-Agent Systems Should Prioritize Concurrency Control
  3. 2026ContractBench: Can LLM Agents Preserve Observation Contracts?
  4. 2026Governed Shared Memory for Multi-Agent LLM Systems
  5. 2026SecureClaw: Clawing Back Control of LLM Agents
  6. 2026MemTX: Transactional Belief Commit for Stateful Agent Memory
  7. 2026When JSON Is Not Enough: Semantic Reliability of Schema-Constrained LLM Ordering Agents
  8. 2026Revocation Is Not Complete at Receipt: An Executable Outcome Contract for Distributed Agent-Tool Gateways