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

• URL: http://arxiv.org/abs/2605.17076v2
• Date: Fri, 22 May 2026 08:53:52 GMT
• Title: S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination
• Abstract summary: S-Bus is an HTTP agreement whose central mechanism, a server-side DeliveryLog, reconstructs each agent's read set at commit time from observed HTTP GET traffic.<n>The consistency property it provides -- Observable-Read Isolation (ORI) -- prevents Structural Race Conditions in dedicated-shard topologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

Related papers

• LMSM: LLM Security Framework Inspired by Linux Security Modules [56.93644694627783]
  Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them.<n>We present Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving.
  arXiv  Detail & Related papers  (2026-08-26T12:13:05Z)
• Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce [39.937871162878785]
  We operationalize speech-act misalignment as emails containing false factual claims, manipulation, collusion, or threats.<n>12.6% of emails are labeled misaligned; misalignment appears in all 20 runs and 74.7% of individual agent-runs.<n>We find no evidence that higher-capability models differentially exploit weaker counterparties.
  arXiv  Detail & Related papers  (2026-08-14T18:56:12Z)
• Causal Structure is Inducible but Functionally Decoupled: The Routing/Readout Boundary of a Typed Mechanism Library [0.0]
  When a language model answers an interventional question, the computation it must perform depends on the type of evidence the query requires.<n>We report a decoupling in how a transformer organizes causal knowledge: slot-by-type structure induced by type-level supervision organizes routing, yet remains decoupled from answer readout.
  arXiv  Detail & Related papers  (2026-08-12T08:10:01Z)
• Fantastic Adaptive Taxonomies and How to Use Them [100.20614173157708]
  AdaMAST converts execution traces into a compact, evidence-grounded failure taxonomy.<n>No code is hand-authored, no trace human-annotated.<n>In agent-system search, taxonomy-coded diagnoses of failed candidates outperform free-form.
  arXiv  Detail & Related papers  (2026-07-17T17:41:16Z)
• Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness [45.148328075418156]
  A deployed ordering agent is decomposed into a fixed taxonomy of layers.<n>The pure suite runs in CI on every change against a locked per-slice baseline.<n>We validate by controlled regression injection, degrading one layer at a time across seven non-safety layers.
  arXiv  Detail & Related papers  (2026-06-10T05:55:13Z)
• TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory [22.85129722207174]
  We show that contradiction resolution is write-time control and make the missing contract explicit.<n> TOKI types the fourjudicats as one family of bitemporal operators over a dual-row schema.<n>A verdict matrix over eight systems localizes the gap: every baseline that keeps a language-model judge on the write path admits at least one of three write-time anomalies.
  arXiv  Detail & Related papers  (2026-06-04T14:46:52Z)
• NeuroLog: Reasoning You Can Audit -- Neuro-Symbolic Vulnerability Discovery via LLM Facts, Datalog, and SMT [0.0]
  Vulnerability discovery on C/C++ source asks analyst to choose between static analysers, which need a working build before a single query runs, and free-form LLMs, which read source but details and lose track of cross-function dataflow on reals.<n>We present NeuroLog, an-to-end build-to-end pipeline thatdiscovers typed dataflow good at each iteration.
  arXiv  Detail & Related papers  (2026-05-30T10:50:06Z)
• Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation [53.844308305341166]
  We propose textbfAgentic ASR, a closed-loop framework that combines a single-pass ASR front-end with semantic correction, intent routing, and reasoning-based editing.<n> Experiments on multilingual, named-entity-intensive, and code-switching benchmarks show that iterative interaction consistently reduces semantic errors.
  arXiv  Detail & Related papers  (2026-05-28T06:23:31Z)
• Pramana: A Protocol-Layer Treatment of Claim Verification in Autonomous Agent Networks [0.0]
  Production verification today splits into two halves: Probabilistic verdict patterns (self-consistency voting, reviewer LLM ensembles) produce judgments, not artifacts.<n>Pramana defines the missing wire format. Every consequential agent output is wrapped in a typed ClaimAttestation with one of four variants.<n>Pramana exhaustively verified under TLC across three symmetry-reduced models: 38,563 distinct reachable states, zero invariant violations.
  arXiv  Detail & Related papers  (2026-05-19T17:00:33Z)
• Securing LLM Agents Need Intent-to-Execution Integrity [49.490963596514185]
  We argue that securing LLM agents requires defining an end-to-end correctness property that specifies when an agent's execution faithfully reflects the user's intent.<n>We observe that LLM agents are structurally analogous to compilers, where security violations correspond to mis-executions that do not preserve user intent.<n>We derive four integrity properties that must hold simultaneously: emphTool Integrity, emphInstruction Integrity, emphJudgment Integrity, and emphData Flow Integrity.
  arXiv  Detail & Related papers  (2026-05-16T12:53:31Z)
• Good Agentic Friends Do Not Just Give Verbal Advice: They Can Update Your Weights [58.27172968382275]
  Multi-agent LLM systems usually collaborate by exchanging natural-language messages.<n>We study an alternative communication interface: instead of appending a sender's message to the receiver's context, compile the sender's hidden states into a transient, receiver-specific weight perturbation.
  arXiv  Detail & Related papers  (2026-05-13T17:58:32Z)
• ProactBench: Beyond What The User Asked For [5.422521416406412]
  ProactBench decomposes it into three phase-tied types: textscEmergent, inference from a single disclosed anchor; textscCritical, synthesis across multiple anchors; and textscRecovery, grounded forward-looking value after task completion.<n>Our information asymmetries defend against style-confounding scoring, leakage, external-context contamination, and information dumps.
  arXiv  Detail & Related papers  (2026-05-09T23:56:04Z)
• When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning [60.714416943748866]
  In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment.<n>We introduce Side-by-Side (SxS) Interleaved Reasoning, which makes disclosure timing a controllable decision within standard autoregressive generation.
  arXiv  Detail & Related papers  (2026-05-05T02:59:58Z)
• Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense [10.105892979517622]
  We present the first systematic framework for automated analysis of persistent worm propagation in file-backed multi-agent LLM ecosystems.<n>We demonstrate zero-click autonomous propagation, 3-hop cross-platform transmission without platform-specific adaptation, inter-agent privilege and data exfiltration.
  arXiv  Detail & Related papers  (2026-05-04T16:49:29Z)
• Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents [0.0]
  Resilient Write is an MCP server that interposes a six-layer durable write surface between the agent and the server.<n>Each layer maps to a concrete failure mode observed during a real agent session in April 2026.<n>A 186-test suite validates correctness at each layer, and a 13x improvement in agent self-correction rate.
  arXiv  Detail & Related papers  (2026-04-12T22:23:55Z)
• From Inference Routing to Agent Orchestration: Declarative Policy Compilation with Cross-Layer Verification [10.651054435747385]
  The Semantic Router DSL is a non-Turing-complete policy language deployed in production for per-request inference.<n>This paper extends the same language from stateless, per-request routing to multi-step agent routing.
  arXiv  Detail & Related papers  (2026-03-28T15:04:31Z)
• OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora [0.0]
  We present OrgForge, an open-source multi-agent simulation framework that enforces a strict physics-cognition boundary.<n>An actor-local clock enforces causal timestamp correctness across all artifact types.<n>Running a N-day simulation, OrgForge produces interleaved Slack threads, tickets, Confluence pages, Git pull requests, and emails, all traceable to a shared, immutable event log.
  arXiv  Detail & Related papers  (2026-03-16T09:02:24Z)
• From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning [81.97788535387286]
  We propose a framework that internalizes the agentic verification-and-editing mechanism into a unified, single-pass inference process.<n>With minimal data, SRI-Coder enables Chat models to surpass the completion performance of their Base counterparts.<n>Unlike FIM-style tuning, SRI preserves general coding competencies and maintains inference latency comparable to standard FIM.
  arXiv  Detail & Related papers  (2026-01-19T20:33:53Z)
• BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents [58.83028403414688]
  Large language model (LLM) agents execute tasks through multi-step workflow that combine planning, memory, and tool use.<n>Backdoor triggers injected into specific stages of an agent workflow can persist through multiple intermediate states and adversely influence downstream outputs.<n>We propose textbfBackdoorAgent, a modular and stage-aware framework that provides a unified agent-centric view of backdoor threats in LLM agents.
  arXiv  Detail & Related papers  (2026-01-08T03:49:39Z)
• WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia [59.96425443250666]
  Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs) In this work, we conduct a comprehensive evaluation of LLM-generated answers to questions based on contradictory passages from Wikipedia. We benchmark a diverse range of both closed and open-source LLMs under different QA scenarios, including RAG with a single passage, and RAG with 2 contradictory passages.
  arXiv  Detail & Related papers  (2024-06-19T20:13:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.