Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems

• URL: http://arxiv.org/abs/2606.17182v1
• Date: Mon, 15 Jun 2026 18:19:34 GMT
• Title: Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems
• Abstract summary: Multi-agent LLM systems state through memory stores, vector indices, and tool registries.<n>We model such sharing as long-running read-generate-write operations under deterministic-generation semantics.<n>We formalize four anomalies in TLA+: stale-generation, phantom-tool, causal-cascade, and tool-effect reordering.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent LLM systems share state through memory stores, vector indices, and tool registries. We model such sharing as long-running read-generate-write operations under deterministic-generation semantics -- the regime durable-execution engines enforce by deterministic replay -- and formalize four concurrency anomalies in TLA+: stale-generation, phantom-tool, causal-cascade, and tool-effect reordering, structural analogues of classical isolation anomalies, each with a TLC counter-example. The exclusion lattice over these anomalies is trivial; the contribution is the mechanically verified realizability and strict separation of one maximal chain within it, $L_0 \subsetneq \cdots \subsetneq L_4$, to our knowledge the first machine-checked consistency hierarchy for such runtimes. A development of 274 Verus obligations (zero assume, zero admit; trust base: two structural axioms and a mutex correspondence) proves the detectors sound and complete against the specifications and each runtime its avoidance set. Three deployed Rust runtimes realize L0-L1 (pessimistic locking, serializable snapshot isolation, default-SI), each verified against stale-generation and refined to its state machine; L2-L4 are exec-mode-verified with dependency-free prevention twins (A3, A6, A2: 0/1000 versus 1000/1000), and L2 is run live across three model families (A3 prevented in all 120 retracted sessions). We reproduce a silent lost update in ByteDance's deer-flow, formalizing its fix as a verified $L_0 \to L_1$ refinement, and exhibit tool-effect reordering in LangGraph's ToolNode on unmodified output, removed by an L3 commit-order sequencer. The verified detector, refinements, and realizability artifacts are the contribution; the phenomena and lattice are classical.

Related papers

• Runtime Observability for Heterogeneous Attention Memory [20.158719055939383]
  We give a runtime observability contract that covers all four memory classes with three operators.<n>We compose the per-stage stack into an executable request-level risk ledger.
  arXiv  Detail & Related papers  (2026-08-06T10:41:50Z)
• PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback [0.0]
  PRIMA operates as coordinated multi-agent research systems over multi-hour runs.<n>Main contributions are three operational patterns for surviving failure modes.<n>A Graph Isomorphism case study grounds the architectural claims in a generated artifact.
  arXiv  Detail & Related papers  (2026-05-23T23:27:46Z)
• CasualSynth: Generating Structurally Sound Synthetic Data [44.80087038178069]
  Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the target domain.<n>We introduce Causal Synth, a framework that decouples causal structure generation from semantic realization, yielding synthetic data that is both causally valid and linguistically rich.
  arXiv  Detail & Related papers  (2026-05-17T16:21:01Z)
• Logic-Regularized Verifier Elicits Reasoning from LLMs [63.65875399266337]
  We propose LOVER, an unsupervised verifier regularized by logical rules.<n>Lover treats theverifier as a binary latent variable, utilizinginternal activations and enforcing three logical constraints.<n>Lover significantly outperforms unsupervised baselines.
  arXiv  Detail & Related papers  (2026-05-07T09:03:49Z)
• Pairwise matrices for sparse autoencoders: single-feature inspection mislabels causal axes [2.741152471987327]
  Standard sparse-autoencoder protocol labels each feature from its top-activating contexts and validates by single-feature steering.<n>We propose the pairwise matrix protocol, co-varying steering coefficient with joint condition, and report three findings the standard one-corner protocol misses on Qwen3-1.7B-Instruct, replicated on Gemma-2-2B-it.<n>All three findings reproduce on Gemma with model-specific damage signatures; the matched-geometry control is CI-separated by 10x.
  arXiv  Detail & Related papers  (2026-05-04T21:11:21Z)
• OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detection [0.0]
  We present a verifiable synthetic benchmark in which a deterministic simulation engine maintains ground truth and language models generate only surface prose.<n>The corpus spans 51 simulated days, 2,904 telemetry records at a 96.4% noise rate, and four detection scenarios designed to defeat single-surface and single-day triage strategies.
  arXiv  Detail & Related papers  (2026-03-23T19:03:53Z)
• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs [50.075587392477935]
  We conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems.<n>Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack.
  arXiv  Detail & Related papers  (2026-01-20T06:42:56Z)
• The Trojan in the Vocabulary: Stealthy Sabotage of LLM Composition [31.827344197678126]
  Tokenizer transplant introduces a supply-chain vulnerability.<n>By exploiting the geometry of coefficient reuse, our attack creates an asymmetric realizability gap.<n> Empirically, the attack is training-free and achieves spectral mimicry to evade outlier detection.
  arXiv  Detail & Related papers  (2025-12-31T19:00:03Z)
• From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models [81.43473418572567]
  Click-Through Rate (CTR) prediction is a core task in recommendation systems.<n>We propose a novel generative framework to address embedding dimensional collapse and information redundancy.<n>We show that SFG consistently mitigates embedding collapse and reduces information redundancy, while yielding substantial performance gains.
  arXiv  Detail & Related papers  (2025-12-16T03:17:18Z)
• Higher-order Linear Attention [59.92962330635185]
  quadratic cost of scaled dot-product attention is a central obstacle to scaling autoregressive language models to long contexts.<n>We introduce Higher-order Linear Attention (HLA), a causal, streaming mechanism that realizes higher interactions via compact prefix sufficient statistics.
  arXiv  Detail & Related papers  (2025-10-31T07:54:37Z)
• Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning Models [86.88657425848547]
  Large reasoning models (LRMs) already possess a latent capacity for long chain-of-thought reasoning.<n>We explicitly align models with three meta-abilities: deduction, induction, and abduction, using automatically generated, self-verifiable tasks.<n>Our three stage-pipeline individual alignment, parameter-space merging, and domain-specific reinforcement learning, boosts performance by over 10% relative to instruction-tuned baselines.
  arXiv  Detail & Related papers  (2025-05-15T17:58:33Z)

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.