arXiv AI
Jul 28
Let AI Agents Translate Networks, Not Reason About Them
arXiv:2607. 22947v1 Announce Type: new Abstract: A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change.
Self-evolving network verifiers
arXiv:2608. 11340v1 Announce Type: cross Abstract: Symbolic network verifiers can reason about correctness across vast spaces of routing inputs and failures, but only for the protocols and features an expert has encoded by hand.
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arXiv AI
Aug 25
NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configuration
arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are po...
arXiv AI
Sep 15
Safety Signals to Verify NetOps Agents with Action-Level Granularity
arXiv:2609.14422v1 Announce Type: new Abstract: Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. Whi...
arXiv AI
Sep 11
Can AI Agents Deliver Verifiable Network-Wide Outcomes Across Authority Boundaries?
The paper introduces EvidenceNet, a runtime assurance layer designed to verify that coordinated AI agent operations achieve an operator’s intended network-wide outcomes across multiple administrative domains. EvidenceNet collects post-change observations from the required authority scopes, checks their freshness and validity, and uses a verifier agent to assess observation content. Experiments on live routing networks demonstrate that this approach can detect successful outcomes that configuration-action logs alone miss, and it rejects completions when observations are sourced incorrectly, substituted, or stale.
arXiv AI
Jun 2
AXIOM: A Trust-First Neuro-Symbolic Execution Architecture for Verifiable Mathematical Reasoning
arXiv:2606. 00671v1 Announce Type: new Abstract: We present AXIOM, a trust-first neuro-symbolic execution architecture for natural-language mathematical reasoning.
arXiv AI
Jun 2
Attested Tool-Server Admission: A Security Extension to the Model Context Protocol
arXiv:2605. 24248v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a host reads a server's self-declared tool list and dispatches calls, with no notion of which servers it may use, at what sensitivity, or which of a server's tools are in bounds.
arXiv AI
Jul 31
Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research
arXiv:2607. 26352v1 Announce Type: cross Abstract: Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it.
arXiv AI
Sep 11
Can AI Agents Detect and Repair Artifact Drift in Network Experiments?
The paper introduces NetArtifactBench, a benchmark designed to evaluate whether AI agents can detect and repair inconsistencies in network experiment records while preserving supported claims. It tests 23 agent configurations on 52 instances with injected inconsistencies, finding an average pass rate of 65.3 % but no runtime exceeding 30 % for complex repairs that require recovering implicit relations and propagating changes across artifacts. The results highlight a clear distinction between local corrections and full record-level repair, leading the authors to argue that artifact integrity should be a primary design and evaluation criterion for AI agents in network systems.
arXiv AI
Sep 16
Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems
The article surveys how large language models (LLMs) can be incorporated into networked control systems, cyber‑physical systems, and multi‑agent networks without violating stability and safety guarantees. It proposes treating the LLM as a slow supervisor that sets high‑level goals, while a fast, certified inner loop preserves physical stability. The survey maps LLM characteristics—such as inference latency, API failures, tokenization, and hallucinations—to classical control challenges and highlights the growing gap between model capability and formal safety assurances, calling for future research on stability proofs.
arXiv AI
Jun 30
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
arXiv:2606. 30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows.
arXiv Computation and Language
Sep 15
RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines
arXiv:2609.13389v1 Announce Type: new Abstract: Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-gener...
arXiv AI
Aug 10
Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On
arXiv:2605. 19035v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution.