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.
AutoSND: From Execution Evidence to Structural Policies for Automated Network Dismantling Heuristic Discovery
arXiv:2608. 03653v1 Announce Type: new Abstract: Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness and computational efficiency, and are usually designed manually by researchers.
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arXiv AI
Jul 15
AutoTrace: From Patches to Triggers via Agentic Interprocedural Exploration
arXiv:2607. 12058v1 Announce Type: cross Abstract: Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe operation.
arXiv AI
Jul 23
An Automated Framework for Extracting Reachable Attack Chains from Cyber Threat Intelligence Reports
arXiv:2607. 19742v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports richly describe real-world attack processes, but their unstructured narratives cannot be directly used for automated attack-path reasoning.
arXiv AI
Aug 18
ReForge: Keeping ABR Algorithms Never Finished with Verified Large Language Model Edits
arXiv:2608. 15138v1 Announce Type: new Abstract: Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching or beating hand-built designs.
arXiv AI
6d ago
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
Aug 13
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.
arXiv AI
Jul 28
Invariant Discovery for Networked Systems
arXiv:2607. 22944v1 Announce Type: cross Abstract: Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation, yet writing them by hand demands rare expertise in both formal logic and networking.
arXiv AI
Jul 28
SpecAHD: Localize to Specialize for Automated Heuristic Design in Large-Scale Routing Problems
arXiv:2607. 23676v1 Announce Type: new Abstract: LLM-based automated heuristic design (AHD) typically scores executable programs on complete instances or within fixed solver components.
arXiv AI
Jun 6
From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability
arXiv:2606. 05252v1 Announce Type: cross Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder.
arXiv AI
Aug 19
ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection
ComNetX is a solver‑agnostic hierarchical adaptation framework that localizes dynamic community detection updates by expanding, closing, and contracting affected communities. It preserves the context needed by high‑quality solvers while restricting computation to the changed graph regions. Experiments on six real networks and synthetic streams show that ComNetX maintains modularity close to full recomputation while achieving up to a 41.9× speedup on large graphs.
arXiv AI
Jun 4
Bilevel Autoresearch: Meta-Autoresearching Itself
arXiv:2603. 23420v2 Announce Type: replace Abstract: If autoresearch is itself a form of research, then autoresearch can be applied to research itself.
arXiv AI
Jul 21
Structure-Induced Information for Rerooting Levin Tree Search
arXiv:2605. 30664v2 Announce Type: replace Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on explicit subgoal generation that can incur substantial overhead and hinders scalability.