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.
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.
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arXiv AI
Jul 16
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
arXiv:2607. 13548v1 Announce Type: new Abstract: Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to both classical and LLM-based approaches.
arXiv AI
Sep 1
Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs
arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for e...
Hugging Face Trending Papers
Jul 15
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to both classical and LLM-based approaches. The OpenRCA dataset exemplifies these challenges: it is large-scale, multimodal, and lacks detailed domain knowledge, and yields consistently low accuracy across all existing methods.
arXiv AI
Jun 9
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery
arXiv:2606. 08728v1 Announce Type: new Abstract: Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequential AI frontiers.
arXiv AI
Sep 16
Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics
The paper introduces Symbolic Separation, a method that grounds deep learning agents in knowledge graphs to improve reliability in operational data analytics. By restricting agent actions to an ontology-constrained Virtual Knowledge Graph with deterministic pre-execution validation, the approach transforms complex queries into validated graph traversals rather than relying on LLM-inferred joins. In experiments on 49.9 TB of supercomputer telemetry, the Neurosymbolic Deep Analyst achieved an 86% task‑success rate, eliminated silent data‑integrity errors, and reduced token costs by 2.4× compared to a non‑symbolic baseline.
arXiv AI
Sep 3
Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis
The paper introduces a structured reasoning framework that leverages large language models (LLMs) for root cause analysis (RCA) in telecom networks. It organizes heterogeneous network telemetry into canonical contexts, enforces decision‑path reasoning, and produces evidence‑grounded explanations to improve fault identification. Experiments on two 5G RCA datasets, TeleLogs and TelecomTS, show that this approach consistently outperforms baseline techniques in diagnostic accuracy and decision consistency.
arXiv AI
Jun 2
PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis
arXiv:2606. 00582v1 Announce Type: new Abstract: Network faults propagate layer by layer along topology and protocol dependencies, yet operations systems typically observe only symptomatic alerts at the tail end of propagation chains, where distinct root-cause faults may produce highly similar end-point symptoms.
arXiv AI
Jul 7
Formal Disco: Scalable Open-Ended Generation of Formally Verified Programs
arXiv:2607. 04631v1 Announce Type: new Abstract: The cost of producing code is rapidly diminishing with increasingly capable AI agents, while quality assurance of generated programs has not kept pace.
Hugging Face Trending Papers
Sep 2
Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis
The paper discusses the challenges of root cause analysis (RCA) in 5G and 6G telecom networks, where complex cross-layer dependencies make diagnosis difficult. It reviews the progression from rule‑based and machine‑learning RCA methods to emerging large language model (LLM) approaches, highlighting issues such as hallucination and unstable reasoning when using vanilla LLMs. The authors propose a structured reasoning framework that organizes network telemetry into canonical contexts, enforces decision‑path reasoning, and generates evidence‑grounded explanations, showing improved diagnostic accuracy on two 5G RCA datasets.
arXiv AI
Aug 28
ProofEvolve: Neuro-Symbolic Evolution for Formal Automated Theorem Proving
ProofEvolve is a neuro‑symbolic framework that evolves formally verified symbolic proof structures alongside neural models to expand the knowledge boundary in automated theorem proving. The neural component proposes variation operators such as decompositions, repairs, and schema recombinations, while the Lean kernel verifies every proof transition, ensuring formal soundness. Across three competition‑level Lean benchmarks, ProofEvolve achieves the highest average solve rate among evaluated proof systems.
arXiv AI
Jun 30
Self-Supervised Theorem Discovery in a Formal Axiomatic System
arXiv:2606. 28747v1 Announce Type: new Abstract: Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical reasoning.