Skip to content

TSAutoBox: A Localized Multi-Agent System for Industrial Time-Series Task Automation

Unknown authors
· 1 citation · 46 references

TL;DR

Results suggest that localized multi-agent automation can make time-series modeling easier, and significantly reduces measured per-run cost, while staying within a comparable energy consumption.

View source

Similar papers

Book Open access Aug 2026

Building Reliable Industrial Agents with MCP: A Hands-on AssetOpsBench Tutorial for AI-Driven Operations

This tutorial provides the foundations for building auditable, production-grade agents for Industry 4.0 through end to end MCP grounded agent pipeline that connects specialized MCP servers to high velocity industrial data.

Dhaval Patel, Chathurangi Shyalika, Shuxin Lin et al. · 0 citations
Book Open access Aug 2026

StarVerus: LLM-Powered Multi-Agent Collaboration for Industrial Rust Code Verification Automation

A multi-agent collaborative framework, StarVerus, to automate the verification of industrial Rust code and introduces a planner-repairer-actor-rewriter multi-agent paradigm to further enhance the proof repair capabilities.

Chao Jiang, Ding Wang, Du-Gang Liu et al. · 0 citations
Preprint Aug 2026

CyberLLM: A Multi-Agent LLM Framework for Autonomous Detection and Guarded Response in Automotive Cybersecurity

CyberLLM is presented, a multi-agent, LLM-orchestrated framework that autonomously detects vulnerabilities and executes remediations under a formal, runtime safety guard, and indicates that LLM agents can perform useful autonomous cyber-defense when wrapped in a deterministic, auditable safety envelope.

Nenad Petrovic, Oussama Jeddou, Feres Ben Fraj et al. · 0 citations
Jul 2026

AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents

This work introduces AgentS4D, a sandboxed benchmark for lifecycle-wide runtime safety evaluation and evaluates all 20 combinations of four harnesses and five LLM backends, finding that the observed safety of an agent system varies with both its harness-LLM pairing and how risk is introduced.

Jiajun Zhou, Zhaoxuan Ke, Jihang Ye et al. · 1 citation

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.