arXiv AI
LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing
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Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
Hugging Face Trending Papers
Jul 9
Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.
arXiv AI
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Benchmarking AI Agents for Hardware Design Automation via MCP Tool Calling
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arXiv AI
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When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry
arXiv:2608. 14680v1 Announce Type: new Abstract: Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails.
arXiv AI
Jul 13
Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting
arXiv:2511. 00651v2 Announce Type: replace Abstract: Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging.
arXiv Machine Learning
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From Detection to Action: Using LLM Agents for Fault-Tolerant Control
arXiv:2606. 28011v1 Announce Type: cross Abstract: We propose an agentic Large Language Model (LLM) framework for active Fault-Tolerant Control (FTC) that transforms fault detection outputs into constraint-aware recovery actions grounded in plant-specific knowledge.