BestHub
Sign in
Tagged articles
11 articles
Page 1 of 1
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
May 26, 2026 · Artificial Intelligence

Qian Xuesen’s 1954 Engineering Control Theory: The Unexpected Blueprint for Large‑Model Harnessing and Ontology

The article links Qian Xuesen’s 1954 work on engineering control theory to today’s challenges in large‑model training, arguing that a three‑step framework—ontology (defining what to control), control theory (designing how to control), and harness (accurate measurement)—is essential for reliable AI systems across domains such as medicine, law, and multimodal perception.

AI EngineeringLarge Language ModelsOntology
0 likes · 9 min read
Qian Xuesen’s 1954 Engineering Control Theory: The Unexpected Blueprint for Large‑Model Harnessing and Ontology
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
May 25, 2026 · Artificial Intelligence

Applying Qian Xuesen’s Engineering Cybernetics to Suppress Hallucinations in Large Language Models

The paper formulates LLM hallucination as systemic noise, builds a forward‑feedback‑adaptive control loop using Prompt engineering, Retrieval‑Augmented Generation and a hallucination detector, proves global asymptotic stability via Lyapunov theory, designs an LQR optimal controller and an MRAC adaptive scheme, and demonstrates up to 5 dB SNR improvement and sub‑5% hallucination rates on standard benchmarks.

Adaptive ControlEngineering CyberneticsHallucination Mitigation
0 likes · 24 min read
Applying Qian Xuesen’s Engineering Cybernetics to Suppress Hallucinations in Large Language Models
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 27, 2026 · Artificial Intelligence

From Parameter Tuning to Control: CFG‑Ctrl Boosts Stability and Precision in Text‑to‑Image Generation

The paper introduces CFG‑Ctrl, a control‑theoretic redesign of classifier‑free diffusion guidance that treats the generation process as a dynamic system, achieving more stable and accurate text‑to‑image results across multiple model scales and evaluation metrics.

CFG-CtrlClassifier-Free Guidancecontrol theory
0 likes · 15 min read
From Parameter Tuning to Control: CFG‑Ctrl Boosts Stability and Precision in Text‑to‑Image Generation
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 17, 2026 · Industry Insights

Can AI Agents Keep Software Engineering Under Control? A Deep Dive into Harness Engineering

The article analyzes how AI agents can write code yet remain uncontrollable, examines the shortcomings of prompt engineering and simple loops, and proposes Harness Engineering—a structured, constraint‑driven, feedback‑rich environment that turns software development into a stable, closed‑loop control system.

AIAgentAutomation
0 likes · 11 min read
Can AI Agents Keep Software Engineering Under Control? A Deep Dive into Harness Engineering
Tencent Cloud Developer
Tencent Cloud Developer
Apr 17, 2026 · Artificial Intelligence

How Harness Engineering Turns AI Coding into Real-World Cybernetics

This article analyzes OpenAI's Harness Engineering concept, connects it to classic cybernetics principles of information, control, and feedback, and explains how AI‑driven code generation requires structured rules, repository‑based constraints, and observable feedback loops to become a practical engineering discipline.

AI programmingCyberneticsHarness Engineering
0 likes · 29 min read
How Harness Engineering Turns AI Coding into Real-World Cybernetics
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 15, 2026 · Industry Insights

Can Harness Engineering Turn AI Agents into Stable Software Systems?

The article analyzes how AI‑driven agents reshape software engineering, tracing historical precedents, exposing the uncontrollability of open‑loop AI code generation, and proposing Harness Engineering—a structured, feedback‑rich environment that turns continuous code generation loops into stable, controllable systems.

AIAgent SystemsHarness Engineering
0 likes · 12 min read
Can Harness Engineering Turn AI Agents into Stable Software Systems?
DevOps in Software Development
DevOps in Software Development
Mar 25, 2026 · Industry Insights

Why Control Theory Is the Secret Sauce Behind Harness Engineering for AI Agents

The article explains how applying control‑theoretic concepts such as set points, sensors, controllers, actuators and feedback loops transforms AI‑driven coding agents into reliable, self‑correcting systems, illustrating the shift from prompt and context engineering to a full harness engineering practice.

AI agentsAutomationHarness Engineering
0 likes · 32 min read
Why Control Theory Is the Secret Sauce Behind Harness Engineering for AI Agents
Model Perspective
Model Perspective
Dec 28, 2025 · Fundamentals

Mastering PID Control: Theory, Tuning, and MATLAB/Simulink Implementation

Explore the core principles of PID control—from proportional, integral, and derivative actions that eliminate error—to practical parameter tuning methods and step-by-step MATLAB/Simulink simulations, including code snippets and block diagrams that illustrate how to model, discretize, and validate a PID controller for real‑world systems.

AutomationMATLABPID
0 likes · 11 min read
Mastering PID Control: Theory, Tuning, and MATLAB/Simulink Implementation
Continuous Delivery 2.0
Continuous Delivery 2.0
Nov 4, 2025 · Operations

Google's STAMP Framework: Redefining SRE for AI‑Driven Systems

Google’s SRE team is shifting from traditional error‑budget approaches to the STAMP (Systems-Theoretic Accident Model and Processes) framework, applying control theory and system‑level analysis to manage the growing complexity of AI‑powered services, improve safety, and proactively prevent hazardous states.

AIReliabilitySRE
0 likes · 12 min read
Google's STAMP Framework: Redefining SRE for AI‑Driven Systems
网易UEDC
网易UEDC
Nov 27, 2019 · Fundamentals

Unlocking Interaction Design: How ‘Control Power’ Transforms UI Communication

This article explores the emerging discipline of interaction design language, illustrating how concepts like control power, drag versus flick gestures, and case studies such as bus‑route apps and WeChat navigation can improve communication among designers, developers, and product managers.

Interaction Designcase studycontrol theory
0 likes · 9 min read
Unlocking Interaction Design: How ‘Control Power’ Transforms UI Communication