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AI Architecture Hub
AI Architecture Hub
May 29, 2026 · Artificial Intelligence

Make Claude Code Auto‑Fix Its Own Bugs with a Ready‑to‑Copy Configuration

This article explains why Claude repeatedly makes the same coding errors, introduces a CLAUDE.md rule file and a series of hooks (PostToolUse, PreToolUse, Stop) plus an automatic retry loop and cross‑session memory, and shows a before‑after comparison that reduces manual debugging from 45 minutes to about 10 minutes per feature.

AIClaudeautomation
0 likes · 11 min read
Make Claude Code Auto‑Fix Its Own Bugs with a Ready‑to‑Copy Configuration
Machine Heart
Machine Heart
Apr 16, 2026 · Artificial Intelligence

CPL++: A Self‑Aware, Self‑Correcting Framework for Weakly Supervised Visual Grounding

The CPL++ framework equips weakly supervised visual grounding models with confidence‑aware pseudo‑label learning, self‑supervised association correction, and dynamic validation, enabling the model to detect and amend erroneous region‑query links during training, which yields absolute performance gains of 1–6 % across five benchmark datasets.

Visual GroundingWeak Supervisioncomputer vision
0 likes · 9 min read
CPL++: A Self‑Aware, Self‑Correcting Framework for Weakly Supervised Visual Grounding
BirdNest Tech Talk
BirdNest Tech Talk
Dec 8, 2025 · Artificial Intelligence

How the New PEV Agent Pattern Boosts Reliable LLM Automation in Go

The article introduces the Plan‑Execute‑Verify (PEV) agent pattern added to langgraphgo, explains its three‑stage workflow, core features, configuration, concrete Go examples, implementation details, comparisons with ReAct and Reflection, and discusses best practices, limitations, and trade‑offs for high‑risk automation.

GoLLM agentsLangGraphGo
0 likes · 9 min read
How the New PEV Agent Pattern Boosts Reliable LLM Automation in Go
Meituan Technology Team
Meituan Technology Team
Aug 28, 2025 · Artificial Intelligence

How Meeseeks Redefines LLM Instruction-Following Evaluation

Meeseeks, a new benchmark released by Meituan’s M17 team, systematically evaluates large language models’ instruction‑following ability with a three‑tier framework, multi‑round self‑correction, and extensive real‑world data, revealing performance gaps among models such as OpenAI o‑series, Claude, DeepSeek and Qwen2.5.

AILLM evaluationMeeseeks
0 likes · 13 min read
How Meeseeks Redefines LLM Instruction-Following Evaluation
DevOps
DevOps
May 6, 2025 · Artificial Intelligence

PPTAgent: An Open‑Source AI System for Automated Presentation Generation Using a Two‑Stage Editing Approach

PPTAgent, an open‑source AI tool jointly developed by the Chinese Academy of Sciences and Shanghai Jiexin Technology, automatically creates high‑quality PowerPoint slides by analyzing reference decks, extracting layout patterns, and iteratively editing content with a self‑correction mechanism, achieving superior content, design, and coherence scores compared to existing methods.

AIPPTAgentmultimodal models
0 likes · 6 min read
PPTAgent: An Open‑Source AI System for Automated Presentation Generation Using a Two‑Stage Editing Approach
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Feb 19, 2025 · Artificial Intelligence

DeepSeek’s Self‑Correction: Transforming AI Reliability and Safety

The article explores DeepSeek’s innovative self‑correction system—combining a Mixture‑of‑Experts architecture with reinforcement‑learning feedback—to achieve real‑time error detection, dynamic knowledge‑graph updates, and enhanced safety in high‑risk fields like autonomous driving and medical diagnostics.

AI safetyDeepSeekMixture of Experts
0 likes · 9 min read
DeepSeek’s Self‑Correction: Transforming AI Reliability and Safety
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 11, 2024 · Artificial Intelligence

How Does OpenAI’s o1 Achieve Self‑Correction? A Deep Dive into MCTS and SCoRe

Examining OpenAI’s o1 model, this article explores its self‑correction capability by linking test‑time scaling, MCTS‑style reasoning, and DeepMind’s SCoRe reinforcement‑learning framework, illustrating step‑by‑step demos, hypothesizing internal judgment mechanisms, and proposing training pipelines that combine self‑generated data with post‑training RL.

LLM reasoningMCTSOpenAI
0 likes · 12 min read
How Does OpenAI’s o1 Achieve Self‑Correction? A Deep Dive into MCTS and SCoRe
DataFunSummit
DataFunSummit
Sep 13, 2024 · Artificial Intelligence

Research on Domain Large Models by Fudan University Knowledge Workshop Lab

This article presents the Fudan University Knowledge Workshop Lab's comprehensive research on domain large models, covering background, domain adaptation, capability enhancement, collaborative workflows, challenges such as inference cost and alignment, and proposed solutions including source‑enhanced training, self‑correction mechanisms, and hybrid retrieval‑augmented generation.

AI researchKnowledge Graphsdomain adaptation
0 likes · 16 min read
Research on Domain Large Models by Fudan University Knowledge Workshop Lab
DataFunTalk
DataFunTalk
Jun 15, 2024 · Artificial Intelligence

Research on Domain Large Models by Fudan University Knowledge Factory Lab

This article presents Fudan University's Knowledge Factory Lab research on domain large models, covering background, challenges, data selection, source‑enhanced tagging, capability improvements, self‑correction, collaborative workflows, and retrieval‑augmented generation for practical AI deployment.

AI researchdomain adaptationknowledge graph
0 likes · 16 min read
Research on Domain Large Models by Fudan University Knowledge Factory Lab