MetaStone-S1: How 32B Beats OpenAI o3-mini with Draft Paper Strategy
2 days ago 高效码农From Quick Guesses to Thoughtful Drafts: How MetaStone-S1 Makes a 32 B Model Rival OpenAI o3-mini 1. Why Do Large Language Models Need Draft Paper? Imagine you are taking a tough math final. If you must write the final answer in one shot, you will probably lose points. Give yourself scratch paper, let yourself jot down three different approaches, and then hand in the cleanest version—your score jumps. Large language models (LLMs) face the same problem. Traditional models generate one answer and stop. A newer idea called Test-Time Scaling (TTS) lets the model create many “draft solutions” at inference time, …
Gemini Deep Think: How Google’s AI Solves Complex Problems Like Humans
3 days ago 高效码农Gemini 2.5 Deep Think: When AI Takes the Time to Truly Think Gemini 2.5 Deep Think now available for Ultra subscribers! Great at tackling problems that require creativity & planning, it finds the best answer by considering, revising & combining many ideas at once. A faster variation of the model that just achieved IMO gold-level. Enjoy! Have you ever wished your AI assistant could take a moment to really think through complex problems before responding? Not just give you the first answer that comes to mind, but actually explore different angles, weigh potential solutions, and refine its thinking—much like how …
Interleaved Reasoning Technology: Revolutionizing AI’s Thought Process for Smarter Decisions
2 months ago 高效码农How to Make Large Language Models Reason More Intelligently? An In-Depth Exploration of Interleaved Reasoning Technology In today’s digital age, with the continuous development of artificial intelligence technology, large language models (LLMs) have become an extremely powerful tool, playing a significant role in numerous fields. However, despite their excellent performance in text generation, these models still have limitations when it comes to handling complex reasoning tasks. Today, let’s delve into a technology that can significantly enhance the reasoning capabilities of large language models—interleaved reasoning, and see how it changes the game. I. The Current Status and Challenges of Reasoning with …
DeepSeek-R1-0528: Revolutionizing AI Reasoning Capabilities with Advanced Problem-Solving
2 months ago 高效码农DeepSeek-R1-0528: Revolutionizing Reasoning Capabilities in Large Language Models Discover how DeepSeek’s latest upgrade transforms AI problem-solving with unprecedented reasoning depth and practical usability. 🔍 Key Breakthroughs in Reasoning Capabilities DeepSeek-R1-0528 represents a quantum leap in AI reasoning, achieved through algorithmic refinements and enhanced computational scaling: • 87.5% accuracy on AIME 2025 advanced math problems (vs. 70% in prior version) • 92% deeper reasoning chains: Average token usage per complex problem surged from 12K → 23K • Hallucination reduction and enhanced tool-calling support Performance Comparison Capability Use Case Improvement Mathematical Reasoning AIME/HMMT contests +17%–38% Code Generation Codeforces/SWE tasks +24%–37% Tool Integration …
Chain-of-Recursive-Thoughts (CoRT): How Self-Debate Makes AI Smarter Through Iterative Learning
2 months ago 高效码农How Chain-of-Recursive-Thoughts (CoRT) Makes AI Smarter Through Self-Debate Why Current AI Needs a Critical Thinking Upgrade Even state-of-the-art AI models occasionally produce puzzling outputs – like a math professor failing basic arithmetic. This gap between potential and performance inspired Chain-of-Recursive-Thoughts (CoRT), a groundbreaking method that teaches AI to systematically refine its answers through self-evaluation. Traditional AI operates like an overconfident student: answer first, think never. CoRT transforms this process into an expert peer-review system, achieving measurable improvements in programming assistance, logical reasoning, and technical analysis. Understanding the CoRT Framework The Self-Improvement Loop CoRT enables AI to: Generate multiple solution candidates …
Xiaomi MiMo-7B: The Compact AI Powerhouse Redefining Reasoning Efficiency
3 months ago 高效码农Xiaomi MiMo-7B: Small Model, Big Intelligence – Redefining AI Reasoning Capabilities Xiaomi-MiMo Introduction: The Rise of Compact Powerhouses in AI The AI industry has long operated under the assumption that bigger models mean better performance. Yet Xiaomi’s MiMo-7B series shatters this myth completely. With just 7 billion parameters, these open-source models outperform multiple 32B-scale competitors in mathematical reasoning and code generation tasks, even rivaling OpenAI’s o1-mini. What makes this breakthrough truly revolutionary? Xiaomi has open-sourced the complete training framework, model weights, and technical blueprints – a gift to developers worldwide seeking efficient reasoning-focused AI solutions. Technical Breakthroughs: How a 7B …