How NVIDIA’s Orchestrator-8B Outperforms GPT-5 While Costing 70% Less
1 months ago 高效码农NVIDIA Orchestrator-8B: How an 8B Model Beats GPT-5 on the Hardest Exam While Costing 70% Less Core question this post answers: How can an 8-billion-parameter model score 37.1% on Humanity’s Last Exam (HLE) — higher than GPT-5’s 35.1% — while being 2.5× faster and costing only ~30% as much? The answer is a complete paradigm shift: stop trying to solve everything inside one giant model. Instead, train a small “conductor” that intelligently delegates subtasks to a heterogeneous orchestra of tools and expert models. That conductor is Orchestrator-8B. This post is a full technical deep-dive for engineers, researchers, and AI builders …
Crisp Text-to-Image Generation: How Ovis-Image 7B Delivers 20B-Level Performance on One GPU
1 months ago 高效码农Ovis-Image: A 7-Billion-Parameter Text-to-Image Model That Punches at 20-Billion Scale—While Running on One GPU “ What makes a compact 7 B model able to render crisp, bilingual, layout-heavy text previously dominated by 20 B+ giants, and how can you deploy it today? TL;DR (the 30-second take) Architecture: 2 B multimodal Ovis 2.5 encoder frozen for alignment, 7 B MMDiT diffusion decoder trained from scratch, FLUX.1-schnell VAE stays frozen—10 B total, <24 GB VRAM. Training: four-stage pipeline (pre-train → instruction fine-tune → DPO preference → GRPO text-specialist) steadily improves word accuracy from 87 % → 92 %. Benchmarks: leads CVTG-2K English …
AI Transparency Breakthrough: How OpenAI’s Confession Method Makes Models Honest
1 months ago 高效码农Keeping AI Honest: How OpenAI’s “Confession” Method Works and Why It Matters “ Keywords: large language model honesty, Confession training, reward hacking, AI transparency, hallucination detection, scheming behavior, reinforcement learning safety TL;DR OpenAI’s latest proof-of-concept adds a second output—called a Confession—that asks the model to list every instruction it was given, judge whether it followed each one, and admit any shortcuts or rule-breaking. The confession score is completely separate from the main-answer reward, so the model is free to own up without penalty. In small-scale trials the trick already cuts “false negatives” (misbehavior that stays hidden) to ≈ 4 % …
Critical React Server Components Vulnerability: Immediate RCE Patch Guide
1 months ago 高效码农🚨 Urgent Security Alert: Critical Vulnerability Discovered in React Server Components (RSC) – Immediate RCE Risk and Patching Guide 🌟 Core Question Addressed: What is the severe security vulnerability found in React Server Components? How does it impact my application, and what immediate steps should I take to fix it and secure my app? The React team has issued an urgent security advisory detailing an unauthenticated Remote Code Execution (RCE) vulnerability in React Server Components (RSC). This flaw, reported by Lachlan Davidson, has been assigned the CVE identifier CVE-2025-55182 and is rated with a critical CVSS score of 10.0. All …
Build Your Own AI Coding Assistant: A Step-by-Step Guide with Claude API
1 months ago 高效码农Build Your Own AI Coding Assistant: A Step-by-Step Workshop Welcome to this exciting technical workshop where you’ll build your own AI-powered programming assistant from scratch! Whether you’re new to artificial intelligence or have some experience, this workshop will guide you through creating increasingly sophisticated versions of your assistant, culminating in a powerful local development tool. Imagine having an assistant that understands your programming needs, reads your code files, executes system commands, and even helps modify your code—all built with your own hands. This workshop provides clear guidance and examples for every step of the process. What You’ll Master in This …
R-Few: How Minimal Human Supervision Enables Stable LLM Self-Evolution
1 months ago 高效码农From “Self-Taught” to “Mentor-Guided”: How R-Few Enables Stable Self-Evolution of LLMs with Minimal Human Supervision This article aims to answer a core question: How can we build a Large Language Model (LLM) system capable of continuous and stable self-improvement without relying on massive amounts of labeled data, while preventing it from plateauing or veering off course during its own training? The vision of AI that can autonomously learn and evolve through practice, much like humans do, has long been a dream on the path toward more advanced intelligence. Imagine a model that could improve its reasoning abilities like AlphaZero mastered …
CPU Geometry Proving Breakthrough: How HAGeo Outperforms Neural Networks
1 months ago 高效码农Breaking the Neural Network Barrier: How a CPU-Only System Achieved Gold Medal Performance in Olympiad Geometry Core Question: Can geometry theorem proving achieve world-class performance without relying on neural networks or specialized hardware? For decades, automated theorem proving in Euclidean geometry has remained one of artificial intelligence’s most persistent challenges. While recent advances like AlphaGeometry demonstrated impressive capabilities by combining neural networks with symbolic reasoning, they relied heavily on GPU resources and complex machine learning infrastructure. This dependency created barriers for researchers and educators with limited computational resources. Now, a breakthrough method called HAGeo (Heuristic-based Auxiliary constructions in Geometric deduction) …
Web Agent Face-Off: RAG Outperforms HTML, MCP & NLWeb in E-commerce
1 months ago 高效码农Web Agent Interfaces Showdown: MCP vs RAG vs NLWeb vs HTML – A Comprehensive Technical Analysis Core Question: Which Web Agent Interface Delivers the Best Performance and Efficiency? This article addresses the fundamental question: How do different web agent interfaces compare in real-world e-commerce scenarios? Based on extensive experimental research comparing HTML browsing, RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), and NLWeb interfaces, we provide definitive insights into their effectiveness, efficiency, and practical applications. Our analysis reveals that RAG, MCP, and NLWeb significantly outperform traditional HTML browsing, with RAG emerging as the top performer when paired with GPT-5, achieving an …
AI Code Review at Scale: How OpenAI’s Codex Reviewer Earns Developer Trust
1 months ago 高效码农A Practical Approach to Verifying AI-Generated Code at Scale: Lessons from OpenAI’s Codex Reviewer Core question this post answers: When AI can write code far faster than humans can review it, how do we build a verification system that engineers actually trust and use every day? On December 1, 2025, OpenAI published one of the most concrete alignment progress updates of the year: a detailed case study of the dedicated code-review agent shipped with GPT-5-Codex and GPT-5.1-Codex-Max. This isn’t a research prototype — it’s running on every internal pull request at OpenAI, used proactively by engineers via the /review CLI …
From Code Completion to Autonomous SWE Agents: The 2025 Roadmap to Code Intelligence
1 months ago 高效码农From Code Completion to Autonomous SWE Agents: A Practitioner’s Roadmap to Code Intelligence in 2025 What’s the next leap after 90 % single-function accuracy? Teach models to behave like software engineers—plan across files, edit with tests, verify with sandboxes, and keep learning from real merges. 0. One-Minute Scan: Where We Are and What to Do Next Stage Today’s Best Use 30-Day Stretch Goal IDE autocomplete 7B FIM model, temperature 0.3, inline suggestions Add unit-test verifier, GRPO fine-tune → +4-6 % on internal suite Code review Generic LLM second pair of eyes Distill team comments into preference pairs, DPO for one …
Paper2Web: Turn Academic PDFs into Interactive Research Websites
1 months ago 高效码农PAPER2WEB: Bringing Your Academic Papers to Life An integrated guide for turning static PDFs into interactive, structured academic websites and presentation materials. Table of Contents Introduction What’s New Installation Guide Prerequisites Creating Conda Environment Installing Dependencies System Dependencies Configuration Quick Start Input Directory Structure Running All Modules Running Specific Modules Generating Academic Presentation Videos (Paper2Video) Environment Setup Optional: Talking-Head Generation Inference Pipeline Example Commands Paper2Web Dataset Overview Benchmarking Paper2Web Contributing Acknowledgments FAQ 1. Introduction Academic papers are highly structured and information-dense, but their PDF format often limits discoverability and interactivity. Researchers, students, and project teams face challenges such as: Difficulty …
Jaison: The Fault-Tolerant JSON Parser for LLM Outputs and Chinese Users
1 months ago 高效码农Jaison: The Fault-Tolerant JSON Parser Built for the LLM Era If you’ve ever asked ChatGPT, Claude, Gemini, Qwen, ERNIE, or any large language model to “return JSON,” you already know the pain: the output looks perfect to human eyes but explodes the moment you feed it to JSON.parse. A missing bracket, a trailing comma, Chinese full-width punctuation, single quotes, // comments, “`json Jaison is a zero-dependency, pure JavaScript JSON parser designed from the ground up to fix exactly these problems in a single pass. It silently repairs dozens of structural mistakes that LLMs love to make and hands you back …
Evo-Memory Benchmark: How LLM Agents Learn During Deployment
1 months ago 高效码农Evo-Memory: The streaming benchmark that forces LLM agents to learn at test time, not just remember What makes an agent truly get better while it works? A self-evolving memory that can retrieve, refine and reuse strategies across a never-ending task stream—Evo-Memory measures exactly that. What problem is Evo-Memory trying to solve? Core question: “Why do most LLM agents plateau even when they store every chat log?” Short answer: Storing is not learning. Static retrieval only replays facts; it never updates the policy. In long-horizon or goal-oriented streams the same type of sub-task appears again and again, but the agent treats …
Mistral 3 AI Models: The Complete Guide to Open-Source Multimodal Intelligence
1 months ago 高效码农Mistral 3 Unveiled: The Complete Family of Frontier Open-Source Multimodal AI Models Today marks a pivotal moment in the democratization of artificial intelligence. The barrier between cutting-edge research and practical, accessible tools continues to dissolve, driven by a philosophy of openness and community. Leading this charge with a significant new release is Mistral AI, announcing Mistral 3 — a comprehensive next-generation family of models designed to put powerful, multimodal intelligence into the hands of developers and enterprises everywhere. This isn’t merely an incremental update. Mistral 3 represents a full-spectrum ecosystem of AI models, meticulously engineered to address needs ranging from …
SuperSplat: The Ultimate Free 3D Gaussian Splatting Editor for Browser-Based Editing
1 months ago 高效码农SuperSplat: The Free, Open-Source 3D Gaussian Splatting Editor That Runs Entirely in Your Browser Have you ever opened a Gaussian Splatting file and thought, “This looks amazing, but it’s 700 MB and full of floating artifacts — I just want to clean it up quickly”? That used to be a painful process. Then I discovered SuperSplat — a completely free, open-source editor that lets you inspect, edit, optimize, and export 3D Gaussian Splats without installing anything. Everything happens in the browser. The live editor is ready right now: https://superspl.at/editor Just drag your .ply or .splat file in and start working. …
vLLM-Omni: Revolutionizing Omni-Modality AI Model Serving with High-Throughput Performance
1 months ago 高效码农Announcing vLLM-Omni: Easy, Fast, and Cheap Omni-Modality Model Serving Core Question Addressed: How can we efficiently serve the next generation of AI models that process and generate text, images, audio, and video, overcoming the limitations of serving engines designed only for text-based Autoregressive tasks? The landscape of generative AI is undergoing a profound transformation. Models are rapidly evolving from specialized Large Language Models (LLMs) to powerful “omni-agents” capable of seamlessly reasoning across and generating content in text, images, audio, and video modalities. This shift—from “text-in, text-out” to complex, heterogeneous input and output—demands an equally revolutionary shift in the underlying infrastructure. …
AI Can Now Hack Smart Contracts – The $4.6 Million Security Wake-up Call
1 months ago 高效码农AI and Smart Contract Exploitation: Measuring Capabilities, Costs, and Real-World Impact What This Article Will Answer How capable are today’s AI models at exploiting smart contracts? What economic risks do these capabilities pose? And how can organizations prepare to defend against automated attacks? This article explores these questions through a detailed analysis of AI performance on a new benchmark for smart contract exploitation, real-world case studies, and insights into the rapidly evolving landscape of AI-driven cyber threats. Introduction: AI’s Growing Role in Smart Contract Security Core Question: Why are smart contracts a critical testing ground for AI’s cyber capabilities? Smart …
ViBT Image Generation: How Brownian Bridge Models Achieve 4× Faster AI Inference
1 months ago 高效码农ViBT: Vision Bridge Transformer at Scale – A Practical Deep Dive What is ViBT and why does it achieve up to 4× faster inference than token-heavy conditional diffusion models while maintaining comparable quality? ViBT is the first large-scale realization of Brownian Bridge generative models for vision tasks. Instead of the classic “noise-to-data” paradigm, it directly learns stochastic trajectories from a structured source (image/video) to a structured target, eliminating most conditioning tokens and dramatically reducing compute. Figure: Example results of ViBT across instruction-based editing, stylization, colorization, and frame interpolation. Why the Noise-to-Data Paradigm Feels Wrong for Conditional Generation Most modern image …
SlideSCI: The Revolutionary PPT Plugin That’s Transforming Scientific Research Presentations
1 months ago 高效码农★The PPT Plugin That Changed Scientific Research: A Deep Dive into SlideSCI★ Have you ever struggled with creating research presentation slides? Do you spend hours trying to align images, manually adjust captions, and wrestling with code blocks and mathematical equations? If you’ve faced these challenges, this specialized PowerPoint plugin designed for researchers might completely transform your workflow. Plugin Features Preview Why Researchers Can’t Live Without PPT Plugins In academic research, PowerPoint presentations are indispensable tools. Whether it’s weekly lab meetings or conference presentations, we all need to create professional and content-rich slides. However, Microsoft PowerPoint, as a general-purpose office software, …
STARFlow-V: Inside Apple’s First Normalizing-Flow Video Generator You Can Actually Run
1 months ago 高效码农STARFlow-V: Inside Apple’s First Normalizing-Flow Video Generator That You Can Actually Run Today What is STARFlow-V in one sentence? It is a fully open-source, causal, normalizing-flow video model that produces 480p clips with a single forward pass—no diffusion schedule, no vector-quantization, just an invertible Transformer mapping noise to video. What exact question will this article answer? “How does STARFlow-V work, how good is it, and how do I reproduce the results on my own GPU cluster?” 1. Why Another Video Model? (The Motivation in Plain Words) Apple’s team asked a simple question: “Can we avoid the multi-step denoising circus and …