Fun-ASR: Ultimate Guide to the High-Precision, Multilingual Speech Recognition Model
26 days ago 高效码农Fun-ASR: The Ultimate Guide to a High-Precision, Multilingual Speech Recognition Model Snippet Fun-ASR is an end-to-end speech recognition model trained on tens of millions of hours of data, achieving 93% accuracy in noisy environments. It supports 31 languages, 7 major Chinese dialects, and 26 regional accents, making it ideal for applications in education, finance, and more. Introduction In an era where voice interaction is becoming ubiquitous, the demand for robust, accurate, and versatile speech recognition technology has never been higher. Whether you’re developing a real-time transcription service for a multinational conference, creating a voice-activated system for a noisy factory floor, …
How to Adapt Full-Attention LLMs to Sliding Window Attention: The SWAA Practical Guide
26 days ago 高效码农How to Adapt Full-Attention LLMs to Sliding Window Attention: A Practical Guide to SWAA Featured Snippet Summary Sliding Window Attention Adaptation (SWAA) is a practical toolkit for adapting full-attention pretrained large language models (LLMs) to sliding window attention (SWA) without expensive pretraining. It combines five methods—prefill-only SWA, sink token preservation, layer interleaving, chain-of-thought prompting, and fine-tuning—to reduce long-context inference costs to linear complexity while recovering most original performance on models like Qwen3 and Llama. Why Sliding Window Attention Matters for Long-Context LLMs If you’ve ever tried running a large language model on a really long prompt—say, analyzing a full book …
Interpretable Circuits Explained: How OpenAI’s Sparse Transformers Demystify Neural Networks
28 days ago 高效码农Understanding Neural Networks Through Sparse Circuits: A Deep Dive into OpenAI’s 2025 Breakthrough Neural networks power some of the most advanced AI systems today, but their inner workings remain largely mysterious. We train these models by adjusting billions of connections, or weights, until they excel at tasks, but the resulting behaviors emerge in ways that are hard to decipher. In late 2025, OpenAI released groundbreaking research titled “Weight-sparse transformers have interpretable circuits” (Gao et al., 2025), introducing a novel approach to make models more transparent. By training weight-sparse Transformers—models where most weights are forced to zero—they created networks with clearer, …
RL for 3D Generation: Why Reinforcement Learning Is the Key to Smarter 3D Models
29 days ago 高效码农When Reinforcement Learning Meets 3D Generation: Why We Need a Paradigm Shift from “Can Generate” to “Can Reason” Core Question: Why do existing text-to-3D models always fall short on complex prompts, and can reinforcement learning enable them to think step-by-step like humans—from understanding global structure to refining local details? If you’ve ever tried generating an “acoustic guitar with a dark fingerboard, six strings, and a circular soundhole” only to receive an alien instrument with the wrong number of strings and an oddly shaped hole, you understand the frustration with current 3D generation technology. The research paper “Are We Ready for …
GLM-ASR-Nano-2512 Review: The 1.5B Model Breaking Speech Recognition Barriers
1 months ago 高效码农🚀 Breaking the Sound Barrier: An In-Depth Look at GLM-ASR-Nano-2512 and High-Performance Speech Recognition Snippet/Abstract: GLM-ASR-Nano-2512 is an open-source speech recognition model by Zhipu AI with a compact 1.5B parameters. It achieves the lowest average error rate (4.10) among its class, excelling in complex acoustic environments, offering superior dialect support (e.g., Cantonese), and robust performance for low-volume speech. 🌟 Introduction: The Next Generation of Acoustic-to-Text Conversion In today’s fast-paced digital world, the need for accurate, real-time, and robust Automatic Speech Recognition (ASR) is paramount. From transcribing critical professional meetings to enabling hands-free navigation, the technology must perform flawlessly across diverse …
PaCo-RL: How This Breakthrough Solves AI Image Consistency with Reinforcement Learning
1 months ago 高效码农PaCo-RL: A Breakthrough in Consistent Image Generation Using Reinforcement Learning Introduction Have you ever tried using AI to generate a series of coherent images—for creating story characters or designing multiple advertisement visuals—only to find the results inconsistent in style, identity, or logical flow? Consistent image generation remains a fundamental challenge in AI content creation, requiring models to maintain shared elements like character appearance, artistic style, or scene continuity across multiple images. In this comprehensive guide, we explore PaCo-RL (Pairwise Consistency Reinforcement Learning), an innovative framework that addresses these challenges through specialized reward modeling and efficient reinforcement learning. Whether you’re a …
CAPO Framework: How AI Learns Like Humans from Imitation to Discrimination
1 months ago 高效码农From Imitation to Discrimination: How a Generalized Curriculum Advantage Mechanism Enhances Cross-Domain Reasoning in AI Summary: This article introduces CAPO (Curriculum Advantage Policy Optimization), an innovative reinforcement learning training paradigm. It employs a staged curriculum, first using positive-advantage samples for imitation learning to build a stable foundation, then introducing negative-advantage samples for discrimination learning to enhance generalization. The method is compatible with mainstream optimization algorithms like GRPO and PPO, consistently improving mathematical reasoning performance by 1.7 to 4.0 points, and effectively generalizes to multimodal GUI reasoning scenarios with a 3.81-point gain, establishing itself as a versatile and robust optimization framework. …
EMMA: The 4B Multimodal AI That Outperforms 7B Rivals in Vision & Generation
1 months ago 高效码农EMMA: The Most Impressive Unified Multimodal Model of 2025 (And It’s Only 4B Parameters) Every week in 2025, someone drops a new “unified vision-generation” model and claims the throne. Most of them are 7–13B behemoths that eat 4–8k visual tokens per image and still struggle with basic image editing. Then Huawei Noah’s Ark Lab quietly uploaded a 4B-parameter model called EMMA that beats almost every public 7B unified model across understanding, text-to-image generation, and image editing — while using only 20% of the visual tokens of its competitors. This isn’t marketing fluff. These are head-to-head numbers from the paper. What …
GLM-4.6V: The Multimodal AI Breakthrough with Native Function Calling
1 months ago 高效码农GLM-4.6V: Ushering in a New Era of Visual Reasoning in Multimodal AI In today’s rapidly evolving artificial intelligence landscape, “multimodal” models capable of simultaneously understanding images and text are becoming central to technological progress. Today, we delve deeply into GLM-4.6V—an advanced vision-language model recently released by the Z.ai team that has garnered significant attention in the open-source community. It represents not just another leap in technology but a crucial step towards seamlessly connecting “visual perception” with “executable action.” If you’re curious about “what multimodal AI can actually do,” “how GLM-4.6V improves upon previous models,” or “how can I start …
Preventing RLHF Training Crashes in Large Language Models
1 months ago 高效码农Why RL for Large Language Models Keeps Crashing — and the 7 Engineering Tweaks That Finally Made a 30B MoE Stable After 300k GPU Hours “ What makes policy-gradient RL for LLMs explode, and how do we stop it? Token-level objectives are only a first-order approximation of the true sequence reward. When the training-inference gap or policy staleness grows, the approximation breaks. Importance sampling, clipping and Routing Replay keep the two gaps small and training stable. 0. One-glance cheat-sheet Scenario Must-have knobs Typical failure signal Proven combo in paper Pure on-policy (N=1) Importance-Sampling (IS) KL(μ‖π) ↑ entropy ↓ MiniRL w/ …
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 …
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 …
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 …
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. …
DeepSeek-V3.2: The Open-Source LLM Challenging GPT-5 & Gemini-3.0 in AI Reasoning
1 months ago 高效码农DeepSeek-V3.2: Pushing the Frontier of Open-Source Large Language Models In today’s rapidly evolving artificial intelligence landscape, large language models (LLMs) have become the core driving force behind technological advancement. Recently, DeepSeek-AI released the全新的DeepSeek-V3.2 model, a breakthrough that not only delivers outstanding performance across multiple benchmarks but also achieves an ingenious balance between efficiency and capability, injecting new vitality into the open-source AI community. Model Overview: The Perfect Fusion of Efficient Reasoning and Agentic AI DeepSeek-V3.2 is a large language model that integrates efficient computation, exceptional reasoning ability, and agent performance. It’s built upon three key technological innovations: DeepSeek Sparse Attention …
GigaWorld-0: The Next-Gen World Model Revolutionizing Embodied AI Training
1 months ago 高效码农GigaWorld-0: Building World Models to Drive Embodied AI Forward Have you ever wondered how AI systems can learn to interact with the real world without needing endless hours of physical trials? That’s where world models come in—they act as virtual simulators that generate realistic data for training AI agents. Today, let’s talk about GigaWorld-0, a framework that’s designed specifically as a data engine for vision-language-action learning in embodied AI. It’s a unified system that combines video generation and 3D modeling to create high-quality, controllable data. I’ll walk you through what it is, how it works, and how you can get …
Adv-GRPO: How Adversarial Reinforcement Learning Revolutionizes AI Image Generation
1 months ago 高效码农The Image as Its Own Reward: How Adversarial Reinforcement Learning Finally Fixes AI Image Generation What if the biggest problem in AI image generation isn’t the model’s ability, but how we tell it what “good” means? For years, researchers have struggled with a fundamental misalignment in reinforcement learning for text-to-image models: our reward functions keep teaching models to game the system rather than create genuinely better images. This article explores Adv-GRPO, a framework that treats images as their own reward source, eliminating reward hacking while delivering measurable improvements in quality, aesthetics, and text alignment. Why Do Existing RL Methods for …
Qwen3-Next-80B-A3B-Thinking: The Ultimate Guide to AI’s Most Advanced Reasoning Model
1 months ago 高效码农A Comprehensive Guide to Qwen3-Next-80B-A3B-Thinking: Technical Breakthroughs and Practical Applications In the rapidly evolving field of artificial intelligence, large language models are advancing toward larger parameter scales and stronger contextual processing capabilities. The model we’re exploring today—Qwen3-Next-80B-A3B-Thinking—represents a significant achievement in this trend. Whether you’re an AI developer, researcher, or someone interested in cutting-edge technology, this article will provide a thorough analysis of this model’s technical characteristics, performance, and practical application methods. What is Qwen3-Next-80B-A3B-Thinking? Qwen3-Next-80B-A3B-Thinking is the first version in the Qwen team’s new generation of foundation model series. This model is specifically optimized for complex reasoning tasks, achieving …
DeepSeekMath-V2: How Self-Verification Is Revolutionizing Mathematical AI Reasoning
1 months ago 高效码农DeepSeekMath-V2: How Self-Verification Is Revolutionizing AI Mathematical Reasoning Discover how DeepSeekMath-V2 achieves gold medal IMO 2025 performance and scores 118/120 on Putnam 2024 through revolutionary self-verification technology. The Self-Critical AI That’s Beating Human Mathematicians What if the key to mathematical excellence isn’t getting everything right on the first try, but rather developing an exceptional ability to recognize and fix your own mistakes? This is exactly what DeepSeekMath-V2 has demonstrated by achieving gold-medal performance at the International Mathematical Olympiad (IMO 2025) and scoring a stunning 118/120 on the prestigious Putnam 2024 competition—surpassing the human top score of 90. From “Answer-Focused” to …
Google HOPE Model: The Self-Learning AI That Rewrites Its Own Rules
1 months ago 高效码农Google’s HOPE Model Drops: A Self-Editing Neural Net That Keeps Learning After Training HOPE uses Nested Learning to update its own weights at inference time, beating Transformer, RetNet and Mamba on 10 benchmarks—with only 1.3 B parameters. Featured Snippet Q&A Q: What makes Google’s HOPE architecture different from Transformer? A: HOPE treats every layer as a nested optimizer that can modify its own weights during inference, enabling lifelong learning without catastrophic forgetting. Hook (3-second rule) Your LLM stops learning the moment you ship it. Google’s new HOPE model doesn’t. It keeps re-writing its own weights while users type—think of it …