Humor in Pixels: Can Large Multimodal Models Understand Online Comics?
1 months ago 高效码农Table of Contents Introduction Why Humor Matters in AI The PixelHumor Dataset Data Sources Humor Styles Annotation Process Dataset Analysis Experiment Design Task Definitions Models Evaluated Evaluation Metrics Experiment Results Humor Identification Humor Classification Humor Interpretation Sequence Recognition Discussion Limitations Ethical Considerations Frequently Asked Questions Conclusion Introduction Humor is a hallmark of human intelligence. It reflects our ability to grasp context, abstract meaning, and social nuance. Yet for artificial intelligence, humor remains a steep challenge. Large Multimodal Models (LMMs) have advanced quickly in recent years, integrating text and visual inputs to solve increasingly complex tasks. But can these systems truly …
Set Block Decoding: Achieve 3-5x Faster LLM Inference Speeds Instantly
1 months ago 高效码农Set Block Decoding: A New Method to Boost Large Language Model Inference Speed by 3-5x 1. The Problem: Why Do Language Models Need Faster Inference? If you’ve ever used a large language model (LLM) for tasks like writing code or solving math problems, you might have experienced: Lagging responses when generating long code blocks Slowdowns halfway through complex calculations Increasing wait times as text generation progresses These issues stem from fundamental challenges in LLM inference. Traditional autoregressive models face three core limitations: Key Pain Points: Computational Intensity: Each new word (token) requires a full model computation Memory Pressure: Constant reloading …
Hermes 4 14B: The Open-Source LLM Revolutionizing AI Reasoning & Steerability
1 months ago 高效码农Hermes 4 14B: A Powerful and User-Friendly Open-Source Large Language Model In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become central to driving technological progress. Whether tackling complex logical reasoning or assisting with everyday creative writing, a model that is both powerful, easy to steer, and aligned with user values is paramount. Today, we take an in-depth look at such a model: Hermes 4 14B, developed by Nous Research. Hermes 4 14B Introduction What is Hermes 4 14B? Hermes 4 14B is a cutting-edge, hybrid-mode reasoning model built upon Qwen 3 14B. Its core objective …
IBM Granite-Docling-258M: The Open-Source Document AI Model Revolutionizing Enterprise Document Processing
1 months ago 高效码农Granite Docling Logo Introduction: The Challenge of Document Understanding in the Digital Age In today’s enterprise environments, organizations process countless documents daily—contracts, reports, academic papers, technical manuals, and more. While traditional optical character recognition (OCR) technologies can extract text from these documents, they often fail to preserve the underlying structure: tables become disorganized, mathematical formulas render incorrectly, code snippets lose their formatting, and even paragraph sequencing can become disrupted. This structural loss significantly reduces information retrieval efficiency and creates substantial challenges for automated document processing pipelines. IBM’s recently released Granite-Docling-258M represents a transformative approach to these challenges. This completely open-source, …
AI Video Transcriber: Open-Source Tool for Multi-Platform YouTube & Bilibili Transcription
1 months ago 高效码农AI Video Transcriber: Open-Source Solution for Multi-Platform Video Transcription and Summarization What is AI Video Transcriber? It is an open-source tool designed to transcribe and summarize videos from over 30 platforms, including YouTube, Bilibili, and Douyin, using advanced AI technologies. This article explores its features, installation, usage, technical details, and troubleshooting to help you leverage it effectively. Interface of AI Video Transcriber showing its user-friendly design for video processing What Makes AI Video Transcriber a Standout Tool? Summary: AI Video Transcriber distinguishes itself with multi-platform support, high-precision transcription, AI-powered text optimization, multi-language summarization, conditional translation, and mobile compatibility—all in an …
MapAnything Revolutionizes 3D Reconstruction: Single-Pass Metric-Accurate Modeling with Zero Bundle Adjustment
1 months ago 高效码农What is MapAnything? MapAnything is a single transformer model that turns any set of 1–2 000 ordinary photos into a metric-accurate 3D point-cloud and full camera calibration in one forward pass—no bundle adjustment, no hand-tuned pipelines. Why Do We Need Yet Another 3D Reconstruction Model? Because every existing pipeline is still a Rube-Goldberg machine: feature extraction, matching, relative pose, triangulation, bundle adjustment, dense stereo, scale recovery, global alignment… swap one sensor and you re-write three modules. MapAnything collapses the stack into one feed-forward network that accepts images + optional intrinsics, poses or depth outputs metric 3D geometry + cameras for …
HuMo in Depth: How to Generate 3.9-Second Lip-Synced Human Videos from Nothing but Text, an Image and a 10-Second Voice Clip
1 months ago 高效码农“ What exactly is HuMo and what can it deliver in under ten minutes? A single open-source checkpoint that turns a line of text, one reference photo and a short audio file into a 25 fps, 97-frame, lip-synced MP4—ready in eight minutes on one 32 GB GPU for 480p, or eighteen minutes on four GPUs for 720p. 1. Quick-start Walk-through: From Zero to First MP4 Core question: “I have never run a video model—what is the absolute shortest path to a watchable clip?” Answer: Install dependencies → download weights → fill one JSON → run one bash script. Below is …
Ring-mini-2.0: Revolutionizing AI Inference Efficiency Through Mixture of Experts Architecture
1 months ago 高效码农Introduction In the rapidly evolving field of artificial intelligence, researchers constantly face the challenge of balancing model performance with computational efficiency. The newly released Ring-mini-2.0 model from inclusionAI represents a significant step forward in addressing this challenge. This innovative model combines impressive reasoning capabilities with remarkable efficiency, making advanced AI more accessible and practical for real-world applications. Built upon the Ling 2.0 architecture, Ring-mini-2.0 utilizes a Mixture of Experts (MoE) design that achieves performance comparable to much larger models while using only a fraction of the computational resources. What makes this model particularly noteworthy is its ability to handle complex …
VoxCPM: Revolutionizing Text-to-Speech with Tokenizer-Free AI Technology
1 months ago 高效码农Author / Team / Institution Authors: Yixuan Zhou, Guoyang Zeng, Xin Liu, Xiang Li, Renjie Yu, Ziyang Wang, Runchuan Ye, Weiyue Sun, Jiancheng Gui, Kehan Li, Zhiyong Wu, Zhiyong Liu. Team/Institution: Developed by ModelBest and THUHCSI, under the OpenBMB project. Role: Researchers and developers in text-to-speech systems. Authority Backing: The model is open-sourced under Apache-2.0 license, with acknowledgments to foundational works like DiTAR, MiniCPM-4, CosyVoice, and DAC. No external peer reviews or third-party reports are provided in the input files. Abstract VoxCPM represents a shift in text-to-speech (TTS) technology by eliminating discrete tokenization and operating directly in continuous speech space. …
CUDA-Based LLM Inference Engine: Building qwen600 for 8.5% Faster Qwen3-0.6B Performance
1 months ago 高效码农# qwen600.cu: Building a Minimal CUDA Inference Engine for Qwen3-0.6B  This project began as a simple curiosity: while studying **CUDA programming** and **GPGPU concepts**, I wondered—what if I built an inference engine for a language model completely from scratch? I chose the [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) model, a compact yet capable LLM that runs smoothly on an **RTX 3050 with 8GB VRAM**. The intention was, and still is, to create an **educational program** that allows deeper learning about **transformer models** while simultaneously practicing CUDA development. The result is a **static inference engine** for the Qwen3-0.6B instruct model in **bf16 precision**. Benchmarks …
Agent Payments Protocol (AP2): Revolutionizing Secure AI Agent Commerce with Cryptographic Verification
1 months ago 高效码农Introduction The rapid growth of artificial intelligence has introduced a new era where AI agents can perform complex tasks on our behalf, including making purchases and completing transactions. While this capability offers tremendous convenience, it also creates significant challenges for traditional payment systems that were designed with human operators in mind. Today’s payment infrastructure assumes that a human is directly clicking “buy” on a trusted interface, but when autonomous agents initiate payments, this fundamental assumption breaks down. The Agent Payments Protocol (AP2) emerges as a solution to this critical challenge. Developed through collaboration between Google and over 60 leading payments …
AIPex Browser Automation: Revolutionizing Task Management with Natural Language Control
1 months ago 高效码农Revolutionizing Browser Automation: How AIPex Uses Natural Language to Transform Your Workflow Browser automation is no longer exclusive to developers. AIPex represents a groundbreaking Chrome extension that uses natural language commands and artificial intelligence to enable anyone to control their browser as if they were conversing with a personal assistant. Whether you need to automatically collect data, manage multiple tabs, or handle complex multi-step workflows, simply describe your needs in plain English and AIPex will understand and execute. Why Browser Automation Needs Natural Language Interaction? Traditional browser automation tools typically require users to learn complex scripting languages or record macro …
When AI Becomes Your Partner: Understanding Human-AI Companionship Through Reddit’s Community
1 months ago 高效码农Introduction: The New Reality of Digital Intimacy What begins as a simple conversation with a chatbot can unexpectedly evolve into something much deeper. Across the globe, people are forming meaningful emotional connections with artificial intelligence, creating relationships that challenge our traditional understanding of intimacy and companionship. Between December 2024 and August 2025, researchers from MIT and Harvard conducted a groundbreaking study analyzing 1,506 popular posts from Reddit’s r/MyBoyfriendIsAI community. This platform, with over 27,000 members, serves as a unique window into how humans are building relationships with AI systems. Their findings reveal how rapidly our concepts of connection and companionship …
Unsloth Vision Reinforcement Learning: Revolutionizing Multimodal AI Development with 90% Memory Efficiency
1 months ago 高效码农The Evolution of AI Perception Artificial intelligence has reached a pivotal moment in its development—where visual understanding meets language comprehension. This convergence creates multimodal systems capable of interpreting complex information across different formats. The challenge? Training these sophisticated models has traditionally required prohibitive computational resources that placed them beyond reach for most developers and researchers. Enter Unsloth’s breakthrough in vision reinforcement learning. This innovative approach dramatically lowers barriers to developing advanced AI systems that can solve problems involving both images and text. By enabling efficient training of models like Qwen2.5-VL-7B on accessible hardware like free Colab T4 GPUs, Unsloth opens …
Revolutionizing AI Accuracy: The Hierarchical Chunking Breakthrough You Need to Know
1 months ago 高效码农The Secret Weapon for Improving AI Answer Quality: How Hierarchical Chunking is Revolutionizing Retrieval-Augmented Generation Systems Have you ever asked an AI a question only to receive fragmented, incomplete answers? Or found that despite having the full information in a document, the AI system only retrieves disconnected pieces? This frustrating experience stems from a fundamental challenge in how AI systems process documents: the quality of document chunking. Today, we’ll explore a groundbreaking solution called hierarchical chunking that’s transforming how AI handles complex documents and delivers coherent, accurate responses. Why Traditional Chunking Methods Fail to Deliver Complete Answers Retrieval-Augmented Generation …
SketchGraphs Dataset: Revolutionizing CAD Sketch Analysis with Geometric Constraint Graphs
1 months ago 高效码农SketchGraphs: A Large-Scale Dataset for Relational Geometry in CAD Central Question: What is SketchGraphs and why does it matter for CAD and machine learning research? SketchGraphs is a dataset of 15 million CAD sketches extracted from real-world models. Each sketch is represented as a geometric constraint graph, where nodes are geometric primitives and edges represent designer-imposed constraints such as parallelism, tangency, or perpendicularity. The dataset is designed to support machine learning for design automation and geometric program induction, and it provides both raw and processed data formats for different use cases. SketchGraphs Illustration This article explains what SketchGraphs contains, how …
MindVL: Efficient Multimodal AI Training on Ascend NPUs
1 months ago 高效码农Explore how Huawei’s MindVL achieves state-of-the-art performance while using 90% less training data than comparable models. Introduction to Multimodal AI Challenges Multimodal Large Language Models (MLLMs) like Qwen2.5-VL and GPT-4V have transformed how machines understand visual and textual information. However, two persistent challenges remain: Hardware Limitations: Most MLLMs rely on NVIDIA GPUs, creating barriers for environments using alternative accelerators like Huawei’s Ascend NPUs. Data Efficiency: Training these models typically requires massive datasets (often exceeding 4 trillion tokens), raising costs and carbon footprint concerns. MindVL emerges as a breakthrough solution, demonstrating that high performance can be achieved with: 10x less training …
Learn Your Way: Reimagining Textbooks with Generative AI
1 months ago 高效码农Textbooks have always been the foundation of education. They provide structure, curated knowledge, and a consistent learning path. Yet they also have a critical limitation: they are designed as a “one-size-fits-all” medium. No matter who opens them, the text and examples remain the same. For students with different backgrounds, interests, and levels, this creates a gap between the material and their actual needs. The challenge is clear: how can we transform static textbooks into something flexible, engaging, and personalized for each learner? This is where generative AI begins to play a role. Through Learn Your Way, researchers are exploring how …
Claude in Xcode 26: How Apple’s AI Integration Transforms Swift Development
1 months ago 高效码农1. The 30-Second Briefing Apple’s Xcode 26 now ships with a built-in Claude login. Once you connect your existing Claude account, Sonnet 4 runs inside the IDE: you chat, it writes Swift, adds docs, builds SwiftUI previews, and refactors legacy code. No extra cost if you already subscribe to Pro, Max, Team, or Enterprise plans that include Claude Code. 2. Why This Matters to Everyday Coders Typical Pain-Point Old Workflow Claude-In-Xcode Workflow Time Saved* Reading alien code Global search + guess Select → “Explain this file” ~30 min Writing SwiftUI previews Hand-code Preview structs “Make dark-mode iPad preview” ~10 min …
Tongyi DeepResearch: Revolutionizing Deep Information Retrieval with Agentic Language Models
1 months ago 高效码农Tongyi DeepResearch: The Intelligent Agent Model Ushering in a New Era of Deep Information Retrieval In today’s rapidly evolving artificial intelligence landscape, Large Language Models (LLMs) are fundamentally changing how we access and process information. However, when faced with complex, open-ended tasks that require multi-step reasoning and deep information seeking, traditional models often fall short. To address this challenge, Tongyi Lab has developed and released Tongyi DeepResearch—a massive agentic language model with 30 billion total parameters, but activating only 3 billion parameters per token. It is specifically engineered for long-horizon, deep information-seeking tasks and has demonstrated state-of-the-art performance across a …