Seed-OSS 36B: Revolutionizing Open-Source AI with Unmatched Context and Performance
17 hours ago 高效码农ASearcher: How Asynchronous Reinforcement Learning Breaks 10-Click Barrier in Open-Source Search Agents
18 hours ago 高效码农Going Beyond Ten Clicks: How ASearcher Uses Asynchronous Reinforcement Learning to Push Open-Source Search Agents Past 40 Turns Imagine you are asked to find the exact number of gold, silver, and bronze medals China won in the 2012 London Olympics as of 31 December 2024. A quick search returns two conflicting totals: “38-27-22” and “39-31-22”. A human researcher would open multiple official reports, cross-check doping appeals, and finally discover that one gold medal was later withdrawn. That process can take dozens of web pages and many reasoning steps—far more than the ten-turn limit that most open-source language agents accept today. …
ComoRAG: How AI Can Now Read Novels Like Humans [New Breakthrough]
1 days ago 高效码农Making Sense of Long Stories: How ComoRAG Lets AI “Read a Novel Like a Human” Imagine finishing a 200,000-word novel and being asked, “Why did Snape kill Dumbledore?” You would flip back several chapters, connect scattered clues, and build a coherent picture. ComoRAG does exactly that—turning one-shot retrieval into iterative reasoning and turning scattered facts into a working memory. Table of Contents What is ComoRAG? Why Classic RAG Struggles with Long Narratives The Three Pillars of ComoRAG End-to-End Walk-Through: Eight Steps from Query to Answer Hard Numbers: Four Benchmarks, Clear Wins Hands-On Guide: 30-Minute Local Demo Frequently Asked Questions One-Line …
vLLM CLI: Mastering LLM Deployment with Interactive Tools & GPU Optimization
3 days ago 高效码农vLLM CLI: A User-Friendly Tool for Serving Large Language Models If you’ve ever wanted to work with large language models (LLMs) but found the technical setup overwhelming, vLLM CLI might be exactly what you need. This powerful command-line interface tool simplifies serving LLMs using vLLM, offering both interactive and command-line modes to fit different user needs. Whether you’re new to working with AI models or an experienced developer, vLLM CLI provides features like configuration profiles, model management, and server monitoring to make your workflow smoother. Welcome screen showing GPU status and system overview What Makes vLLM CLI Stand Out? vLLM …
Arithmetic Paradox in AI: Why Advanced Models Fail at Basic Math
4 days ago 高效码农The Arithmetic Paradox: When Advanced AI Stumbles on Simple Math Recently, a seemingly trivial math problem sparked widespread discussion in AI circles: calculating the difference between 10.9 and 10.11. What should be a straightforward elementary school calculation has become a recurring stumbling block for cutting-edge AI models, including the newly launched GPT-5 and popular models like Gemini Pro 2.5. This phenomenon, while amusing on the surface, reveals a profound challenge in artificial intelligence development that deserves our serious attention. The Simple Math Problem That Tripped Up Advanced AI Let’s begin with the concrete example that has become something of a …
How the Hierarchical Reasoning Model Outperforms Billion-Parameter LLMs with Just 27M Parameters
5 days ago 高效码农Hierarchical Reasoning Model: The AI Architecture Outperforming OpenAI’s ‘o3-mini-high’ Key breakthrough: Singapore-based Sapient Intelligence lab has developed a 27-million parameter model that solves complex reasoning tasks with just 1,000 training samples – outperforming leading LLMs like DeepSeek-R1 and Claude 3. Why Current AI Models Struggle with Reasoning Today’s top language models (LLMs) face fundamental limitations in logical reasoning: 1. Architectural Constraints Fixed-depth architectures can’t scale with problem complexity Non-Turing complete design limits computational capability Polynomial-time problems remain unsolvable (research evidence) 2. Fragile Reasoning Process Over-reliance on Chain-of-Thought (CoT) prompting Single misstep causes complete reasoning derailment (2402.08939) Human reasoning occurs in …
Machine Learning Decoded: From Core Algorithms to Real-World Impact
5 days ago 高效码农Machine Learning: From Fundamentals to Real-World Applications Introduction Machine learning (ML) has transformed how we approach problem-solving across industries, from healthcare to finance. This guide explores core ML concepts based on Princeton University’s COS 324 course notes, covering supervised learning, unsupervised learning, deep learning, and reinforcement learning. Whether you’re a student or a professional, understanding these fundamentals will help you leverage data effectively. 1. Supervised Learning: Learning from Labeled Data 1.1 Linear Regression: Predicting Continuous Values What it is: A method to model the relationship between variables using a straight line. Equation: y = a₀ + a₁x₁ + a₂x₂ + …
Dual Chunk Attention: The Training-Free Breakthrough for 100k+ Token LLMs
6 days ago 高效码农What is Dual Chunk Attention? by @karminski-dentist dual-chunk-attention-concept (Image source: Paper “Training-Free Long-Context Scaling of Large Language Models”) DCA (Dual Chunk Attention) is a technology developed by institutions including the University of Hong Kong in 2024. It’s a training-free method to expand the context window of large language models. This means models like Llama2 70B, which originally only support a 4k token context window, can now handle more than 100k tokens without the need for any ongoing training. In simple terms, think of a language model’s context window as the “memory” it has when processing text. If you’ve ever tried …
M3-Agent: Revolutionizing Multimodal AI with Graph-Based Long-Term Memory
6 days ago 高效码农Seeing, Listening, Remembering, and Reasoning: A Practical Guide to the M3-Agent Multimodal Assistant with Long-Term Memory This post is based entirely on the open-source M3-Agent project released by ByteDance Seed. Every command, file path, and benchmark score is copied verbatim from the official repositories linked below. No outside knowledge has been added. TL;DR Problem: Most vision-language models forget what they saw in a video minutes later. Solution: M3-Agent keeps a graph-structured long-term memory that can be queried days later. Result: Up to 8.2 % higher accuracy than GPT-4o + Gemini-1.5-pro on long-video QA. Cost: Runs on a single 80 GB …
LLM Plagiarism Detection Breakthrough: How MDIR Technology Ensures AI Integrity
7 days ago 高效码农Large Language Model Plagiarism Detection: A Deep Dive into MDIR Technology Introduction The rapid advancement of Large Language Models (LLMs) has brought intellectual property (IP) concerns to the forefront. Developers may copy model weights without authorization, disguising originality through fine-tuning or continued pretraining. Such practices not only violate IP rights but also risk legal repercussions. This article explores Matrix-Driven Instant Review (MDIR), a novel technique for detecting LLM plagiarism through mathematical weight analysis. All content derives from the research paper “Matrix-Driven Instant Review: Confident Detection and Reconstruction of LLM Plagiarism on PC”. Why Do We Need New Detection Methods? Limitations …
On-Device Generative AI Model LFM2: Liquid AI’s Pocket-Sized Powerhouse for Fast, Offline AI
8 days ago 高效码农Pocket-Sized Powerhouse: Liquid AI Launches LFM2, the Fastest On-Device Generative Model You Can Actually Run Today Performance overview of LFM2 If you have ever tried to run a large language model on your laptop, you probably faced three headaches: The model is huge—several gigabytes before you even start chatting. RAM usage shoots up and the cooling fan sounds like a jet engine. Each new word appears slowly, one… token… at… a… time. Liquid AI’s new LFM2 (Liquid Foundation Models v2) is built to solve exactly these problems: 350 M to 1.2 B parameters, small enough for a phone. 2× faster …
BigModel Platform: Revolutionizing Enterprise AI Adoption with Modular Architecture & Smart Deployment
8 days ago 高效码农BigModel: An Integrated Platform for Large Model Services and Applications Introduction: Streamlining Enterprise AI Adoption The rapid advancement of artificial intelligence has transformed large models from research projects into essential business tools. BigModel emerges as a comprehensive solution designed specifically to help small and medium-sized enterprises overcome implementation barriers. This integrated platform simplifies the entire lifecycle of large model deployment – from data preparation and model training to application development and production deployment. By providing a unified environment with granular permission controls and modular architecture, BigModel accelerates AI adoption while maintaining enterprise-grade security and scalability. Platform Overview: Integrated Workflows for …
AA-LCR Benchmark Reveals AI’s Long Context Reasoning Challenges: Key Insights for Developers and Businesses
9 days ago 高效码农Exploring the Artificial Analysis Long Context Reasoning (AA-LCR) Benchmark: Insights from Real-World Data In today’s digital age, the ability of AI models to process and reason through large volumes of information is more critical than ever. From analyzing financial reports to understanding legal documents, knowledge workers rely on these models to handle complex tasks that involve sifting through thousands of tokens of data. That’s where the Artificial Analysis Long Context Reasoning (AA-LCR) benchmark comes in. Designed to evaluate how well language models can reason across multiple long documents, AA-LCR provides valuable insights into the capabilities and limitations of today’s leading …
Top 10 LLM Applications You Need to Know in 2024 [Ultimate Guide]
9 days ago 高效码农Exploring the World of LLM Applications: A Comprehensive Guide to Awesome LLM Apps Introduction: The Transformative Power of Language Models Large Language Models (LLMs) are fundamentally reshaping how humans interact with technology. The Awesome LLM Apps project serves as an extensive, curated repository showcasing practical implementations of these powerful models across diverse domains. This collection demonstrates how LLMs from leading providers like OpenAI, Anthropic, and Google Gemini—alongside open-source alternatives such as DeepSeek, Qwen, and Llama—can be transformed into functional applications that solve real-world problems. Whether you’re a developer, product manager, or technology enthusiast, this open-source project offers valuable insights into …
CRINN Vector Search Optimization: AI-Led Reinforcement Learning Slashes ANNS Latency by 85%
9 days ago 高效码农CRINN: Teaching an AI to Make Vector Search Lightning-Fast ❝ “My vector database is getting sluggish—can anything be done without a PhD in performance engineering?” “Is there a way to let software tune itself?” “Once my model is trained, can I still squeeze out more speed?” ❞ If you have asked any of these questions, this post explains a practical path forward. We will walk through 「CRINN」—a framework that uses 「contrastive reinforcement learning」 to accelerate 「approximate nearest-neighbor search (ANNS)」 by 10 %–85 %, without touching a line of hand-tuned assembly. 1. Why ANNS Matters More Every Day Real-world job Why …
HRM AI: How Brain-Inspired Hierarchical Reasoning Outperforms Traditional Models
10 days ago 高效码农Hierarchical Reasoning Model (HRM): Brain-Inspired AI for Complex Problem Solving Imagine an AI system that can solve puzzles like Sudoku or navigate mazes with near-perfect accuracy using just 1,000 training examples. Meet the Hierarchical Reasoning Model (HRM)—a breakthrough architecture inspired by the human brain’s ability to process information in layers and timescales. In this post, we’ll break down how HRM works, why it outperforms traditional models, and its potential to transform AI reasoning. The Challenge: Why Current AI Struggles with Deep Reasoning Most AI systems today rely on large language models (LLMs) built on the Transformer architecture. While powerful, these …
R-Zero: How AI Models Self-Improve Without Any Training Data
10 days ago 高效码农R-Zero: Teaching Large Language Models to Reason—Without Any Data “ A step-by-step guide for practitioners who want a self-improving LLM that starts from nothing but a base checkpoint. 1. The Problem We All Share Training a model to reason has always looked like this: Collect thousands of exam questions. Pay experts to write detailed, correct answers. Fine-tune the model on those answers. Hope the model generalises. That pipeline is slow, expensive, and hard to scale. R-Zero removes steps 1–2 entirely. It shows how one base model can act as both teacher and student, producing its own curriculum and steadily getting …
AutoRound: Revolutionizing LLM Quantization for Ultra-Low Bit Efficiency
10 days ago 高效码农AutoRound: Making Large Language Model Quantization Simple and Efficient In today’s rapidly evolving AI landscape, large language models (LLMs) have become increasingly powerful but also increasingly demanding in terms of computational resources. As these models grow larger, deploying them on standard hardware or edge devices becomes challenging. This is where model quantization comes into play—a technique that reduces model size while maintaining acceptable performance. Among the various quantization tools available, AutoRound stands out as a particularly effective solution. In this comprehensive guide, we’ll explore what makes AutoRound special, how it works, and how you can leverage it to optimize your …
Unlock OpenAI’s gpt-oss: Run & Fine-Tune Billion-Parameter Models on Consumer Hardware
11 days ago 高效码农The Complete Guide to Running and Fine-Tuning OpenAI’s gpt-oss Models with Unsloth You might wonder: How can I run billion-parameter open-source models efficiently? OpenAI’s newly released gpt-oss series combined with Unsloth’s toolchain enables high-performance inference and fine-tuning on consumer hardware. What Are gpt-oss Models? In August 2025, OpenAI open-sourced two breakthrough language models: gpt-oss-120b and gpt-oss-20b. Both models feature: Apache 2.0 license for commercial use 128k context window for long-form reasoning State-of-the-art performance in reasoning, tool use, and agentic tasks Key Model Specifications Model Parameters Performance Benchmark Core Strengths gpt-oss-20b 20 billion Matches o3-mini Tool calling, chain-of-thought reasoning gpt-oss-120b 120 …
How MLE-STAR is Revolutionizing Machine Learning Engineering: Beyond AutoML
11 days ago 高效码农MLE-STAR: Revolutionizing Machine Learning Engineering Through Intelligent Search and Targeted Refinement In today’s data-driven landscape, building effective machine learning models has become essential across industries. But let’s face it—developing high-performance ML solutions is complex, time-consuming, and often requires specialized expertise that many teams lack. What if there was a way to automate this process while maintaining quality? That’s precisely where MLE-STAR comes in—a groundbreaking approach that’s changing how we approach machine learning engineering. What Exactly is MLE-STAR? MLE-STAR (Machine Learning Engineering Agent via Search and Targeted Refinement) is an innovative system designed to automate the entire machine learning engineering workflow. …