How TTT-E2E Lets Transformers Continuously Learn at Inference—A Plain English Guide
1 days ago 高效码农How to Let a Transformer Keep Learning While It Reads: A Plain-English Guide to TTT-E2E “ Keywords: long-context language modeling, test-time training, TTT-E2E, sliding-window attention, meta-learning, inference speed-up 1. The Problem in One Sentence Today’s best language models can open a book, but they cannot close it—they forget the first page before they reach the last. TTT-E2E, a paper posted on arXiv in December 2025, offers a different deal: read once, keep learning, and never pay more per new word. 2. A Quick Refresher (No Math Yet) What we already have Pain point Full attention Remembers everything, cost grows with …
Stubborn Persistence: The 2026 AGI Race & China’s Path to AI Leadership
24 days ago 高效码农Stubborn Persistence Might Win the Race – A Plain-English Walk-through of the Tsinghua AGI-Next Panel Keywords: next step of AGI, large-model split, intelligence efficiency, Agent four-stage model, China AI outlook, Tsinghua AGI-Next, Yao Shunyu, Tang Jie, Lin Junyang, Yang Qiang Why spend ten minutes here? If you only have time for one takeaway, make it this line from Tang Jie: “Stubborn persistence might mean we are the ones left standing at the end.” If you also want to understand what the leading labs are really fighting over in 2026-27, read on. I have re-organised the two-hour panel held on 10 …
QwenLong-L1.5: The Complete Post-Training Blueprint for Superior Long-Context LLMs
1 months ago 高效码农Unveiling QwenLong-L1.5: A Post-Training Blueprint for Mastering Long-Context Reasoning and Memory Management Summary QwenLong-L1.5, built on Qwen3-30B-A3B-Thinking, excels in long-context reasoning through innovative post-training techniques. It features a data synthesis pipeline for multi-hop tasks, stabilized RL with task-balanced sampling and AEPO, and a memory framework for ultra-long inputs. Evaluations show a 9.9-point average gain, matching GPT-5 and Gemini-2.5-Pro levels. Have you ever wondered why large language models struggle with lengthy texts, often losing track of key details across thousands of words? Picture this: you’re sifting through a massive report, needing to connect dots from scattered evidence to form a coherent …
Bottom-Up Policy Optimization: The Secret to LLM Reasoning Revealed
1 months ago 高效码农What’s Hiding Inside Your LLM? A New “Bottom-Up” Perspective on Optimization Have you ever wondered what actually happens inside a large language model like ChatGPT or DeepSeek when it generates an answer? We typically view it as a black box: question in, answer out. However, a recent study titled “Your Language Model Policy Secretly Contains Internal Policies” reveals a groundbreaking discovery: An LLM is not a single, unified policy. Instead, every internal layer and module is executing its own distinct “sub-policy,” working in concert to complete the reasoning process. This research acts like a “neural CT scan,” providing the first …
How WorldWarp’s Async Video Diffusion Creates 1000-Frame 3D Scenes from One Photo
1 months ago 高效码农From One Photo to a 200-Frame Walk-Through: How WorldWarp’s Async Video Diffusion Keeps 3D Scenes Stable A plain-language, code-included tour of the open-source WorldWarp pipeline For junior-college-level readers who want stable, long-range novel-view video without the hype 1. The Problem in One Sentence If you give a generative model a single holiday snap and ask it to “keep walking forward”, most pipelines either: lose track of the camera, or smear new areas into a blurry mess. WorldWarp (arXiv 2512.19678) fixes both problems by marrying a live 3D map with an async, block-by-block diffusion model. The code is public, the weights …
Interpretable Circuits Explained: How OpenAI’s Sparse Transformers Demystify Neural Networks
1 months 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, …
Google’s Titans & MIRAS: How to Give AI Genuine Long-Term Memory
1 months ago 高效码农Titans + MIRAS: Empowering AI with Genuine Long-Term Memory Core Question: How Can AI Models Achieve Human-Like Long-Term Memory? In today’s artificial intelligence landscape, we face a fundamental challenge: how can we enable AI models to remember and utilize accumulated knowledge over time, rather than having goldfish-like seven-second memory? This article delves deep into Google’s groundbreaking Titans architecture and MIRAS theoretical framework, which are redefining AI memory mechanisms, enabling models to learn, update, and retain important information in real-time. 1. The Memory Dilemma of Transformer Architecture Core Question: Why Can’t Existing Transformer Models Handle Ultra-Long Sequences? The Transformer architecture revolutionized …
Google HOPE Model: The Self-Learning AI That Rewrites Its Own Rules
2 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 …
How Stanford’s AI Reviewer Transforms Research Feedback from Months to Hours
2 months ago 高效码农How Stanford’s AI Reviewer Cuts Research Feedback from Months to Hours The Researcher’s Dilemma: A Painfully Slow Cycle Imagine spending three years on a research paper, only to face rejection six times. For one student, this wasn’t a hypothetical scenario. Each submission meant waiting roughly six months for feedback from the peer review process. These slow, noisy cycles, where reviews often focused more on judgment than on constructive guidance, provided only a faint signal for how to improve the work. This six-month iteration loop is not just frustrating; it’s a significant barrier to scientific progress. This very problem sparked a …
OLMo 3 32B: The Ultimate Open-source Language Model Guide
2 months ago 高效码农A Comprehensive Guide to OLMo 3 32B: The Fully Open-Source Language Model OLMo Logo Understanding OLMo: Open Language Models for the Research Community Have you ever wondered how sophisticated language models like ChatGPT actually work? Or perhaps you’ve been curious about how to leverage these powerful AI tools in your own projects? Today, we’re taking an in-depth look at OLMo 3 32B, a completely open-source language model developed by the Allen Institute for AI that provides full access to code, weights, and training details for the research community. OLMo stands for “Open Language Model,” representing a series of models specifically …
MiroThinker AI Research Assistant: Revolutionizing Tool-Augmented Reasoning for Complex Tasks
2 months ago 高效码农AI Research Assistant Revolution: How MiroThinker Redefines Tool-Augmented Reasoning Are you struggling with complex research tasks that require multiple tool calls and deep analysis? Traditional AI assistants often fall short when faced with multi-step research workflows. However, MiroThinker, an innovative open-source project, is quietly transforming how we approach intelligent research assistance. Today, we’ll explore this groundbreaking tool-augmented reasoning system that’s revolutionizing AI research capabilities. What Makes MiroThinker So Special? MiroThinker isn’t just another large language model—it’s a tool-augmented agent system specifically designed for research tasks. While regular AI assistants function like students who can answer questions, MiroThinker resembles a professional …
Kosmos AI Scientist: How It Delivers 6 Months of Research in One Day
2 months ago 高效码农Kosmos: The AI Scientist That Delivers 6 Months of Research in One Day Core question answered: What exactly can Kosmos do, and how does it compress half-a-year of human R&D into a single 24-hour cycle while remaining fully auditable? 1. TL;DR – Why You Should Care Kosmos is not another chatbot. It is a structured-world-model agent that reads 1,500 papers and executes 42,000 lines of analysis code in one run, returning a 30-page interactive report whose every claim can be clicked open to the exact paper paragraph or code cell that produced it. Beta users estimate the output equals 6.14 …
Cambrian-S: Spatial Supersensing for Robust AI Understanding
2 months ago 高效码农Cambrian-S: Teaching AI to Understand Space Like Humans Do – A Deep Dive into Spatial Supersensing Imagine asking a home robot to “find the coffee mug you saw on the kitchen counter three hours ago.” For humans, this is effortless—we maintain an implicit mental model of our environment, effortlessly tracking objects and spaces over time. For today’s AI systems, this seemingly simple task remains nearly impossible. Most video AI models excel at describing what’s directly in front of them but struggle to build persistent, structured understandings of 3D space that survive viewpoint changes, occlusions, and long time gaps. This article …
DS-STAR: Revolutionizing Data Science Automation with AI Agents and Unstructured Data Processing
3 months ago 高效码农DS-STAR: Google’s Multi-Agent Breakthrough That Teaches AI to Think Like a Data Scientist How a new framework transforms messy CSVs, JSON files, and text documents into reliable Python code without human intervention Imagine walking into your office to find a zip file containing seven different data formats—CSV tables, nested JSON files, markdown documents, and unstructured text logs. Your boss asks you to “find insights” from this data jumble. A typical data scientist would spend hours manually inspecting files, writing exploratory code, debugging errors, and iterating on their analysis plan. Now, Google Cloud and KAIST researchers have developed DS-STAR, an AI …
Kimi K2 Thinking: Revolutionizing AI Reasoning and Tool Invocation Stability
3 months ago 高效码农Kimi K2 Thinking: Redefining the Boundaries of AI Reasoning and Tool Use “ When AI learns to think deeply and stably invoke tools across hundreds of steps, what transformation does it bring? The Core Question This Article Answers This article comprehensively analyzes the core characteristics, technical architecture, performance metrics, and practical applications of the Kimi K2 Thinking model, helping technical decision-makers, developers, and AI researchers understand how this next-generation thinking model achieves seamless integration of deep reasoning and tool invocation. Model Introduction: The New Generation Thinking Agent Kimi K2 Thinking represents the most advanced open-source thinking model currently available. It …
Audio Flamingo 3: How This Open-Source AI Outhears Google Gemini
3 months ago 高效码农How Audio Flamingo 3 Redefines AI Hearing: From 1.3B to 7B in 18 Months The open-source audio-language model that’s outperforming giants like Gemini—while using 1/3 the parameters. The Breakthrough That Changed Everything In July 2025, NVIDIA dropped Audio Flamingo 3 (AF3): a 7B-parameter model that understands speech, music, and sounds for up to 10 minutes straight. It crushed Google’s Gemini Pro 1.5 on 20+ benchmarks, achieved 92.7% accuracy on bird-song classification (vs. Gemini’s 71%), and even chats back in real-time voice. Yet here’s the kicker: AF3’s predecessor (Audio Flamingo 1) was just a 1.3B “proof of concept” released in 2024. …
Revolutionizing Semantic RAG: The Power of Knowledge Graph Traversal Algorithms
3 months ago 高效码农Novel Knowledge Graph Traversal Algorithms: Enhancing Accuracy in Semantic Retrieval-Augmented Generation (RAG) Systems In the fast-paced evolution of artificial intelligence, large language models (LLMs) have become indispensable tools for information processing. However, relying solely on an LLM’s internal knowledge often limits its ability to answer complex or domain-specific questions accurately. This is where Retrieval-Augmented Generation (RAG) systems shine—they supplement LLMs with context from databases or knowledge graphs, enabling more precise and well-grounded responses. Yet traditional RAG systems have a critical limitation: they mostly rely on text matching in vector stores, which struggles to capture deep semantic connections between pieces of …
LongCat-Flash-Omni: The 560B Parameter Open-Source Breakthrough in Real-Time Omni-Modal AI
3 months ago 高效码农Excellent. I will now generate a 3,000+ word analytical and professional English technical blog—in the tone of Google AI Blog or OpenAI Research—based strictly and exclusively on the two input files you provided (README.md + Hugging Face model card). No external data or assumptions will be added. The output will follow Google/Baidu SEO and LLM-ingestion best practices, in Markdown format, with natural, factual, human-style writing. LongCat-Flash-Omni: Building a Unified Foundation for Real-Time Omni-Modal Intelligence Core Question: How can a single model perceive, reason, and interact across text, image, audio, and video — in real time — while maintaining large-scale efficiency? …
Enterprise Deep Research: How Steerable AI Agents Are Transforming Research
3 months ago 高效码农Title: Enterprise Deep Research (EDR): How Steerable Multi-Agent Systems Are Redefining AI-Powered Research Meta Description: Discover how Salesforce’s Enterprise Deep Research (EDR) framework uses steerable multi-agent AI to transform enterprise research, enabling real-time human guidance and superior benchmark performance. Introduction: When Research Agents Learn to Take Directions In October 2025, Salesforce AI Research open-sourced Enterprise Deep Research (EDR)—a multi-agent system that accepts real-time human guidance during research execution. This isn’t just another “AI research assistant” but an intelligent partner that understands natural language commands like “focus on peer-reviewed sources” or “ignore outdated information.” Imagine having a tireless research team that …
Glyph AI Breakthrough: How Visual Compression Is Revolutionizing Long-Text Processing
3 months ago 高效码农Visual Revolution: When LLMs Start Processing Text with “Eyes” This technical analysis is based on the October 2025 Glyph research paper. Views expressed are personal interpretations. 1. The 2025 AI Dilemma: The Compute Black Hole of Long-Text Processing When OpenAI’s o1 model triggered a reasoning compute arms race in 2024, Google DeepMind engineers uncovered a brutal truth: Every 100K tokens added to context increases training costs exponentially. Industry whitepapers from Q2 2025 revealed global AI compute demand surpassing $6.7 trillion, with 40% consumed by long-text processing. Against this backdrop, Glyph emerged from Tsinghua University and Zhipu AI – a framework …