How LongVie 2 Solves AI Video Generation: Sharp, Steerable 5-Minute Clips
12 hours ago 高效码农LongVie 2 in Plain English: How to Keep AI-Generated Videos Sharp, Steerable, and Five-Minutes Long “ Short answer: LongVie 2 stacks three training tricks—multi-modal control, first-frame degradation, and history context—on top of a 14 B diffusion backbone so you can autoregressively create 3–5 minute clips that stay visually crisp and obey your depth maps and point tracks the whole way through. What problem is this article solving? “Why do today’s video models look great for 10 seconds, then turn into blurry, flickering soup?” Below we walk through LongVie 2’s pipeline, show exact commands to run it on a single A100, …
Bloom Behavioral Evaluation Tool: What If AI Could Test Itself?
1 days ago 高效码农Bloom: The Open-Source “Behavioral Microscope” for Frontier AI Models Imagine you’re a researcher at an AI safety lab. You’re facing a newly released large language model, with a cascade of questions swirling in your mind: How “aligned” is it really? In complex, multi-turn conversations, might it fabricate lies to please a user? Given a long-horizon task, could it engage in subtle sabotage? Or, would it show bias toward itself in judgments involving its own interests? Historically, answering these questions required assembling a team to design hundreds of test scenarios, manually converse with the AI, and record and analyze the outcomes—a …
Seedance 1.5 Pro Complete Guide: AI Video & Audio Generation in Minutes
3 days ago 高效码农Seedance 1.5 Pro: How It Generates Video and Sound in One Go—A Complete Technical Walk-Through Can an AI model turn a short text prompt into a ready-to-watch clip with synchronized speech, music, and sound effects in minutes? Seedance 1.5 Pro does exactly that by treating audio and video as equal citizens inside one Diffusion Transformer. What problem is Seedance 1.5 Pro solving? It removes the traditional “picture first, dub later” pipeline and delivers a finished audiovisual scene in a single forward pass, while keeping lip-sync, dialect pronunciation, and camera motion under tight control. 1. 30-Second Primer: How the Model Works …
Demystifying Shapash: The Ultimate Tool to Make Machine Learning Models Speak Human
3 days ago 高效码农Demystifying Shapash: Making Machine Learning Models Speak Human Introduction: Why Model Interpretability Matters Have you encountered situations where your carefully trained machine learning model performs exceptionally on test sets but struggles to explain its predictions to business stakeholders? In critical domains like financial risk management or medical diagnostics, this lack of transparency can lead to serious consequences. Shapash addresses this pain point by transforming complex ML models into self-explanatory tools that communicate using clear labels and interactive visualizations. This comprehensive guide, based on official documentation, will walk you through Shapash’s technical architecture, practical implementation, and real-world applications while ensuring compliance …
Zero-Error EFLA: How to Fix Linear Attention’s Hidden Euler Problem with Exact ODE Solutions
6 days ago 高效码农# Zero-Error Linear Attention is a Free Lunch: How EFLA Turns the Delta Rule into an Exact ODE Solution > Can we keep linear-time attention and still eliminate numerical error completely? Yes—by treating the delta rule as a continuous-time ODE, solving it in closed form, and exploiting the rank-1 structure of the dynamics, EFLA delivers an infinite-order Runge–Kutta update with zero truncation error and zero extra parameters. ## What exact problem does EFLA solve? It removes the accumulation of local truncation error that plagues existing linear-attention mechanisms when sequences grow long, inputs are noisy, or activations are large, while retaining …
Fun-ASR: Ultimate Guide to the High-Precision, Multilingual Speech Recognition Model
6 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 Budget-Aware Search Agents Break Performance Ceilings (BATS Framework)
7 days ago 高效码农Running on a Budget, Yet Smarter—How “Money-Wise” Search Agents Break the Performance Ceiling Keywords: budget-aware tool use, test-time scaling, search agent, BATS, Budget Tracker, cost-performance Pareto frontier Opening: Three Quick Questions Hand an agent 100 free search calls—will it actually use them? If it stops at 30 and calls it a day, will more budget move the accuracy needle? Can we teach the machine to check its wallet before every click? A new joint study by Google, UCSB and NYU says YES. “Simply letting the model see the remaining balance pushes accuracy up while keeping the tab unchanged—or even smaller.” …
OneStory: How Adaptive Memory Solves Multi-Shot Video Generation’s Biggest Challenge
11 days ago 高效码农OneStory: Redefining Multi-Shot Video Generation with Adaptive Memory Abstract OneStory addresses the critical challenge of maintaining narrative coherence across discontinuous video shots by introducing an adaptive memory system. This framework achieves a 58.74% improvement in character consistency and supports minute-scale video generation through next-shot prediction and dynamic context compression. By reformulating multi-shot generation as an autoregressive task, it bridges the gap between single-scene video models and complex storytelling requirements. What is Multi-Shot Video Generation? Imagine watching a movie where scenes seamlessly transition between different locations and characters. Traditional AI video generators struggle with this “multi-shot” structure—sequences of non-contiguous clips that …
How ChatGPT’s Memory System Actually Works: The 4-Layer Architecture Behind the Illusion
11 days ago 高效码农ChatGPT Memory System Exposed: How It Remembers 33 Facts About You Without a Database When you ask ChatGPT what it knows about you, the response can be surprisingly personal. In one instance, it listed 33 distinct facts, ranging from a user’s name and career ambitions to their current fitness routine. This leads to a fundamental question: how does an AI model store, retrieve, and utilize this information so seamlessly? After extensive experimentation and reverse engineering through direct interaction, a surprising discovery emerged. ChatGPT’s memory system is not the complex, vector-database-driven architecture many might assume. There is no RAG (Retrieval-Augmented Generation) …
LivingSwap: The Breakthrough in Cinematic Video Face Swapping Using Source Video Reference
11 days ago 高效码农Title: High-Fidelity Face Swapping for Cinematic Quality: When AI Learns to “Reference” the Source Video Snippet: LivingSwap is the first video face-swapping model to use the source video itself as a pixel-level reference. By combining keyframe-guided identity injection with a novel reference-guided generation architecture, it achieves unprecedented temporal consistency and attribute fidelity in long, complex video sequences, reducing manual editing effort by up to 40x for film production. Imagine this scenario: an actor becomes unavailable to complete filming, or a director wants to recast a role in post-production. Traditionally, this meant costly reshoots or painstaking, frame-by-frame manual editing prone to …
AlphaEvolve: How Gemini-Powered Code Evolution Solves Intractable Optimizations
12 days ago 高效码农AlphaEvolve: the Gemini-powered coding agent that turns your “good-enough” algorithm into a world-beater — while you sleep What exactly did Google just release? AlphaEvolve is a fully-managed Google Cloud service that wraps Gemini models inside an evolutionary loop to mutate, test and breed better algorithms without human intervention. If you can write a seed program and a scoring function, it will return code that outperforms your hand-tuned version in days, not quarters. 1. Why brute-force search is dead for real-world optimization Core question: “My combinatorial space is astronomical — why can’t I just grid-search or throw more VMs at it?” …
AlphaEvolve: How Google Cloud’s Self-Improving AI Rewrites Code & Optimizes Your Infrastructure
12 days ago 高效码农AlphaEvolve: How Google Cloud Lets Gemini Rewrite Its Own Code and Why It Matters to Your Infrastructure “ Yes, a single Early-Access API now allows Gemini to propose, test and keep code changes that outperform hand-tuned baselines on real production bills of materials. Below is the complete play-book, straight from the private-preview documentation. What Exactly Is AlphaEvolve? AlphaEvolve is a cloud-native, evolutionary code-generation service that couples Gemini 2.0 (Flash for speed, Pro for depth) with user-supplied evaluation scripts. It repeatedly mutates an initial “seed” program, keeps the variants that improve a quantitative score, and returns a final patch ready for …
Apriel-1.6-15B-Thinker: The 30% More Efficient Multimodal AI Model Explained
13 days ago 高效码农Apriel-1.6-15B-Thinker: A Deep Dive into the Cost-Efficient Multimodal AI Powerhouse Snippet ServiceNow’s Apriel-1.6-15B-Thinker is a 15-billion parameter multimodal AI model that delivers competitive performance against models up to 10x its size. It achieves this by significantly reducing reasoning token usage by over 30%, fits on a single GPU, and scores 69 on key enterprise benchmarks like Tau2 Bench Telecom. Introduction: The New Frontier of Efficient AI In the rapidly evolving landscape of artificial intelligence, a persistent challenge has emerged: how to balance powerful performance with practical, cost-effective deployment. Large models are undeniably capable, but their massive size often translates to …
CAPO Framework: How AI Learns Like Humans from Imitation to Discrimination
13 days 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. …
How to Run LLMs on MediaTek Phones Using LiteRT-NeuroPilot
13 days ago 高效码农MediaTek NPU × LiteRT: Running LLMs on Phones Without Losing Your Sanity A field-note style walkthrough of the new LiteRT NeuroPilot Accelerator—what it is, why it matters, and how to ship a 1B-parameter model in an Android APK in under 30 min. 0. One-Sentence Take-away You can now compile a Gemma 3 1B model once and run it on millions of MediaTek phones at 1 600 tokens/s prefill—without writing a single line of SoC-specific C++—thanks to the LiteRT NeuroPilot Accelerator. 1. Why On-Device LLMs Keep Getting Stuck 1 cm from the Finish Line Core question: “I already have an INT8 …
Google’s Titans & MIRAS: How to Give AI Genuine Long-Term Memory
14 days 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 …
Preventing RLHF Training Crashes in Large Language Models
17 days 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/ …
AI Transparency Breakthrough: How OpenAI’s Confession Method Makes Models Honest
18 days 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 % …
R-Few: How Minimal Human Supervision Enables Stable LLM Self-Evolution
18 days 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 …
From Code Completion to Autonomous SWE Agents: The 2025 Roadmap to Code Intelligence
19 days 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 …