Hey Learning Crew, Ernis here, ready to dive into another fascinating piece of research from the PaperLedge! Today, we're tackling a challenge that's becoming increasingly important as AI gets smarter: keeping these powerful reasoning models safe. Think of it like this: we're teaching a super-smart kid, but we need to make sure they use their knowledge responsibly.
The paper we're unpacking focuses on something called Large Reasoning Models, or LRMs. Now, don't let the name scare you. Essentially, these are AI systems designed to think through complex problems, step-by-step, kind of like how you'd solve a puzzle. They're amazing at tasks that require logic and deduction.
But here's the catch: because these models follow structured reasoning paths, if you feed them a bad prompt – a harmful prompt as the researchers call it – they might end up generating unsafe or undesirable outputs. It's like giving that super-smart kid a bad idea; they might be smart enough to figure out how to execute it!
So, what's been done so far to address this? Well, there are existing "safety alignment methods." These try to reduce harmful outputs, but they often come at a cost. Imagine trying to teach our smart kid not to do something bad, but in the process, you accidentally stifle their creativity and ability to think deeply. This is what happens with current methods: they can degrade the reasoning depth of the AI, making it less effective at complex tasks. Plus, they can still be tricked by clever "jailbreak attacks" – ways to bypass the safety measures.
That's where this new research comes in. The researchers introduce SAFEPATH. Think of it as a quick safety lesson before the AI starts reasoning. It's a lightweight method, meaning it doesn't require a ton of computing power. Here's how it works:
When the LRM receives a harmful prompt, SAFEPATH kicks in.
It makes the AI generate a short "Safety Primer" – just a few words that remind it to be safe and responsible.
Then, the AI continues reasoning as usual, but with that safety reminder in mind.
It's like giving our super-smart kid a quick pep talk about being a good citizen before they tackle a tricky problem. The best part? It doesn't interfere with their ability to think deeply and solve the problem effectively.
The results are pretty impressive! The researchers found that SAFEPATH significantly reduces harmful outputs. In one example, it reduced harmful responses by up to 90% and blocked over 80% of jailbreak attempts in one particular model. And the best part? It does this while using way less computing power than other safety methods. They even came up with a zero-shot version that doesn't require any fine-tuning!
"SAFEPATH effectively reduces harmful outputs while maintaining reasoning performance."
This research matters for several reasons:
For AI developers: It provides a more efficient and effective way to align LRMs with safety guidelines.
For policymakers: It offers insights into how to regulate AI development and deployment to minimize potential risks.
For the general public: It helps ensure that AI systems are used responsibly and ethically.

This paper also takes a step back and looks at how well current safety methods for regular Large Language Models work when you try to apply them to these reasoning-focused models. And, surprise, surprise, the paper shows that many of these existing methods don't translate very well and uncovers important differences between LLMs and LRMs. This means we need new and specific safety approaches when it comes to these reasoning-focused AI.
So, what do you think, Learning Crew? It's a fascinating step forward in making AI safer and more reliable. Here are a couple of questions that popped into my mind:
How might we scale up SAFEPATH to handle even more complex and nuanced forms of harmful prompts?
Could we adapt the "Safety Primer" concept to include more specific ethical guidelines or values, tailored to different contexts?
Let me know your thoughts in the comments! Until next time, keep learning, keep questioning, and keep exploring the amazing world of AI!Credit to Paper authors: Wonje Jeung, Sangyeon Yoon, Minsuk Kahng, Albert No

Hey PaperLedge crew, Ernis here! Get ready to dive into some seriously cool AI stuff. Today, we're talking about making AI assistants that are, well, actually helpful. Think less annoying pop-up ads, and more like a super-attentive friend who anticipates your needs before you even realize them yourself.
So, there's this new paper that's tackling a big problem with current AI assistants – they're often kinda…dumb. They react to what you just did, like clicking a button, but they don't really understand the bigger picture. Imagine a virtual assistant that only knows you're typing an email but has no clue you're running late for a meeting and stressed out. Not exactly helpful, right?
This research introduces something called ContextAgent. Think of it as an AI that's trying to become Sherlock Holmes for your daily life. The team aimed to create an LLM agent that incorporates sensory and historical contexts to enhance proactive capabilities.
Instead of just looking at what's happening on your computer screen, ContextAgent pulls in information from all sorts of places, especially wearable devices like smartwatches or glasses. It's like having AI ears and eyes on your side, processing video, audio, and other sensor data to understand what you're really doing and what you might need.
"ContextAgent first extracts multi-dimensional contexts from massive sensory perceptions on wearables (e.g., video and audio) to understand user intentions."
For example, imagine you're rushing around the kitchen, muttering about needing a specific ingredient. ContextAgent, through your smartwatch mic, picks up on that, checks your calendar and sees you have friends coming over for dinner, and proactively suggests a recipe that uses that ingredient. Boom! Problem solved before you even fully formed it.
But it's not just about the immediate situation. ContextAgent also learns from your past behavior. Think of it as building a personal profile, a "persona context". It remembers that you always forget your umbrella when it's drizzling, or that you prefer your coffee extra strong in the mornings. This historical data helps it make even smarter predictions about what kind of assistance you might need.
The amazing thing is that when it figures out you need help, it doesn't just throw a bunch of options at you. It automatically uses the right "tools" to assist you, quietly and efficiently. Think of it like this: instead of asking if you want to set a reminder, it just does it based on your conversation and calendar. It's about being helpful without being intrusive.
Now, to prove that ContextAgent is actually better than existing systems, the researchers created a special test called ContextAgentBench. It's a benchmark with 1,000 real-world scenarios, covering everything from working at your desk to cooking dinner. They tested ContextAgent against other AI assistants, and guess what? It performed significantly better, achieving 8.5% higher accuracy in proactive predictions and 6.0% higher accuracy in tool calling.
Proactive predictions: How well the AI can guess what you need before you ask.
Tool calling: How accurately it can choose the right tool (like setting a reminder or sending a message) to help you.
These results are pretty impressive, suggesting that this approach of using sensory data and personal history is a big step forward in creating truly helpful AI assistants.
So, why does this research matter?
For everyday listeners: Imagine a world where your AI assistant anticipates your needs, saving you time and reducing stress.
For developers: This research provides a blueprint for building more sophisticated and user-centric AI assistants.
For ethicists: This raises important questions about data privacy and the potential for AI to become too intrusive.
This research opens up a lot of possibilities, and also raises some interesting questions:
How comfortable are we with AI constantly monitoring our actions and conversations?
Could this technology be used to manipulate or influence our behavior?
What safeguards need to be in place to protect our privacy and ensure that these AI assistants are used ethically?
That's all for this episode, crew! Keep thinking, keep learning, and keep questioning. Until next time!Credit to Paper authors: Bufang Yang, Lilin Xu, Liekang Zeng, Kaiwei Liu, Siyang Jiang, Wenrui Lu, Hongkai Chen, Xiaofan Jiang, Guoliang Xing, Zhenyu Yan

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're talking about how to make AI really understand what we want from it, kind of like teaching a super-smart puppy good manners.
The paper we're looking at introduces something called Reward Reasoning Models (RRMs). Now, that sounds complicated, but the core idea is pretty straightforward. Think of it this way: Large Language Models, like the ones powering your favorite chatbots, learn by getting feedback. This feedback comes in the form of 'rewards' – basically, a thumbs up or thumbs down for the answers they give.
But sometimes, figuring out if an answer is truly good isn't so simple. It requires a little deeper thought. That's where RRMs come in. Instead of just instantly judging the answer, they take a moment to reason about it. It's like if you asked your friend for directions and they didn't just blurt out the first thing that came to mind, but instead thought through the different routes, considering traffic and shortcuts.
So, how do these RRMs learn to reason? Well, the researchers used a clever trick. They didn't have to spoon-feed the models with examples of perfect reasoning. Instead, they used a technique called reinforcement learning to let the RRMs self-evolve their reasoning skills. Imagine training a dog by rewarding it for figuring out a puzzle, rather than showing it the solution every time!
The cool thing is that these RRMs can adapt. If a question is easy, they can give a quick reward. But if it's a tricky one, they can use extra "brainpower" (or, in this case, test-time compute) to really think it through before deciding on the reward. It’s like having a student who knows when to spend more time on a difficult problem.

"Through chain-of-thought reasoning, RRMs leverage additional test-time compute for complex queries where appropriate rewards are not immediately apparent."

So, why does this matter? Here's the breakdown:

For AI developers: This is a potential game-changer for building more reliable and helpful AI assistants. Better reward models mean better training, which means better AI.


For everyday users: Imagine chatbots that are less likely to give misleading or unhelpful information. RRMs could contribute to more trustworthy and useful AI interactions.


For society as a whole: As AI becomes more integrated into our lives, ensuring it aligns with our values becomes crucial. RRMs offer a way to guide AI more effectively, reducing the risk of unintended consequences.

The researchers even made their pre-trained RRMs available online! You can find them on Hugging Face - I will add the link to the show notes.
Now, a couple of things that popped into my head while reading this paper:

Could this approach be adapted to other areas of AI, like image recognition or robotics?


How do we ensure that the reasoning process of RRMs is transparent and understandable, so we can avoid potential biases or unintended outcomes?

What do you think, PaperLedge crew? Let me know your thoughts in the comments! Until next time, keep those neurons firing!Credit to Paper authors: Jiaxin Guo, Zewen Chi, Li Dong, Qingxiu Dong, Xun Wu, Shaohan Huang, Furu Wei

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool AI research! Today, we're talking about teaching computers to really see and understand images, not just recognize objects.
Think about it: you look at a picture, and almost instantly, you can describe what's happening, figure out the context, and even answer questions about it. That's reasoning! We want to get AI to that level, especially when it comes to images.
Now, the typical way to teach AI to reason has been to give it examples of how to think step-by-step, a process called "chain-of-thought." It's like showing your work in math class. But what if we could teach it to reason without explicitly spelling out every step?
That's what the folks behind this paper tackled. They focused on visual language models (VLMs), which are AI systems that can understand both images and text. They used a technique called reinforcement learning. Imagine training a dog: you give it treats (rewards) when it does something right. With reinforcement learning, the AI gets "rewards" for giving correct answers to visual questions.
Here’s the catch: the researchers found that if you only reward the VLM for answering correctly, it can start taking shortcuts! Think of it like a student who crams for a test and only memorizes the answers, instead of understanding the concepts. The VLM might perform well on the training questions, but then totally bombs when it sees something new.
"Simply applying reinforcement learning to a VLM can lead the model to develop shortcuts from easy questions, thereby reducing its ability to generalize across unseen data distributions."
So, how do you prevent these AI shortcuts? This is where it gets interesting. The researchers realized they needed to force the VLM to really look at the image first. They did this by making the AI describe the image in detail before it even tried to answer the question. It's like telling the AI, "Okay, before you answer, tell me what you see. What's happening in this picture?"
They call this a caption-reason-answer format. First, the VLM generates a detailed caption (description) of the image. Then, it uses that caption to construct a reasoning chain – a step-by-step explanation of how it arrived at the answer. Finally, it gives the answer.
And guess what? It worked! They trained their VLM, which they named Visionary-R1, on a bunch of visual question-answer pairs (273,000 of them!), and it blew away other powerful AI models like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro on visual reasoning tests. That's like, a major achievement!
Why does this matter?
For AI developers: It shows a new way to train VLMs without relying on those tedious, human-labeled "chain-of-thought" examples.
For anyone interested in AI safety: Preventing AI from taking shortcuts is crucial for building reliable and trustworthy systems.
For the average person: Better visual reasoning in AI could lead to improvements in areas like self-driving cars, medical image analysis, and even robots that can help around the house.
So, here are a few things I've been pondering:
Could this caption-reason-answer approach be applied to other types of AI tasks, like understanding complex documents or solving math problems?
How do we ensure that the AI's captions are accurate and unbiased? Could biased captions lead to biased reasoning?
What are the ethical implications of having AI that can "see" and understand the world around us?
That's all for this episode. Let me know your thoughts on this paper! I'm super curious to hear what you all think. Until next time, keep learning!Credit to Paper authors: Jiaer Xia, Yuhang Zang, Peng Gao, Yixuan Li, Kaiyang Zhou

Alright Learning Crew, Ernis here, and welcome back to the PaperLedge! Today, we're diving into a fascinating paper about making AI think... well, think better. It's all about Large Reasoning Models, or LRMs – think of them as the brainiacs of the AI world, tackling complex problems and trying to figure things out.
Now, these LRMs often use something called "Mixture-of-Experts," or MoE. Imagine you have a team of specialists, each an expert in a different area. When a tricky question comes in, the system chooses the right expert, or a mix of experts, to handle it. It's like assembling the Avengers for a specific mission! This allows for a more structured and efficient way to deal with different challenges.
But here's the catch: sometimes, these AI brains can overthink or underthink a problem. Overthinking is like getting lost in the weeds, going down rabbit holes, and ultimately missing the forest for the trees. Underthinking, on the other hand, is like jumping to conclusions without properly considering all the evidence. Neither is ideal when you're trying to solve complex problems!
That's where this research comes in. The authors introduce a new method called RICE, which stands for Reinforcing Cognitive Experts. The goal of RICE is to improve the reasoning performance of these models, making them more efficient and accurate, without needing to retrain the entire AI or use complicated tricks.
So, how does RICE work? Well, it identifies "cognitive experts" within the larger MoE architecture. Think of these cognitive experts as the project managers of the AI brain. They're the ones focused on the process of thinking itself. The researchers used a clever technique called "normalized Pointwise Mutual Information" (nPMI) to find these experts by looking for parts of the model that tend to use words like "" – literally signals of the AI engaging in reasoning.
Once they've identified these cognitive experts, RICE reinforces them, giving them a bit of a boost during the reasoning process. It's like giving the project manager a little extra caffeine to keep them focused and on track!
The idea is to nudge the model towards a more deliberate and thoughtful approach, without sacrificing its overall abilities.
The researchers tested RICE on two powerful MoE-based LRMs, DeepSeek-R1 and Qwen3-235B, using challenging quantitative and scientific reasoning benchmarks. And guess what? RICE consistently improved the models' reasoning accuracy, cognitive efficiency, and ability to generalize across different types of problems. Importantly, RICE proved to be more effective than traditional methods like carefully crafting prompts or limiting the model's responses.
In essence, this research shows that by strategically reinforcing the parts of an AI brain that are responsible for the process of thinking, we can make the whole system much smarter and more efficient.
So, why should you care about this? Well, if you're:
An AI researcher: This offers a new, lightweight, and interpretable way to improve the reasoning abilities of large language models.
A developer using AI: You could potentially use RICE to make your AI applications more reliable and accurate.
Just curious about AI: It's a fascinating glimpse into how researchers are trying to understand and improve the way AI systems think.
This research opens up some interesting questions. For instance:
Could RICE be adapted to improve other aspects of AI performance, such as creativity or problem-solving?
How can we better identify and understand the roles of different experts within a Mixture-of-Experts architecture?
What are the ethical implications of making AI systems more efficient and powerful reasoners?
Food for thought, Learning Crew! That's all for today's episode. Stay curious, and I'll catch you next time on the PaperLedge!Credit to Paper authors: Mengru Wang, Xingyu Chen, Yue Wang, Zhiwei He, Jiahao Xu, Tian Liang, Qiuzhi Liu, Yunzhi Yao, Wenxuan Wang, Ruotian Ma, Haitao Mi, Ningyu Zhang, Zhaopeng Tu, Xiaolong Li, Dong Yu

Alright learning crew, Ernis here, ready to dive into something that's going to get our mental gears turning! Today, we're talking about a fascinating new benchmark called SATBench. Think of it as a logic playground designed to really test how well large language models, or LLMs – like the ones powering your favorite chatbots – can actually think logically.
Now, you might be thinking, "Don't these AI models already do amazing things? Write poems, translate languages, even code?" And you'd be right! But what this research is digging into is a more fundamental kind of reasoning. It's not just about spitting out information; it's about solving puzzles with logical constraints.
Imagine you're trying to solve a Sudoku puzzle. You have all these rules – numbers can't repeat in a row, column, or box – and you have to find a combination that satisfies all of those rules. That's the basic idea behind what's called a "Boolean satisfiability" or SAT problem. And SATBench uses these kinds of problems, disguised as stories, to challenge LLMs.
What makes SATBench different? Well, a lot of previous research focused on testing LLMs' ability to follow rules like "If A, then B." But SATBench throws them into a more complex scenario where they have to search for a solution that fits all the conditions. It's like searching for the right key to unlock a door, rather than just knowing what happens after you open the door.
The researchers used LLMs themselves to generate these puzzles! They started with a basic SAT problem and then had the LLM turn it into a story with specific conditions. They even made sure the difficulty was adjustable by changing the number of conditions. Think of it like setting the difficulty on a video game – more conditions, harder puzzle!
To make sure the puzzles were fair, the researchers did two things: First, they had LLMs check the puzzles. Second, they used special solver programs to make sure that the puzzles were logically sound. Finally, humans validated a subset of the puzzles. This is an important step because it ensures that the puzzles are solvable and that the LLMs are not just making up answers.
So, what did they find? Even the most powerful LLMs struggled! On the hardest puzzles, they were barely better than random guessing, achieving only 65% accuracy. This suggests that current LLMs have serious limitations when it comes to this kind of search-based logical reasoning. It's like they can memorize the recipe, but they can't figure out how to bake the cake if you change the ingredients slightly.
Why does this matter? Well, for those of us interested in the future of AI, it highlights areas where we need to improve. For developers building AI-powered tools, it's a reminder that these models aren't perfect and that we need to be careful about relying on them for complex logical tasks. And for everyone else, it's just fascinating to see the boundaries of what these powerful technologies can and can't do.
This research matters because it gives us a way to measure the logical reasoning abilities of LLMs. It's also scalable, which means that we can create new puzzles easily. This will allow researchers to continue to test and improve the logical reasoning abilities of LLMs in the future.
As the researchers said:
SATBench exposes fundamental limitations in the search-based logical reasoning abilities of current LLMs and provides a scalable testbed for future research in logical reasoning.
Here are a few things that I'm pondering as I reflect on this research:
Given that LLMs are so good at pattern recognition, why do they struggle so much with the search-based logic of SAT problems? Is it a fundamental limitation of their architecture?
Could we use SATBench to train LLMs to be better logical reasoners? What kind of training data or techniques might be most effective?
If LLMs struggle with SAT problems, what other types of complex reasoning tasks might they also find challenging, and how could we design benchmarks to test those abilities?
That's all for today's deep dive, learning crew! I hope this has given you a new perspective on the capabilities and limitations of large language models. Until next time, keep those gears turning!Credit to Paper authors: Anjiang Wei, Yuheng Wu, Yingjia Wan, Tarun Suresh, Huanmi Tan, Zhanke Zhou, Sanmi Koyejo, Ke Wang, Alex Aiken

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool AI research! Today, we're tackling a paper that asks a really important question: how do we keep an eye on AI when it starts getting smarter than we are? Think of it like this: imagine you're teaching a kid to ride a bike, but then suddenly, they're doing wheelies and jumps you can't even dream of. How do you even know if they're doing it safely?
Well, this paper explores a fascinating solution inspired by… you guessed it… debate! But not just any debate. We're talking AI vs. AI in a battle of wits!
So, the researchers focused on a specific AI task called Visual Question Answering, or VQA. Imagine showing an AI a picture – say, a photo of a crowded beach. And then you ask it, "How many people are wearing hats?" The AI has to "see" the image and "understand" the question to give you the right answer.
Now, these researchers set up a system where two AI models, both pretty good at VQA, debate the answer to these questions. Think of them as two expert witnesses, each with their own opinion.
Here's where it gets really clever. Instead of forcing the AI to pretend to disagree (which can be tricky), they only debate when they actually disagree! This keeps the debate focused on the real sticking points.
But who decides who wins the debate? This is where a third AI comes in, a "blind" judge. This judge can't see the image. All it gets is the arguments made by the two debating AIs. It's like a legal case where the judge only hears the spoken evidence, not seeing any physical evidence.
"Judgments from weaker LLMs can help instill reasoning capabilities in vision-language models through finetuning."
So, what did they find? The results were pretty impressive! The researchers discovered that this debate framework consistently produced better answers than either of the individual AI experts could on their own. It's like having two chefs collaborate on a dish – you often end up with something even more delicious!
But the real kicker is this: they also found that the "blind" judge didn't have to be super-smart. Even a weaker AI judge could help the VQA models improve through a process called finetuning. This means that even less powerful AI can help train and improve the reasoning skills of the more powerful, "sighted" AI models.
Why is this important? Well, as AI gets more powerful, we need ways to ensure it's making good decisions, even when those decisions are complex and hard for humans to understand. This research suggests that AI debate could be a powerful tool for overseeing and improving these advanced AI systems. It has implications for:
AI Safety Researchers: This provides a tangible method for scalable oversight.
AI Developers: This offers a method for improving model performance without requiring vast amounts of human-labeled data.
Anyone Concerned About AI: This shows the potential for AI to self-regulate and improve its reasoning.
This research really makes you think! A couple of questions popped into my head:
Could this debate framework be applied to other complex AI tasks, like medical diagnosis or financial modeling?
What are the ethical considerations of using AI to judge AI? How do we prevent bias from creeping into the judging process?
Food for thought, right? That's all for this episode of PaperLedge! Keep learning, keep questioning, and I'll catch you next time!Credit to Paper authors: Ashutosh Adhikari, Mirella Lapata

Hey PaperLedge crew, Ernis here, ready to dive into some brain-tickling research! Today, we're tackling a paper about making AI models smarter and faster. Think of it like this: imagine you're solving a math problem. Sometimes, it's a quick calculation you can do in your head. Other times, you need to pull out a pen and paper and really work through the steps. That's kind of what this paper is all about – teaching AI to figure out when it needs to "think hard" and when it can just give you the answer straight away.
So, these researchers noticed that the really smart AI models, the ones that can reason and solve complex problems, often take a long time to answer even simple questions. It's like they're overthinking everything! This uses up a lot of computing power and makes them slower, which isn't ideal.
Their solution? They created something called Large Hybrid-Reasoning Models (LHRMs). The key word here is "hybrid." These models can decide, on the fly, whether a question needs deep, step-by-step reasoning or if it's something they can answer quickly without all the extra processing.
Think of it like a chef. A simple salad? They can whip that up in minutes. A complicated soufflé? That requires careful planning, precise measurements, and a whole lot more time. The LHRM is like a chef who knows when to make a salad and when to bake a soufflé.
Now, how did they teach the AI to do this? They used a two-step training process:
Hybrid Fine-Tuning (HFT): This is like giving the AI a basic understanding of different problem types and when to use different "thinking strategies." It's a cold start, giving the model some initial guidance.
Hybrid Group Policy Optimization (HGPO): This is where things get really interesting. They use a technique called reinforcement learning, which is like training a dog with treats. The AI gets "rewards" for choosing the right thinking strategy for the right problem. Over time, it learns to pick the most efficient method.
To see how well their AI was learning, they invented a new way to measure its performance, called Hybrid Accuracy. This tells them how good the model is at picking the right "thinking mode" for each question.
The results were pretty impressive! The LHRMs were not only faster than previous models on easy questions, but they were also just as good, or even better, at answering the really tough ones. They were able to adapt their approach based on the question, making them more efficient overall.
"Together, our work advocates for a reconsideration of the appropriate use of extended thinking processes and provides a solid starting point for building hybrid thinking systems."
So, why does this matter?
For AI developers: This shows a promising new way to build more efficient and adaptable AI systems. It's not just about making them smarter; it's about making them smarter and faster.
For businesses: Faster AI means faster answers, quicker decisions, and potentially lower costs. Imagine customer service bots that can instantly answer simple questions but can also handle more complex issues when needed.
For everyone: More efficient AI can lead to breakthroughs in all sorts of fields, from medicine to engineering. It can help us solve complex problems more quickly and efficiently, improving our lives in countless ways.
This research challenges the assumption that more "thinking" always equals better results. It suggests that the best AI systems are those that can adapt their approach based on the situation.
Here are a couple of questions that popped into my head:
Could this hybrid approach be applied to other areas of AI, like image recognition or natural language understanding?
What are the ethical implications of AI systems that can make decisions about when to "think hard" and when to take shortcuts? Could this lead to biases or unintended consequences?
That's all for this week's episode. I hope you found this deep dive into Large Hybrid-Reasoning Models as fascinating as I did. Keep learning, keep questioning, and I'll catch you next time on PaperLedge!Credit to Paper authors: Lingjie Jiang, Xun Wu, Shaohan Huang, Qingxiu Dong, Zewen Chi, Li Dong, Xingxing Zhang, Tengchao Lv, Lei Cui, Furu Wei