Alright learning crew, buckle up! Today, we're diving into some seriously cool research about bringing 3D characters to life with way less effort. We're talking about a new framework called AnimaX, and it's shaking up the world of 3D animation.
Now, imagine you want to make a 3D character dance, fight, or even just walk realistically. Traditionally, that's hard. You either have to stick to pre-made skeletons, or you get stuck tweaking a million tiny settings. It’s like trying to build a Lego castle with only the tiniest bricks – super tedious!
But what if you could somehow teach a computer to understand movement by showing it videos? That's the core idea behind AnimaX. The researchers have essentially found a way to take the knowledge embedded in video diffusion models - think AI that can generate realistic videos - and apply it to 3D animation.
Here's the clever bit: AnimaX doesn't directly manipulate the 3D mesh. Instead, it represents the motion as a series of 2D poses from multiple camera angles, across multiple frames. Think of it like having several cameras filming a person dancing, and the AI is learning to predict where the joints (elbows, knees, etc.) should be in each of those camera views at every moment in time.
Then, it uses some mathematical wizardry called "triangulation" to combine those 2D poses into a 3D skeleton. Finally, it uses "inverse kinematics" to make the character's body follow that skeleton. It's like puppeteering, but with AI!
To make this work, they've used some fancy tech like:
Shared Positional Encodings: This helps the system understand where things are in space and time, both in the videos and in the 3D animation. It's like giving the AI a common language to describe positions.
Modality-Aware Embeddings: This helps the system understand the difference between video data and pose data. Think of it as teaching the AI to distinguish between seeing a dance and knowing how to dance.
The beauty of AnimaX is that it's category-agnostic. It doesn't care if you're animating a human, a dog, or a completely made-up creature. As long as you have a 3D model with a skeleton, AnimaX can bring it to life.
And they trained it on a massive dataset: 160,000 rigged sequences! That's like showing it a lifetime of dance lessons.
The result? AnimaX is fast and creates realistic motions. It's like going from building that Lego castle one tiny brick at a time to using pre-built sections - much faster and the end result is way more impressive.
Why does this matter?
For game developers: Imagine being able to quickly generate realistic character animations without spending hours on motion capture or manual tweaking.
For filmmakers: Think about the possibilities for creating realistic CGI characters with less time and resources.
For anyone creating content: This could democratize animation, making it easier for anyone to create 3D content.
So, here are a couple of questions I'm pondering:
How far away are we from being able to just type in a sentence like "a dragon gracefully lands on a mountain peak" and have AnimaX generate the entire animation?
What ethical considerations do we need to think about as AI-powered animation becomes more powerful and accessible? Could this lead to a decrease in jobs for animators, or will it simply augment their abilities?
What do you think, learning crew? Let's discuss!Credit to Paper authors: Zehuan Huang, Haoran Feng, Yangtian Sun, Yuanchen Guo, Yanpei Cao, Lu Sheng

Alright learning crew, Ernis here, ready to dive into another fascinating paper from the cutting edge! Today we’re tackling something that’s super relevant to anyone excited about AI-generated videos: making it faster, cheaper, and able to create much longer clips. Think of it as giving AI video artists a serious upgrade without breaking the bank.
So, the paper basically addresses a bottleneck in how AI creates videos. You know how these AI models, called “diffusion models,” are getting incredibly good at generating realistic video? The problem is, the longer the video, the more computing power it demands. It's like trying to paint a mural versus a small canvas – the mural requires way more paint and effort.
The researchers identified this phenomenon they call Spatiotemporal Energy Decay. Sounds complicated, right? But it's actually quite intuitive. Imagine tossing a pebble into a pond. The ripples are strongest near where the pebble landed, and they fade away as they spread further out in space and time. It’s the same with the AI's “attention” when creating a video. The AI needs to pay attention to different parts of the video to make sure it all makes sense. But the further apart two moments are in the video, the less directly relevant they usually are to each other.
"Post-softmax attention scores diminish as spatial and temporal distance between tokens increase, akin to the physical decay of signal or waves over space and time in nature."
So, the AI is wasting a lot of computing power paying close attention to things that are barely related! It's like carefully scrutinizing every single leaf on a tree when you only care about the overall shape.
Now, here's where the genius comes in. To solve this, the researchers came up with something called Radial Attention. The core idea is to focus the AI's attention where it matters most – on the parts of the video that are close together in space and time. As the video progresses, the model uses a static attention mask, only paying close attention to nearby tokens, with the attention window shrinking with time.
Think of it like this: instead of trying to look at everything in the video at once, it's like having a spotlight that focuses on specific areas. This spotlight is wider for moments that are close together in time and narrows as you look at moments further apart. The researchers use math to make this spotlight exponentially decaying! This is where that O(n log n) complexity comes in!
This Radial Attention is far more efficient than the old method, which they call "dense attention," and it’s more expressive than another method called “linear attention.”
Dense attention is like trying to process every single detail of the video simultaneously.
Linear attention is faster, but it loses some of the nuance and detail.

The truly cool part is that this new method is also more flexible. It allows pre-trained models to generate much longer videos! They can even tweak existing models that were already trained to use the old method, using something called LoRA-based fine-tuning.
So, what did they find in their experiments?
Radial Attention maintained the video quality across different models.
They saw speed increases of up to 1.9x over the old method.
They could generate videos up to four times longer.
Training costs were reduced by up to 4.4x.
Inference (generating the video) was up to 3.7x faster.

Okay, learning crew, let's think about why this research is important. For the average listener, this means:
Potentially cheaper and faster AI-generated video content. Think personalized learning videos, custom animations, or even AI-assisted filmmaking.
The possibility of longer, more complex, and more immersive AI-generated videos.
For researchers and developers, this opens up doors to:
Creating more efficient and scalable video generation models.
Exploring new applications of AI in video production and content creation.
Building AI models that can create videos that are longer and more engaging.

Here are a few thought-provoking questions that come to mind:
How might this technology be used to create interactive or personalized video experiences?
Could this lead to a future where anyone can easily create high-quality videos using AI, regardless of their technical skills or budget?
What are the potential ethical implications of having such powerful video generation tools readily available?
That's all for today, learning crew! I hope this breakdown of Radial Attention has sparked your curiosity about the exciting advancements in AI video generation. Until next time, keep learning and keep exploring!Credit to Paper authors: Xingyang Li, Muyang Li, Tianle Cai, Haocheng Xi, Shuo Yang, Yujun Lin, Lvmin Zhang, Songlin Yang, Jinbo Hu, Kelly Peng, Maneesh Agrawala, Ion Stoica, Kurt Keutzer, Song Han

Hey everyone, Ernis here, and welcome back to PaperLedge! Today, we're diving into a fascinating paper about keeping our AI helpers safe and sound, especially those big language models – you know, the ones powering chatbots and writing assistants.
These LLMs, or Large Language Models, are becoming super useful, but there's a catch. Think of it like teaching a puppy tricks. You train it to be friendly, but someone else could later teach it bad habits, right? Similarly, even after we've tried to make these AI models "safe" by aligning them with good values, they can still be tricked into saying or doing harmful things. It's like they have a secret soft spot that can be exploited.
This paper highlights a really important vulnerability: after these models have been carefully aligned to avoid harmful responses, seemingly harmless tweaks can undo all that hard work. Imagine you've carefully crafted a recipe for a delicious cake, but a tiny change in one ingredient throws off the whole thing. That’s kind of what’s happening here.
So, what’s going on under the hood? The researchers found that within the massive collection of numbers that make up an LLM (we call them parameters), there are specific, sensitive areas – what they call "safety-critical low-rank subspaces." Think of it like the supporting beams of a building. If those beams are weak, the whole structure is at risk. These subspaces are crucial for keeping the model safe, but they're surprisingly susceptible to even small changes during further training.
But here’s the exciting part: the researchers came up with a clever solution called LoX, short for Low-Rank Extrapolation. It's a training-free method, meaning it doesn't require retraining the entire model. Think of it like applying a shield to those vulnerable spots, reinforcing the model's safety without affecting its ability to learn new things. They essentially extrapolate the safety subspace, strengthening it against unwanted influences.
Their experiments showed that LoX works really well! It significantly reduced the success rate of attacks – both accidental and malicious – that tried to make the model say or do harmful things. In some cases, they saw an 11% to 54% reduction in "attack success rates," which is a massive improvement!
The researchers explain that LoX works because it moves the LLM's parameters to a "flatter zone," making them less sensitive to disruptions. Picture a ball rolling on a bumpy surface versus a flat surface. It's much harder to knock the ball off course on the flat surface, right?
Why does this matter?
For developers: This provides a practical way to build more robust and reliable AI systems.
For businesses: This reduces the risk of AI-powered services being misused or causing harm, protecting brand reputation.
For everyone: This contributes to a safer and more trustworthy AI landscape, ensuring these powerful tools are used responsibly.

This research raises some interesting questions:
How can we continuously monitor and adapt our safety measures as these models evolve?
Could techniques like LoX be combined with other safety methods for even stronger protection?
How do we ensure that these safety measures don't inadvertently stifle creativity or limit the model's ability to learn and adapt?
The code for LoX is available on GitHub (github.com/VITA-Group/LoX) if you want to dive deeper! Thanks for joining me on this journey into the world of AI safety. Until next time, keep exploring!Credit to Paper authors: Gabrel J. Perin, Runjin Chen, Xuxi Chen, Nina S. T. Hirata, Zhangyang Wang, Junyuan Hong

Hey PaperLedge crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're talking about a clever way to make your computer's storage act more like its super-fast memory. Think of it like this: imagine your computer's memory (RAM) is a chef's countertop – it's where all the active cooking happens. Your hard drive or SSD is more like the pantry – it's got everything you need, but it takes longer to grab ingredients from there. What if you could blur the lines and have a pantry shelf that's almost as fast as the countertop? That's essentially what this paper is exploring.
Specifically, these researchers are looking at a technology called Compute Express Link (CXL). CXL is like a super-fast highway inside your computer that connects different components. Right now, it's mostly used to connect CPUs and memory, but these researchers are thinking bigger.
They're asking: "Can we use CXL to connect regular storage – like your SSD – in a way that makes it act more like extra memory?" It's a big question because traditional storage is accessed in "blocks," like reading entire pages from a book. Memory, on the other hand, is accessed byte by byte, allowing for much faster and more granular access.
The key idea here is making the storage cacheable. Think of caching like pre-chopping all your vegetables before you start cooking. You're anticipating what you'll need and making it readily available. By making the SSD's data cacheable, the computer can keep frequently used data closer to the processor, significantly speeding things up. They even propose using a special kind of device they call a CXL-SSD, which is essentially an SSD designed specifically to take advantage of CXL's capabilities.
"By making the SSD's data cacheable, the computer can keep frequently used data closer to the processor, significantly speeding things up."
To make this work even better, the researchers came up with some clever tricks they call "Determinism" and "Bufferability." Imagine you're sending a package. "Determinism" is like knowing exactly when your package will arrive, so you can plan accordingly. "Bufferability" is like having a safe place for your package to sit temporarily if there's a slight delay, preventing it from getting lost. These tricks help the computer manage the data flow between the SSD and the processor more efficiently, leading to even lower latency and better performance, while also making sure your data is safe even if something goes wrong.
So, what did they find? Well, they built a prototype and ran simulations, and the results were pretty impressive. They found that their CXL-SSD approach was 10.9 times faster than using regular PCIe-based memory expansion! And with their "Determinism" and "Bufferability" tricks, they reduced latency by another 5.4 times! For certain types of tasks where the computer is constantly accessing the same data over and over – what they call "high locality" – the CXL-SSD performed almost as well as regular DRAM memory! That's a huge win!
Why does this matter? Well, for gamers, it could mean faster loading times and smoother gameplay. For scientists and engineers, it could mean running complex simulations and analyzing huge datasets much more quickly. For everyday users, it could mean a more responsive and snappier computer experience overall. It opens the door to a future where the line between memory and storage becomes increasingly blurred, giving us access to more data, faster than ever before.
This research really makes you think. What happens when storage is as fast as memory? Will we even need to distinguish between the two in the future? And if this technology becomes widespread, how will it impact the way we design and use software applications?Credit to Paper authors: Miryeong Kwon, Donghyun Gouk, Junhyeok Jang, Jinwoo Baek, Hyunwoo You, Sangyoon Ji, Hongjoo Jung, Junseok Moon, Seungkwan Kang, Seungjun Lee, Myoungsoo Jung

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're talking about Large Language Models, or LLMs – think of them as super-smart chatbots – and how we can use them to make decisions in complex situations, like playing games.
Now, LLMs have a bit of a memory problem. They don't naturally remember what happened in the past, which is kind of a big deal when you're trying to, say, play a game that unfolds over multiple rounds. Imagine playing chess, but forgetting all the moves that came before your turn! That's where this paper comes in. It's all about how to give these LLMs a "memory" using natural language, like the kind we use every day.
Think of it like this: you're telling the LLM the story of the game so far. But how do you tell that story? What details do you include? That's what this research breaks down. They've created a system for thinking about how we represent the state of the game to the LLM. And they've identified three key aspects of this state representation.
Action Informativeness: How much do we tell the LLM about the moves that have been made? Do we give it a play-by-play, or just a general summary?
Reward Informativeness: How much do we tell the LLM about the results of those moves? Do we focus on the raw points earned, or on how much the LLM regrets its choices?
Prompting Style (Natural Language Compression): Do we feed the LLM the entire transcript of the game so far, or do we summarize it down to the essentials? Think of it as the difference between giving someone a novel versus a short story.
The researchers tested their framework on a game called a "selfish routing game." Now, don't let the name scare you. It's basically a simplified version of how people choose routes to get somewhere, like driving to work. Everyone wants to take the fastest route, but if too many people choose the same route, it gets congested, and everyone ends up being late. The game has a simple solution, a sweet spot where everyone can get to work with minimal delay.
Here's the cool part: the researchers found that how they "told the story" of the game to the LLM really mattered. Some ways of representing the game's history led the LLMs to play the game in a way that matched the ideal solution, while other representations led to chaos and unpredictability.
"Representations which provide agents with (1) summarized, rather than complete, natural language representations of past history; (2) information about regrets, rather than raw payoffs; and (3) limited information about others' actions lead to behavior that more closely matches game theoretic equilibrium predictions..."
Basically, if they summarized the past, focused on regrets, and didn't overwhelm the LLM with information about what everyone else was doing, the LLM played much more effectively.
So, why does this matter? Well, imagine using LLMs to manage traffic flow in a real city. Or to negotiate deals between companies. Or even to help us make better decisions in our own lives. Understanding how to feed information to these LLMs is crucial to getting them to make good choices.
For listeners who are interested in AI, this paper highlights the importance of prompt engineering. It's not just about having a powerful model; it's about knowing how to communicate with it effectively.
For listeners who are into game theory or economics, this research shows how LLMs can be used to model and understand complex strategic interactions.
And for everyone else, this paper is a reminder that even the smartest technology needs to be guided and informed in the right way.
Here are a few things I'm wondering about:
Could this framework be applied to other types of games or decision-making scenarios? What about something more complex than selfish routing?
How do the researchers decide what to include in the "summarized" version of the game history? Is there a risk of introducing bias or overlooking important information?
What are the ethical implications of using LLMs to make decisions in situations that affect real people? How can we ensure that these systems are fair and transparent?
That's all for today, PaperLedge crew. Keep learning, and keep asking questions!Credit to Paper authors: Lyle Goodyear, Rachel Guo, Ramesh Johari

Alright learning crew, Ernis here, ready to dive into something super interesting! We're tackling a paper that's all about making AI, specifically those big language models that can reason, think a little smarter and faster. You know, the ones that can solve complex problems, almost like a human would...but sometimes, maybe a little TOO much like a human.
This paper focuses on what they call "overthinking" in these large reasoning models, or LRMs. Think of it like this: you ask your friend for directions, and instead of just telling you "go straight two blocks and turn left," they give you a five-minute explanation of the history of the street, the types of trees lining the road, and their personal experiences walking that route. Helpful? Maybe. Efficient? Definitely not!
That's what these LRMs are doing. They're generating unnecessarily verbose and redundant content – basically, they're rambling! This makes them slower and more expensive to use. And the researchers behind this paper were like, "Hold on, can't we make them a bit more concise?"
So, they dug into why these models overthink. They discovered that these models actually have the capability for more concise reasoning built in. It's like they have a super-efficient route to the answer, but they keep taking the scenic route! The research showed that there are many different ways to get to the right answer, and some are way shorter than others.
"Correct reasoning paths vary significantly in length, and the shortest correct responses often suffice..."
Think of it like finding the best path through a maze. There might be a really direct path, but sometimes the AI is wandering around in circles before finding it!
Now, here's where it gets really cool. Armed with this knowledge, they developed two lightweight methods to make these LRMs more efficient:
Efficiency Steering: Imagine having a volume knob for "reasoning efficiency." This method is kind of like that. It's a way to tweak the model's behavior in a specific direction to make it more concise, without even having to retrain the entire model. It's like giving the AI a gentle nudge in the right direction.
Self-Rewarded Efficiency RL: This one uses reinforcement learning. It's like training a dog with treats, but instead of treats, the model gets rewarded for giving concise, correct answers. It learns to balance accuracy and brevity. So, it’s not just about getting the answer right, but also about getting it right in the fewest steps possible.
They tested these methods on seven different LRM backbones across various mathematical reasoning problems. And guess what? It worked! They were able to significantly reduce the reasoning length while still maintaining or even improving the model's accuracy!
So what does this mean for us? Well, for starters, it means more efficient and cost-effective AI. Imagine using these more efficient models for things like:
Automated customer service: Getting faster and more direct answers to your questions.
Scientific research: Speeding up the process of analyzing data and drawing conclusions.
Education: Providing more concise and focused explanations of complex concepts.
But it also makes you wonder...
If these models already have the capacity for efficient reasoning, why are they overthinking in the first place? What are the underlying mechanisms that cause this inefficiency?
Could these techniques for improving efficiency be applied to other areas of AI, like image recognition or natural language understanding?
This research shows that we can make AI smarter, not just by making it bigger and more complex, but by helping it use its existing capabilities more efficiently. It's a fascinating step towards a future where AI is not only powerful but also practical and accessible. That's all for now, learning crew! Keep those gears turning!Credit to Paper authors: Weixiang Zhao, Jiahe Guo, Yang Deng, Xingyu Sui, Yulin Hu, Yanyan Zhao, Wanxiang Che, Bing Qin, Tat-Seng Chua, Ting Liu

Hey PaperLedge learning crew! Ernis here, ready to dive into some cutting-edge research. Today, we're tackling a paper about finding sneaky memory bugs in Rust code. Now, Rust is this cool programming language known for being super safe, like having a built-in bodyguard for your computer's memory. But, like any bodyguard, it's not perfect.
See, Rust has this special "unsafe" mode. It's there for when you need to do things that are a little more...risky. Think of it like letting your bodyguard take a break so you can try some extreme skateboarding. You might pull off an awesome trick, but you also might face-plant. In Rust's case, "face-planting" means introducing memory bugs that can crash your program or, even worse, let bad guys mess with your system.
The problem is, finding these bugs in "unsafe" Rust code is tricky. Existing tools are either not very good at it, struggle with Rust's unique features, or need a ton of human help – imagine needing a team of experts to watch you skateboard every second!
That's where deepSURF comes in. This paper introduces a new tool that's like a super-smart, AI-powered bug detective for Rust. It combines two powerful techniques:
Static Analysis: Think of this as the detective carefully examining the code, looking for suspicious patterns and potential problems before the code even runs. It's like checking the skateboard for cracks before you even step on it.
LLM-Guided Fuzzing: Okay, this is where it gets really cool. LLM stands for Large Language Model – basically, a powerful AI like the one that powers ChatGPT. DeepSURF uses this AI to automatically create test programs, called "fuzzing harnesses," that try to break the code in every way imaginable. It’s like having an AI that comes up with crazy skateboard stunts to see if the board will break!
One of the coolest things about deepSURF is how it handles something called "generics." Imagine you have a recipe for a cake, but it's a generic cake recipe. It can make a chocolate cake, a vanilla cake, or whatever kind of cake you want! In Rust, generics are a way to write code that can work with different types of data. DeepSURF cleverly figures out how to create specific versions of these generic recipes so it can test them thoroughly.
And the LLM part? It dynamically helps create better and better tests on the fly. The AI learns from what works and what doesn't, constantly evolving its "skateboarding stunts" to find new ways to break the code.
"deepSURF employs LLMs to augment fuzzing harnesses dynamically, facilitating exploration of complex API interactions and significantly increasing the likelihood of exposing memory safety vulnerabilities."
So, what were the results? The researchers tested deepSURF on 27 real-world Rust projects. And guess what? It not only rediscovered 20 bugs that were already known, but it also found six brand new, previously unknown memory safety vulnerabilities! That's like not only confirming that your old skateboarding tricks are dangerous, but also discovering six new ways to break your board!
Why does this matter?
For developers: DeepSURF can help you write safer, more reliable Rust code. Think of it as a safety net that catches those sneaky bugs before they cause problems for your users.
For users of Rust software: This research helps ensure that the software you rely on is more secure and less likely to crash. It's like knowing that the bridge you're driving over has been thoroughly inspected for weaknesses.
For the Rust community: This work pushes the boundaries of what's possible in automated bug finding, making Rust an even more trustworthy and robust language.
This paper is a big step forward in making Rust code even safer and more reliable.
Now, a few questions that came to mind for me are:
Could deepSURF be adapted to find other types of bugs besides memory safety issues?
How does the performance of deepSURF compare to other bug-finding tools? Is it fast enough to be used in real-world software development workflows?
That's all for this episode! Let me know what you think of deepSURF. Until next time, keep learning!Credit to Paper authors: Georgios Androutsopoulos, Antonio Bianchi

Hey PaperLedge learning crew, Ernis here, ready to dive into some cutting-edge AI research! Today, we're cracking open a paper about making AI chatbots even better at understanding what we actually want.
Now, you know how training AI is like teaching a puppy? You give it treats (rewards) when it does something right. But what if the puppy's a super-smart chatbot, and instead of treats, we give it feedback like "I prefer this response over that one"? That's called Reinforcement Learning from Human Feedback, or RLHF for short.
The problem is, current RLHF methods can be a bit... vague. It's like saying "good boy!" without explaining why it was good. This paper tackles that by introducing a new system called AutoRule.
Think of AutoRule as a super-efficient AI tutor that automatically figures out the rules behind our preferences. Instead of just saying "I like this answer," AutoRule tries to understand why we liked it. Did it use the right vocabulary? Was it factually accurate? Did it avoid being too verbose?
The magic of AutoRule happens in three steps:
First, it uses a sophisticated reasoning model to figure out why a human preferred one answer over another. Imagine it's like a detective trying to understand the clues left behind in our feedback.
Next, it identifies candidate rules from this reasoning. These are like potential reasons for our preference, like "the answer should be concise" or "the answer should be polite".
Finally, it synthesizes these candidate rules into a single, unified rule set. Think of it as writing a clear and concise set of guidelines for the chatbot to follow.
"AutoRule is like giving the chatbot a cheat sheet to understand what 'good' looks like to us."
So, how does AutoRule actually use these rules to train the AI?
Well, after figuring out the rules, AutoRule uses a language model verifier to check how well each of the chatbot's responses follows them. It's like giving the chatbot a score on how well it followed the guidelines.
This score is then used as an auxiliary reward, meaning it's added to the regular rewards the chatbot gets from human feedback. It's like giving the chatbot extra points for following the rules, in addition to the general "good boy!" reward.
The researchers tested AutoRule on a powerful chatbot model called Llama-3-8B, and the results were impressive! They saw a significant improvement in how well the chatbot performed, especially when it came to things like controlling the length of its responses and providing helpful second turns in conversations.
But why does all of this matter?
For AI researchers, this is a big step towards more efficient and reliable RLHF. It means we can train better chatbots with less human effort.
For businesses using AI chatbots, this could lead to more engaging and helpful customer service. Imagine a chatbot that truly understands your needs and responds in a way that's both accurate and satisfying.
And for everyone else, this means interacting with AI that's less frustrating and more aligned with human values. No more weird, rambling, or unhelpful chatbot responses!
The research also showed that AutoRule is less prone to reward hacking. Reward hacking is like when the puppy figures out a way to get treats without actually doing what you wanted. AutoRule helps prevent the chatbot from finding loopholes and instead focuses on genuinely improving its performance.
This research offers some interesting questions:
If AutoRule can extract rules from our preferences, could it also be used to identify biases in our feedback?
How can we ensure that the rules extracted by AutoRule are aligned with ethical principles and avoid reinforcing harmful stereotypes?
Could AutoRule be adapted to train AI in other areas, like robotics or image generation?
The researchers have even made their code publicly available, so anyone can experiment with AutoRule! You can find it on Github.
That's all for today's episode of PaperLedge. I hope you found this deep dive into AutoRule insightful. Until next time, keep learning, keep questioning, and keep pushing the boundaries of what's possible with AI!Credit to Paper authors: Tevin Wang, Chenyan Xiong