Alright Learning Crew, Ernis here, ready to dive into another fascinating paper! Today, we're tackling a topic in the wild world of computer vision, specifically how we teach computers to "see" images like we do. Get ready, because we're going to explore a new way to help these systems understand where things are in a picture!
So, you've probably heard of Transformers, right? They're all the rage in AI, powering things like ChatGPT. Well, they're also making waves in image recognition. These Vision Transformers, or ViTs, are super powerful at identifying what's in a picture. But here's the thing: they have a bit of a quirky way of processing images.
Imagine you have a puzzle, and instead of looking at the whole picture, you chop it up into little squares or "patches". That's what ViTs do! Then, they flatten each patch into a long line of information. The problem is, by doing this, they lose some of the original sense of where each patch was located relative to the others. It’s like taking apart your LEGO castle and then trying to rebuild it without knowing which bricks were next to each other!
To help the computer remember the location of these patches, researchers use something called "positional encoding." It’s like adding a little note to each patch saying, "Hey, I was in the top-left corner!" But the traditional ways of doing this aren’t perfect. They don't always capture the natural geometric relationships, how close things are to each other, that we intuitively understand when looking at a picture. It’s like trying to describe a map using only street names, but without any distances or directions.
Now, this is where the cool stuff comes in. This paper introduces a brand-new way to handle positional encoding, and it's based on some seriously fancy math called Weierstrass Elliptic Functions. Don't worry, we're not going to get bogged down in the equations! Think of it this way: these functions are like special maps that naturally capture the repeating patterns and relationships we often see in images.
Imagine a tiled floor. The pattern repeats over and over. Elliptic functions are naturally suited to describe that kind of translational invariance - the idea that moving something slightly doesn't fundamentally change what it is. The researchers cleverly use these functions to tell the computer how far apart different patches are in a picture, and how they relate to each other. It's like giving the LEGO bricks a built-in GPS so the computer always knows where they belong! The fancy name for this technique is WEF-PE, short for Weierstrass Elliptic Function Positional Encoding.
"Our method exploits the non-linear geometric nature of elliptic functions to encode spatial distance relationships naturally..."
The real breakthrough here is that WEF-PE helps the computer understand the image in a more natural way. It’s not just about memorizing locations, but about understanding the spatial relationships between different parts of the image. This has some important implications!
So, what did the researchers find? Well, they put WEF-PE to the test on a bunch of different image recognition tasks, and it consistently outperformed the traditional methods. For example, they trained a ViT-Tiny architecture from scratch on the CIFAR-100 dataset, and achieved 63.78% accuracy. They got even better results, 93.28%, when fine-tuning a ViT-Base model on the same dataset! They also showed consistent improvements on the VTAB-1k benchmark which is a set of diverse vision tasks.
But it's not just about better numbers! The researchers also showed that WEF-PE helps the computer focus on the right parts of the image. Imagine you're looking at a picture of a cat. You instinctively know that the cat's eyes and nose are important. WEF-PE helps the computer do the same thing, focusing on the key features that define the object. This is known as geometric inductive bias - the model is encouraged to learn the geometric relationships in the image, leading to more coherent semantic focus.
Okay, so why does this matter to you, the listener?
For the AI enthusiast: This is a fascinating new approach to positional encoding that could lead to more efficient and accurate image recognition systems.
For the developer: The code is available on GitHub, so you can experiment with WEF-PE yourself and see how it improves your own projects!
For everyone else: This research is a step towards building AI systems that understand the world more like we do, which could have a wide range of applications, from self-driving cars to medical diagnosis.

So, after geeking out on this paper, a few things popped into my head that might be worth discussing:
Could WEF-PE be applied to other types of data, like video or 3D models?
What are the limitations of WEF-PE? Are there specific types of images or tasks where it might not perform as well?
How can we make these complex mathematical concepts even more accessible to a wider audience so more people can contribute to the conversation?
That's all for this episode, Learning Crew! Until next time, keep exploring and keep questioning!Credit to Paper authors: Zhihang Xin, Xitong Hu, Rui Wang

Hey PaperLedge learning crew, Ernis here, ready to dive into some fascinating research! Today, we're tackling a paper about keeping our voice-activated security systems safe from sneaky attacks. Think about it: your smart home, your bank account accessed with your voice – we want to make sure only you get in, right?
The paper focuses on speaker verification, which is just a fancy way of saying "technology that confirms it's really you speaking." But here's the problem: these systems, while cool, are vulnerable. Someone could use a manipulated recording or even a cleverly disguised voice to trick the system. It's like a digital con artist!
So, how do we protect ourselves? That's where the "Mask Diffusion Detector," or MDD, comes in. Think of MDD as a super-smart bouncer for your voice-activated systems. It's designed to spot and neutralize these adversarial "attacks" – those manipulated voice samples.
Now, here's where it gets interesting. The researchers used something called a diffusion model. Imagine taking a pristine photograph and slowly covering parts of it with a blurry mask, adding more and more noise until it's almost unrecognizable. That's the "forward diffusion" process. MDD does something similar to speech, masking out portions of a voice recording's Mel-spectrogram - which, in simple terms, is a visual representation of the audio - and adding noise.
But then, the magic happens! MDD uses the text of what was said – the actual words spoken – to reverse the process. It's like having a detective who knows the content of the message and can use that knowledge to unmask the distorted voice and clean it up. This "reverse process" aims to reconstruct the original, clean voice, filtering out the malicious manipulations.
"Unlike prior approaches, MDD does not require adversarial examples or large-scale pretraining."
That's a key point! Previous defenses often needed to be trained on examples of attacks to learn how to spot them. MDD doesn't! It's like learning to recognize a fake ID not by seeing every possible fake, but by understanding what a real ID should look like.
The results? Pretty impressive! The MDD not only detected the adversarial attacks effectively, outperforming other state-of-the-art methods, but it also managed to purify the manipulated speech. It's like taking a distorted image and restoring it close to its original clarity. This meant the speaker verification system could still accurately recognize the speaker, even after someone had tried to trick it.
Why does this matter? Well:

For developers of voice-activated systems, it offers a powerful tool to build more secure and reliable products.


For businesses using voice authentication, it provides peace of mind knowing their systems are better protected against fraud.


And for us, the everyday users, it means our voice-activated gadgets and services are less vulnerable to attack, keeping our data and accounts safer.

So, wrapping up, this research shows that using diffusion-based masking is a promising approach for building more robust and secure speaker verification systems.
Now, some questions that pop into my head:
How well does MDD work against completely new types of voice manipulation attacks that it hasn't "seen" before?
Could this technology be adapted to protect other types of biometric authentication, like facial recognition?
What do you think, learning crew? Let me know your thoughts in the comments! Until next time, keep learning!Credit to Paper authors: Yibo Bai, Sizhou Chen, Michele Panariello, Xiao-Lei Zhang, Massimiliano Todisco, Nicholas Evans

Hey PaperLedge crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're tackling a paper that explores why giving AI tools, like a Python code interpreter, makes them so much smarter. Think of it like this: a regular LLM, a large language model, is like a really smart person who can only think in words. But a tool-integrated LLM? That's like giving that person a calculator, a library, and the internet!
This paper asks the fundamental question: why does this tool integration work so well? We've seen LLMs using tools like Python interpreters to solve problems, but until now, we haven't had a solid theoretical understanding of why it's such a game-changer.
The researchers behind this paper actually proved, mathematically, that tools fundamentally expand what an LLM can do. They showed that tools allow the model to tackle problems it simply couldn't solve before, like breaking through a ceiling of ability! It's like the difference between trying to build a house with just your bare hands versus having access to power tools and blueprints. The tools unlock problem-solving strategies that were either impossible or would take forever with just text alone.
Now, just giving an AI a tool isn't enough. You need to teach it how to use it effectively. That's where something called "Advantage Shaping Policy Optimization," or ASPO, comes in. Think of ASPO as a super-smart tutor. It's an algorithm that subtly guides the AI's learning process by directly tweaking how it evaluates its own actions. It nudges the model towards better tool usage without messing up its overall ability to learn. It's like gently guiding someone's hand while they're learning to write, rather than grabbing the pen and doing it for them.
"Overall, our work provides the first principled explanation for TIR's success, shifting the focus from the mere fact that tools work to why and how they enable more powerful reasoning."
To test their ideas, the researchers put their tool-integrated LLM through a series of tough math problems, using a Python interpreter as its tool. And guess what? The tool-integrated model crushed its pure-text counterpart. It wasn't just better at computationally heavy problems; it also excelled at problems requiring abstract thought and insight!
The researchers even observed how the model learned to "think" with the tool. They noticed that it started using the tool earlier in the problem-solving process and interacted with it more frequently. It's almost like the AI realized the power of the tool and started incorporating it into its thinking process from the get-go.
So, why should you care about this research? Well...
For AI developers: This gives us a better understanding of how to build more capable and efficient AI systems. It's not just about adding tools; it's about understanding why and how they work, so we can use them more effectively.
For educators: It highlights the importance of teaching problem-solving skills alongside knowledge. Just like an LLM, students need the right tools and the ability to use them effectively.
For everyone: It shows the potential of AI to augment human intelligence. By giving AI the right tools, we can unlock new levels of problem-solving and innovation.
This research essentially provides a blueprint for building smarter AI by understanding the fundamental principles behind tool integration. It's a big step towards creating AI that can truly augment our own abilities.
So, here are a couple of things I'm pondering:
How can we ensure that AI systems use tools ethically and responsibly? If we're giving them more power, we need to be careful about how that power is wielded.
What are the limits of tool-integrated reasoning? Will there be certain types of problems that even the most advanced AI can't solve with tools?
Let me know what you think, PaperLedge crew! I'm excited to hear your thoughts on this groundbreaking research.Credit to Paper authors: Heng Lin, Zhongwen Xu

Hey PaperLedge crew, Ernis here, ready to dive into some brain-tickling research! Today, we're tackling a paper that's all about how well AI, specifically those fancy Large Language Models, or LLMs, can actually think like a scientist.
Now, we all know LLMs are great at spitting out text and answering questions, but scientific problem-solving is a whole different ballgame. It's not just about knowing facts; it's about connecting those facts, using logic, and figuring out something new. Think of it like this: an LLM might know all the ingredients for a cake, but can it actually bake one, troubleshoot when it's not rising, and invent a new frosting flavor? That's the kind of reasoning we're talking about.
The researchers behind this paper noticed a problem: we don't really have a standardized way to test how good LLMs really are at scientific reasoning. So, they put together a suite of benchmarks, like a series of challenges, to see how these AI models perform. They called it SciReas, and a tougher version, SciReas-Pro.
Think of these benchmarks like different events in a science decathlon. One event might test their knowledge of chemistry, another their ability to solve physics problems, and another their understanding of biology. By looking at how LLMs do across all these different events, we get a much better picture of their overall scientific reasoning abilities.
But here's where it gets really interesting. The researchers didn't just want to know if LLMs were good at scientific reasoning; they wanted to know why they were good or bad. So, they created a framework called KRUX to figure out if the models were struggling because they lacked the necessary knowledge or because they couldn't reason properly, or both!
It's like trying to figure out why someone can't solve a math problem. Is it because they don't know the formulas (lack of knowledge), or because they can't apply those formulas correctly (poor reasoning)?
And what did they find? Well, a few key things:

Finding the right information in the LLM's brain is tough: It turns out that a big problem for LLMs is actually retrieving the relevant knowledge they already have stored inside. It's like having a library in your head but not being able to find the right book when you need it!



External knowledge helps a ton: When you give the LLM extra information related to the task, it performs much better. It's like giving that struggling student a cheat sheet of formulas – it helps them connect the dots.



Reasoning can unlock hidden knowledge: Guiding the LLM through the problem-solving process step-by-step actually helps it access more of the knowledge it already possesses. It's like coaching someone to think through a problem, which helps them remember things they already knew.



To top it off, they even created a new and improved LLM specifically for scientific tasks, called SciLit01. It's like they built a super-athlete specifically for the science decathlon!
"Retrieving task-relevant knowledge from model parameters is a critical bottleneck for LLMs in scientific reasoning."
So, why does all this matter? Well, for a bunch of reasons:

For scientists: This research could help us build AI tools that can actually assist in scientific discovery, helping us solve problems faster and more effectively.


For AI developers: It gives us a better understanding of what's holding LLMs back and how to improve their ability to reason scientifically.

For everyone else: It sheds light on the potential (and limitations) of AI in tackling complex problems, helping us have more informed conversations about the future of AI.

This research is a really good start to understand how reasoning can be improved in science, and where the major bottlenecks are.
Now, before we wrap up, a couple of questions that popped into my head:

If LLMs struggle to retrieve knowledge they already have, how can we design better "memory systems" for them? Maybe we need a better "library catalog" for their brains?


Could this framework be adapted to evaluate reasoning in other complex domains, like medicine or law?

That's all for today, PaperLedge crew! I hope you found this dive into scientific reasoning with LLMs as fascinating as I did. Until next time, keep those neurons firing!Credit to Paper authors: Alan Li, Yixin Liu, Arpan Sarkar, Doug Downey, Arman Cohan

Hey PaperLedge crew, Ernis here! Get ready to dive into some seriously cool research. Today, we're tackling a paper that's all about making AI smarter... and making sure it shows its work! Think of it like this: imagine you're teaching a student a complex math problem. You don't just want the right answer; you want to see their steps, right? You want to know how they got there.
That's essentially what this paper is trying to achieve with AI. As AI models get more sophisticated and start tackling really tricky problems – like, say, diagnosing a rare disease or figuring out the best route for a delivery truck with a million stops – they often use what we call multi-step reasoning. They break the problem down into smaller, more manageable chunks.
Now, here's the challenge: how do we ensure that each of those little steps makes sense? How do we know the AI isn't just randomly guessing its way to the right answer (or, even worse, confidently guessing the wrong one)? That's where process reward models come in. These models try to give feedback at each step of the way.
But, according to this paper, current process reward models have some limitations. The big ones are:

They often act like simple classifiers, just saying "right" or "wrong" without explaining why. It's like getting a grade on a test without any feedback. Super frustrating, right?


They're usually trained on static datasets, which limits how well they can generalize to new, unseen situations. Think of it as only learning math from one textbook – you might struggle when you encounter a problem phrased differently.

So, what's the solution? The researchers behind this paper came up with something called StepWiser. And it's a game changer!
Instead of just classifying each step as right or wrong, StepWiser actually reasons about the AI's reasoning. It's like a meta-reasoner! It outputs “thinking tokens” – basically, it explains its judgment before giving a final verdict. Think of it like this: imagine a detective (StepWiser) watching another detective (the AI) solve a case. StepWiser isn't just saying "good job" or "you're wrong." It's saying, "Okay, I see why you looked at the fingerprints there, but did you consider the alibi?"
Here's the key part: StepWiser is trained using reinforcement learning. This means it learns by trial and error, gradually improving its judgment based on the outcomes of different AI reasoning paths. It's constantly refining its understanding of what good reasoning looks like.
The paper shows that StepWiser:

Is better at judging the accuracy of intermediate steps compared to existing methods.


Can be used to improve the AI model's reasoning skills during training.


Helps the AI model explore better solutions during the problem-solving process (inference).

So, why should you care about this research? Well, if you're an AI researcher, it offers a promising new approach to building more reliable and transparent AI systems. If you're a developer, it provides a tool for debugging and improving the reasoning capabilities of your AI applications. And if you're just someone who's curious about the future of AI, it gives you a glimpse into how we can make AI not just smarter, but also more understandable and trustworthy.
Here are a couple of things that popped into my head while reading this:

Could StepWiser be adapted to help humans improve their reasoning skills? Imagine using it to get feedback on your problem-solving approach in a business negotiation or even a personal argument!


What are the ethical implications of having an AI judge another AI's reasoning? Could this lead to biases or unintended consequences?

Food for thought, right? That's all for today's deep dive. Keep learning, keep questioning, and I'll catch you in the next PaperLedge episode!Credit to Paper authors: Wei Xiong, Wenting Zhao, Weizhe Yuan, Olga Golovneva, Tong Zhang, Jason Weston, Sainbayar Sukhbaatar

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool research! Today, we're tackling a paper that's all about making computers better at understanding those messy, real-world tables we see everywhere.
Think about it: financial reports, medical records, even your online shopping history – a lot of this stuff lives in tables. But these aren't your neat, organized spreadsheets. They're often semi-structured. Meaning they have funky layouts, like headings that span multiple columns or cells that are merged together. They're a bit of a wild west!
Right now, humans are the ones who have to wade through these tables and answer questions about them. It's time-consuming and, frankly, a bit of a pain. So, the researchers behind this paper asked: can we automate this?
Now, previous attempts to get computers to understand these tables have hit some snags. Some methods try to force these messy tables into a rigid structure, which ends up losing important information – kind of like trying to cram a square peg into a round hole. Other methods, using fancy AI models, struggle with the complex layouts and often get confused, leading to inaccurate answers.
This is where ST-Raptor comes in! Think of ST-Raptor as a super-smart librarian who's really good at navigating complex organizational systems. It's a framework that uses Large Language Models (LLMs) – those are the same AI models that power things like ChatGPT – to answer questions about semi-structured tables.
So, how does it work? Well, ST-Raptor has a few key components:

The HO-Tree: This is the secret sauce! The researchers created a Hierarchical Orthogonal Tree, or HO-Tree, to represent the structure of the table. Imagine a family tree, but instead of people, it's showing how all the different parts of the table are related. This tree captures all the complexities of the table's layout.


Tree Operations: They defined a set of basic actions the LLM can take on this tree. These are like instructions for the librarian – “Find the cell in this row and column,” or “Go up to the parent node.”


Decomposition and Alignment: When you ask ST-Raptor a question, it breaks it down into smaller, simpler questions. Then, it figures out which tree operations are needed to answer each sub-question and applies them to the HO-Tree.


Two-Stage Verification: This is where things get really clever. ST-Raptor doesn't just blindly trust its answers. It uses a two-step process to make sure it's correct. First, it checks each step of its reasoning to make sure it's making sense. Then, it takes the answer it came up with and tries to reconstruct the original question. If it can't, it knows something went wrong!

Think of it like baking a cake. The HO-Tree is the recipe. The tree operations are the individual steps in the recipe. And the verification process is like tasting the cake to make sure you followed the recipe correctly!
To test ST-Raptor, the researchers created a new dataset called SSTQA, which includes 764 questions about 102 real-world semi-structured tables. The results were impressive! ST-Raptor outperformed other methods by up to 20% in answer accuracy.
"Experiments show that ST-Raptor outperforms nine baselines by up to 20% in answer accuracy."
That's a significant improvement, showing that this tree-based approach is a powerful way to unlock the information hidden in these messy tables.
So, why does this matter? Well, for data scientists, it means a more efficient way to extract insights from real-world data. For businesses, it could lead to better decision-making based on accurate analysis of financial reports and other important documents. And for everyone, it means a future where computers are better at understanding the world around us.
Now, I'm curious to hear your thoughts! Here are a couple of questions to ponder:

Could ST-Raptor be adapted to understand other types of unstructured data, like images or videos?


What are the ethical implications of using AI to analyze sensitive data like medical records, and how can we ensure responsible use?

That's all for today's deep dive into the world of semi-structured table question answering! Until next time, keep learning, keep questioning, and keep exploring the fascinating world of research. Catch you on the PaperLedge!Credit to Paper authors: Zirui Tang, Boyu Niu, Xuanhe Zhou, Boxiu Li, Wei Zhou, Jiannan Wang, Guoliang Li, Xinyi Zhang, Fan Wu

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool robotics research! Today, we're talking about how to teach robots to see the world and figure out where they can and can't go. Think of it like this: you can easily tell the difference between a sidewalk and a muddy puddle, right? But for a robot, that's a really tricky problem.
This paper tackles that challenge by helping robots understand traversability - basically, whether a surface is safe and suitable for them to roll or walk on. Why is this important? Well, imagine self-driving cars getting stuck in construction zones, or delivery robots face-planting in a pile of leaves. Not ideal!
So, what's the big idea here? Traditionally, researchers have struggled to train robots to recognize non-traversable areas – like those muddy puddles we mentioned. Plus, they've often relied on just one sense, like a camera, to make these decisions. This paper argues that's not enough. Just like we use both our eyes and our feet to judge a surface, robots need multiple senses to be truly reliable.
The researchers came up with a clever multimodal approach. Think of it as giving the robot multiple superpowers!
First, they created a system to automatically label different terrains using a combination of data: where the robot's "feet" have been, LiDAR (that's like radar but with lasers), and camera images. It's like teaching the robot what "safe" and "unsafe" look like.
Then, they trained a dual-stream network - essentially two brains working together - to learn from these labels using different types of information. One brain focuses on camera images, and the other focuses on LiDAR data.
Finally, to make sure the robot doesn't get confused by the automatic labels (which aren't perfect), they added a little bit of "ground truth" information from the LiDAR.

“The proposed automatic labeling method consistently achieves around 88% IoU across diverse datasets…our multimodal traversability estimation network yields consistently higher IoU, improving by 1.6-3.5% on all evaluated datasets.”

So, what's the result? The researchers tested their system in all sorts of environments: cities, off-road trails, and even a college campus. And guess what? It worked really well! Their robot was significantly better at identifying safe and unsafe paths compared to other methods. They saw improvements between 1.6%-3.5%. That might not sound like a lot, but in the world of robotics, even small improvements can make a huge difference in safety and reliability.
The beauty of this approach is that it doesn't require humans to manually label tons of data. The robot can learn on its own, making it much more scalable and adaptable to new environments.
Why should you care?
For robotics enthusiasts: This research offers a powerful new way to improve robot navigation, opening up possibilities for more autonomous and reliable robots.
For self-driving car developers: Better traversability estimation means safer and more efficient autonomous vehicles.
For anyone interested in AI: This paper highlights the power of multimodal learning and self-supervision, two key trends in modern AI research.
This study also raises some interesting questions. For example:
Could we incorporate even more senses, like sound or touch, to further improve traversability estimation?
How can we ensure that these robots are making ethical decisions about which paths to take, especially in complex or crowded environments?
What are the limitations of relying on self-supervised learning? How can we ensure the robot is learning the "right" things?
That's it for this episode of PaperLedge! I hope you found this deep dive into traversability estimation as fascinating as I did. Until next time, keep learning!Credit to Paper authors: Zipeng Fang, Yanbo Wang, Lei Zhao, Weidong Chen

Hey PaperLedge crew, Ernis here! Get ready to dive into some seriously cool research that tackles a problem we all face: how do we know if our predictions are actually useful?
Think about it this way: imagine you're building a weather app. You might have the fanciest algorithm predicting rainfall with 99% accuracy. Sounds great, right? But what if that 1% error always happens during rush hour, causing chaos for commuters? Suddenly, that amazing prediction isn't so amazing anymore!
This paper zeroes in on this exact issue. The researchers argue that just focusing on how accurate a prediction seems (using standard metrics) often misses the bigger picture: how well does it perform in the real world when it's actually used?
The core problem they address is this "evaluation alignment problem." Current methods either rely on a bunch of different metrics for each specific task (which is a total headache to analyze), or they try to assign a cost to every mistake (which requires knowing the cost beforehand – good luck with that!).
"Metrics based solely on predictive performance often diverge from measures of real-world downstream impact."
So, what's their solution? They've developed a clever, data-driven approach to learn a new way to evaluate predictions, a "proxy" evaluation function, that's actually aligned with the real-world outcome.
They build upon a concept called "proper scoring rules." Imagine a game where you have to guess the probability of something happening. A proper scoring rule rewards you for being honest and accurate with your probability estimate. The researchers found ways to tweak these scoring rules to make them even better at reflecting real-world usefulness.
The key is using a neural network to weight different parts of the scoring rule. Think of it like adjusting the importance of different factors when judging a prediction. This weighting is learned from data, specifically, how the prediction performs in the downstream task – that is, the real-world application.
For example: Let's go back to our weather app. Their method might learn to heavily penalize errors made during rush hour, even if the overall accuracy is high. This forces the prediction model to focus on being accurate when it really matters.
The beauty of this approach is that it's fast, scalable, and works even when you don't know the exact costs of making a mistake. They tested it out on both simulated data and real-world regression tasks, and the results are promising – it helps bridge the gap between theoretical accuracy and practical utility.
Why does this matter for data scientists? It offers a new way to evaluate models that's more aligned with business goals.
Why does this matter for product managers? It helps ensure that predictions actually lead to better user experiences and outcomes.
Why does this matter for everyone else? It means that AI systems can be better designed to serve our needs in the real world.
So, here are a couple of things I'm thinking about:
How easy is it to implement this in practice? Do you need a ton of data about the downstream task?
Could this approach be used to identify biases in our evaluation metrics, biases that might be leading us to build models that aren't fair or equitable?
Alright PaperLedge crew, that's the gist of it! Let me know what you think. What other real-world scenarios could benefit from this kind of "downstream-aware" evaluation? Until next time, keep learning!Credit to Paper authors: Novin Shahroudi, Viacheslav Komisarenko, Meelis Kull