Hey PaperLedge crew, Ernis here, ready to dive into another fascinating piece of research! Today, we’re talking about a problem that's becoming increasingly relevant in the world of AI: how do we get these amazing Language Models, these digital brains, to work together better?
Think of it like this: you've got a team of experts, each brilliant in their own specific area. One's a whiz at writing poems, another's a coding guru, and a third is a walking encyclopedia of historical facts. Wouldn't it be awesome if you could combine their strengths without having to retrain them all from scratch every time you need a new project done?
That's essentially what this paper is tackling. Right now, there are tons of different Language Models (LMs) out there, each with its own strengths and weaknesses. But no single model is the ultimate champion. So, naturally, researchers are looking for ways to merge them, to create a super-brain that's better than the sum of its parts.
The problem is, the current methods for merging these models often have drawbacks. Some require a lot of extra data and computation, which can be expensive and time-consuming. Others end up messing with the internal knowledge that each model already possesses, kind of like scrambling the brains of our expert team.
That’s where this new technique, called SeMe (Semantic-based Merging), comes in. What's really cool about SeMe is that it’s data-free and training-free. That means it doesn’t need any extra data to work its magic, and it doesn't require retraining the models. It’s like finding a universal translator that allows our experts to collaborate seamlessly without needing to learn a new language.
So, how does it work? Well, SeMe focuses on aligning the semantic meaning of the models' internal representations. Think of it like this: each layer of a Language Model "thinks" about information in a certain way. SeMe figures out how those different ways of thinking relate to each other and then merges the models layer by layer, ensuring that the important stuff is preserved. It's like carefully combining the notes from different experts in a way that keeps the core message intact.
The researchers found that SeMe works surprisingly well across different types of Language Models and tasks. It consistently outperforms existing methods, both in terms of performance and efficiency. And, crucially, it doesn't mess with the models' existing knowledge!
"SeMe... establishes a new paradigm for knowledge-aware model merging."
This is a pretty big deal because it opens up the possibility of creating much more powerful and versatile AI systems without having to spend a fortune on data and training. Imagine being able to combine specialized AI models for everything from medical diagnosis to financial forecasting, creating customized solutions that are both accurate and efficient.
So, why should you care about this research?

For the AI enthusiasts: This is a major step towards more scalable and interpretable model composition. It could lead to the development of entirely new types of AI systems that are more powerful and efficient than anything we have today.


For the business leaders: SeMe offers a way to leverage the power of AI without breaking the bank. It could enable companies to create customized AI solutions that are tailored to their specific needs, without having to invest in massive amounts of data and training.


For everyone else: This research highlights the ongoing effort to make AI more accessible and useful. By finding ways to combine existing models, researchers are paving the way for a future where AI can help us solve some of the world's most pressing problems.

This paper brings up some interesting questions for me:

How far can we push this "knowledge-aware" merging? Could we eventually create a single, unified AI model that combines all the knowledge of the world?


What are the ethical implications of combining AI models in this way? How do we ensure that the resulting systems are fair and unbiased?


Could SeMe be adapted to merge other types of AI models besides Language Models, like image recognition or reinforcement learning models?

That's all for this episode of PaperLedge! I hope you found this research as fascinating as I did. Until next time, keep learning, keep questioning, and keep exploring the amazing world of AI!Credit to Paper authors: Jian Gu, Aldeida Aleti, Chunyang Chen, Hongyu Zhang

Hey everyone, Ernis here, and welcome back to PaperLedge! Today we're diving into a fascinating new research paper that asks: How good are AI agents, like the ones powering self-driving cars or robots, at actually understanding and manipulating the world around them? Not just recognizing objects, but planning and building things in a virtual space?
The paper introduces something called MineAnyBuild, which is basically a super-cool, comprehensive benchmark designed to test the spatial planning skills of AI agents inside the Minecraft game. Think of Minecraft as the ultimate digital sandbox – agents can mine resources, craft tools, and build structures.
Now, previous tests for AI "spatial intelligence" often relied on things like answering questions about pictures (Visual Question Answering, or VQA). But the researchers argue that's like asking someone to describe how to build a house without ever handing them a hammer or letting them lay a brick. There's a gap between understanding the theory and actually doing it.
MineAnyBuild bridges that gap. It challenges AI agents to create executable building plans based on multi-modal instructions - think text descriptions, images, or even voice commands. So, a player could tell the agent: "Build a cozy cottage with a chimney next to the river using stone bricks and a wooden door." The agent then needs to figure out how to make that happen in Minecraft. It's like giving an architect a brief and expecting them to design a building that can actually be constructed.
The benchmark has 4,000 curated spatial planning tasks and can be infinitely expanded by leveraging player-generated content. That's a lot of digital LEGO bricks!
The researchers evaluate the agents on four key areas:
Spatial Understanding: Can the agent grasp the instructions and the relationships between objects?
Spatial Reasoning: Can the agent figure out how to arrange things in a logical and functional way?
Creativity: Can the agent come up with unique and interesting designs?
Spatial Commonsense: Does the agent understand basic real-world constraints, like gravity or the need for a foundation?
So, what did they find? Well, the existing AI agents, even the ones based on powerful Multimodal Large Language Models (MLLMs), struggled! They showed some potential, but also some serious limitations in their spatial planning abilities. It's like they can talk about building a house, but they don't know how to swing a hammer or read a blueprint.
"MineAnyBuild reveals the severe limitations but enormous potential in MLLM-based agents' spatial planning abilities."
Why does this matter? Well, think about it. If we want AI to truly help us in the real world – to build robots that can assemble furniture, design sustainable cities, or even assist in disaster relief – they need to be able to understand and plan in three-dimensional space. This research provides a valuable tool for measuring and improving those skills.
This research could be useful to:
Game developers: For building more realistic and intelligent NPCs.
Robotics engineers: For developing robots that can navigate and manipulate objects in complex environments.
Urban planners: For simulating and optimizing city layouts.
This paper makes us think about some important questions:
If current AI struggles with spatial planning in a relatively simple environment like Minecraft, how far away are we from AI that can truly design and build things in the real world?
Could incorporating more "embodied" experiences, like simulations where AI agents actively interact with a virtual world, help them develop stronger spatial reasoning skills?
That's it for this episode of PaperLedge! I hope you found this research as fascinating as I did. Until next time, keep learning and keep exploring!Credit to Paper authors: Ziming Wei, Bingqian Lin, Zijian Jiao, Yunshuang Nie, Liang Ma, Yuecheng Liu, Yuzheng Zhuang, Xiaodan Liang

Alright learning crew, Ernis here, ready to dive into some seriously cool research! Today, we're tackling a paper that's all about making AI smarter when it comes to understanding geometry – think shapes, angles, and spatial relationships. It's called... well, let's just call it "Making AI a Geometry Whiz."
So, what's the big deal? You know how Large Language Models (LLMs) like GPT-4 are amazing at understanding and generating text? Well, Large Multimodal Models (LMMs) are like their even cooler cousins – they can also understand images! They're trained on massive datasets of images and text, learning to connect what they see with what they read.
Think of it like this: imagine showing a toddler a picture of a dog and saying "dog." They eventually connect the image with the word. LMMs do something similar, but on a massive scale.
Now, these LMMs are pretty good at visual perception tasks, like identifying objects in a picture. But when it comes to really reasoning about geometric problems – like, say, figuring out the area of a triangle based on a diagram and some text – they often struggle. The researchers behind this paper found that the way these LMMs are initially trained limits their detailed reasoning abilities, especially in geometry.
Why? Because a common way to train the "vision" part of these models is through something called "contrastive learning." Imagine showing the AI a picture of a cat and telling it, "This is a cat." Then, you show it a picture of something else (like a dog) and tell it, "This is not a cat." The AI learns to distinguish between cats and non-cats by contrasting them. However, the "non-cat" examples are often too easy. It's like teaching someone to recognize the Mona Lisa by only showing them blurry photos of random objects as "not Mona Lisa."
"The inherent limitations of contrastive learning upon summarized descriptions fundamentally restrict the capabilities of models in meticulous reasoning, particularly in crucial scenarios of geometric problem-solving."
This is where the really clever part comes in. The researchers developed a new training method called "hard negative contrastive learning." Basically, they made the "non-cat" examples much harder. For the image side, they did this by taking a diagram and tweaking the code that generated the diagram in the first place to create similar, but incorrect, diagrams. For the text side, they did it by slightly changing the problem description using geometry rules or by finding similar but ultimately wrong descriptions from other problems.
Think of it like this: instead of showing the AI a blurry photo of a shoe as "not Mona Lisa," they showed it a slightly altered version of the Mona Lisa itself – maybe with a slightly different smile or background. This forces the AI to pay much closer attention to the details and learn to distinguish the real Mona Lisa from very similar fakes.
They used this "hard negative" approach to train a model based on CLIP (Contrastive Language-Image Pre-training), calling it MMCLIP (Multimodal Math CLIP). Then, they used this improved "vision" encoder to train an LMM specifically for geometric problem-solving, which they dubbed MMGeoLM.
And guess what? It worked! MMGeoLM significantly outperformed other open-source models on geometric reasoning benchmarks. They even claim that their 7B parameter model can compete with closed-source behemoths like GPT-4o!
In essence, these researchers have created a more robust foundation for geometry-aware AI by improving the model's ability to discern subtle nuances. This is incredibly important, because AI that can reason geometrically is crucial for applications like:
Robotics: Helping robots navigate complex environments and manipulate objects with precision.
Computer-Aided Design (CAD): Making CAD software more intuitive and efficient.
Scientific Discovery: Assisting researchers in fields like physics and engineering.
Education: Providing personalized geometry tutoring.
The team also dug deeper, experimenting with different ways to create these "hard negative" examples and seeing how the number of examples affected the performance. These experiments provided valuable insights into how to best train LMMs for geometric reasoning. All the code and data are available on Github, which is awesome for reproducibility and further research!
So, what does this all mean for us?
Well, it means that we're one step closer to AI that can truly understand and reason about the world around us. It demonstrates the immense impact of training data quality on the overall performance of multimodal models. It also highlights the importance of thinking outside the box when it comes to training AI – sometimes, making things harder can actually make them smarter.
Okay, learning crew, that's the gist of it! Let's think about this a bit more:
Could this "hard negative" technique be applied to other areas of AI, like medical image analysis or self-driving cars? What kind of "hard negatives" would be most effective in those domains?
The model is still trained on diagrams. How could we train the model to work with real-world images of geometric shapes? Would that require a completely different approach?
How do we ensure that these models are not just memorizing solutions but are actually learning to reason geometrically? What kinds of tests could we devise to evaluate this?
I'd love to hear your thoughts on this! Hit me up on the PaperLedge Discord channel. Until next time, keep learning!Credit to Paper authors: Kai Sun, Yushi Bai, Zhen Yang, Jiajie Zhang, Ji Qi, Lei Hou, Juanzi Li

Alright learning crew, Ernis here, ready to dive into another fascinating paper! Today, we're tackling a really interesting challenge in the world of AI, specifically with those super-smart Large Language Models, or LLMs – think of them as the brains behind chatbots and AI writing assistants.
So, these LLMs are constantly getting better, right? And to measure how good they are, we use something called a benchmark. Imagine a benchmark as a standardized test for LLMs, like a spelling bee for computers. It helps us see which models are truly improving and which are just good at sounding smart.
But here's the catch: putting these benchmarks out in the open, on the internet, can actually mess up future LLMs. It's like giving students the answer key before the exam! Why? Because developers might unintentionally (or even intentionally!) use the benchmark questions and answers to train their models. This is called data contamination, and it makes it really hard to know if a model is genuinely smart or just memorized the test.
Now, one way to avoid this is to keep the benchmark super secret, like a hidden vault. But then, we have to trust a single organization to run the tests fairly, and even then, people can still try to "overfit" to the test by repeatedly querying the system, slowly figuring out the answers. It's like trying to guess the combination to a lock by trying every possible number.
So, what's the solution? That's where this paper comes in! The authors propose a clever way to publish benchmarks without giving away all the answers. Their idea is to inject a little bit of randomness into the answers. Think of it like this: instead of having only one correct answer to a question, they create several logically correct answers, but only include one of them in the benchmark.
Imagine the question is "What is a synonym for 'happy'?" Instead of just "joyful," the benchmark might also accept "content," "elated," or "cheerful," but only one of those is marked as the "correct" answer. This introduces a level of uncertainty that makes it much harder for models to cheat. This approach reduces what is called the Bayes accuracy of the benchmark. In simple terms, it lowers the highest score a model could possibly achieve.
Why is this important? Because even the smartest LLM shouldn't be able to score above this Bayes accuracy if it's truly learning and not just memorizing the benchmark. If a model does surpass this limit, it's a big red flag that something's fishy – that it's likely been trained on the benchmark data and is therefore contaminated.
The researchers tested this method on a bunch of different benchmarks, models, and training techniques, and they found that it was surprisingly good at detecting data contamination. Basically, it's like a built-in lie detector for LLMs!
Why should you care?
For AI researchers: This is a crucial tool for developing and evaluating truly intelligent AI systems. It helps ensure that progress is real and not just an illusion.
For developers: It encourages the development of more robust and generalizable models that aren't just good at answering specific questions.
For everyone else: As AI becomes more and more integrated into our lives, it's essential to have reliable ways to assess its capabilities. This research helps to build trust in AI by ensuring that it's being developed responsibly.
"In principle, even fully capable models should not surpass the Bayes accuracy. If a model surpasses this ceiling despite this expectation, this is a strong signal of data contamination."
So, a couple of things that popped into my head while reading this paper:
How could this "randomized answer" approach be applied to other types of AI benchmarks, like those used for image recognition or robotics?
Could this method be used to actively prevent data contamination, by training models to be robust to these kinds of noisy or ambiguous answers?
Food for thought, learning crew! What do you think? Let me know in the comments!Credit to Paper authors: Takashi Ishida, Thanawat Lodkaew, Ikko Yamane

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool research! Today we're talking about giving AI a powerful new tool: the entire internet!
We all know how impressive those big language models are, right? Like ChatGPT, Gemini, the list goes on. They can answer almost anything, but a lot of that magic happens behind closed doors. It's like knowing the chef makes an amazing dish, but you have no idea what ingredients they use or how they cook it. That's where this paper comes in.
These researchers wanted to build a system, they call it ManuSearch, that makes "deep search" more accessible and transparent. Think of it like this: imagine you're trying to solve a complex puzzle. Instead of just staring at all the pieces at once, ManuSearch breaks it down into smaller, more manageable tasks, just like a team of experts working together.
So, how does it work? Well, it uses three “agents”:

First, we have the Solution Planning Agent. It's like the team leader, figuring out the best strategy and breaking down the big question into smaller, more focused sub-questions. Think of it as planning your road trip - you need to figure out the destination, the route, and the stops along the way.


Next up is the Internet Search Agent. This agent is the researcher. It goes out and finds relevant information on the web using those sub-questions. It's like having a super-efficient research assistant who can quickly find exactly what you need online.


Finally, we have the Structured Webpage Reading Agent. This agent is like your highly skilled note-taker. It sifts through all the web pages found by the Search Agent and extracts the key pieces of information, structuring it for the other agents to use. It's like highlighting the important sentences in a textbook chapter.

These agents work together. The Solution Planning Agent defines the sub-questions, the Internet Search Agent finds the answers, and the Webpage Reading Agent extracts the key evidence. Then, they all collaborate to solve the original problem.
Now, to test how well ManuSearch works, the researchers created a new, super-challenging benchmark called ORION. This benchmark focuses on "long-tail entities", which are basically obscure or niche topics. Think of it like asking the AI about a really specific species of beetle found only in a remote part of the Amazon rainforest. This requires real reasoning and the ability to sift through a lot of potentially irrelevant information.
And guess what? ManuSearch didn't just perform well; it beat existing open-source systems and even some of the top closed-source systems! That's a huge deal because it shows that this transparent, modular approach is not only feasible but also incredibly effective.
Why does this matter?

For researchers: It provides a framework that can be easily extended and improved upon. It allows for more reproducible and transparent research in the field of deep search.


For developers: It offers a blueprint for building their own web-augmented LLMs.


For everyone: It moves us closer to a future where AI is more accessible and understandable.

The researchers have even released their code and data, which is fantastic news for the open-source community!
"Our work paves the way for reproducible, extensible research in open deep search systems."
So, what questions does this research bring to mind?
First, given that ManuSearch is built around internet search, how vulnerable is it to misinformation or biased sources online? In other words, if the internet is full of junk, how does ManuSearch filter out the noise and find the truth?
Second, could this approach be adapted to other complex problem-solving tasks beyond just answering questions? What about using it for scientific discovery, or creative writing, or even something like coding?
Third, if systems like ManuSearch become more powerful, what are the ethical implications of having AI that can access and process vast amounts of information? How do we ensure that these systems are used responsibly and don't perpetuate harmful biases?
That's all for this episode! Let me know your thoughts on ManuSearch. I'm curious to see where this research leads!Credit to Paper authors: Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Wayne Xin Zhao

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool science! Today, we're shrinking down – way down – to the nanoscale, where things get… well, let's just say seeing and understanding these tiny particles is a huge challenge. Think of it like trying to assemble a LEGO set where you only have a blurry photo of the finished product.
The paper we're looking at tackles this problem head-on. Nanomaterials, these incredibly small substances, are becoming super important in everything from better batteries to targeted drug delivery. To really use them effectively, we need to know exactly what they look like – their topology, as the scientists say. Are they spheres? Rods? Weird, lumpy blobs? This shape dictates their properties.
Now, the problem is, getting good images of these nanoparticles is tough. Really tough. And even when you do get an image (usually from something like a scanning electron microscope, or SEM), figuring out what you're actually seeing – segmenting the image – is even harder. It's like trying to pick out individual grains of sand on a beach from a satellite photo. That means labeling these images is painstaking and requires experts. And that means… not many labeled images exist!
This lack of data is a major bottleneck for training AI to automatically analyze these images. If the AI doesn't have enough examples to learn from, it's like trying to teach a dog tricks with no treats or guidance.
So, what's the solution? Well, these researchers came up with something pretty ingenious: they built a system called F-ANcGAN (try saying that five times fast!), which is a fancy acronym, but the key is that it creates realistic fake images of nanoparticles.
Think of it like this: imagine you're trying to learn how to draw a cat. You could spend years trying to find the perfect cat to model. Or, you could use a special computer program that understands what cats are supposed to look like and then generates endless variations. That's essentially what F-ANcGAN does, but for nanoparticles.
Here's how it works (in a nutshell, of course!):
They use a "generator" – kind of like an artist – that creates images from simple shapes and instructions.
Then, they have a "segmentation network" – think of it as a very picky art critic – that tries to analyze those images.
The generator gets feedback from the critic, learning to make the images more and more realistic.
They also use something called "self-attention," which helps the system focus on the important structural relationships within the nanoparticles. It's like the artist knowing where the cat's ears should be in relation to its eyes.
They even use "augmentation methods" – like stretching, rotating, and slightly distorting the few real images they do have – to create even more variety in the training data. It's like showing the cat artist pictures of cats in different poses and lighting conditions.
The results? Pretty impressive! They tested their system on images of titanium dioxide (TiO$_2$) nanoparticles (commonly used in sunscreen and pigments). They used a metric called the FID score to evaluate how realistic the generated images were. A lower score is better, and they achieved a score of nearly 10, which is a significant improvement over previous methods.
“By facilitating scalable high-fidelity synthetic dataset generation, our approach can improve the effectiveness of downstream segmentation task training, overcoming severe data shortage issues in nanoparticle analysis, thus extending its applications to resource-limited fields.”
Basically, they're making it easier for researchers, especially those in labs with limited resources, to study these important nanomaterials.
So, why should you care? Well, if you're in materials science, this could seriously speed up your research. If you're interested in medicine, it could lead to better drug delivery systems. And if you're just curious about the world around you, it's a fascinating example of how AI can help us understand even the tiniest things.
Now, a few questions that popped into my head while reading this:
Could this technique be used to generate realistic images of other microscopic structures, like cells or viruses?
How far away are we from AI being able to design novel nanoparticles with specific properties, based on these generated images?
What are the ethical considerations of generating synthetic data that could potentially be used for malicious purposes (e.g., creating fake research results)?
That's all for this episode! Until next time, keep learning!Credit to Paper authors: Varun Ajith, Anindya Pal, Saumik Bhattacharya, Sayantari Ghosh

Hey learning crew, Ernis here, ready to dive into some fascinating research! Today, we're talking about how computers recognize you just by the way you walk – that's gait recognition!
Now, you might think this is straight out of a spy movie, and in some ways, it is! But gait recognition has serious real-world applications, from security systems that can identify individuals in crowds to helping doctors diagnose neurological conditions by analyzing subtle changes in someone's walk.
The paper we're unpacking today is all about using large vision models, or LVMs, for gait recognition. Think of LVMs as super-smart computers that have been trained on massive amounts of visual data, allowing them to "see" and understand images in incredible detail. They're like having a super-powered art critic analyzing every step you take!
So, what's the buzz? Well, researchers have already been using these LVMs to recognize people's gaits, and they've been getting pretty good results. But the authors of this paper thought something was missing. They felt that existing methods were too focused on pre-programmed ideas about what makes a gait unique – things like stride length or arm swing. It's like forcing the art critic to only focus on brushstrokes and ignoring the overall composition of the painting.
The real power, they argued, lies within the LVM itself! These models have tons of "layers," each capturing different aspects of the visual information. Imagine it like peeling an onion – each layer reveals a different level of detail, from the overall shape to the tiniest textures.
This research found that different layers of the LVM are good at different things when it comes to gait recognition. Some layers might be better at identifying overall body movement, while others might be better at spotting subtle differences in how your feet hit the ground. And get this: combining information from multiple layers gives you a much better result than relying on any single layer alone!
"LVM's intermediate layers offer complementary properties across tasks, integrating them yields an impressive improvement even without rich well-designed gait priors."
Think of it like this: you're trying to identify a friend in a crowd. One person tells you they're wearing a blue shirt. Another person tells you they have curly hair. Neither piece of information alone is enough, but put them together, and you can pinpoint your friend much more easily.
Based on this insight, the researchers developed a new approach called BiggerGait. It's a simple but effective way to combine the information from different layers of the LVM to achieve state-of-the-art gait recognition. The cool thing is that it works well even when the LVM hasn't been specifically trained on gait data. This makes it a really universal baseline for future research.
They tested BiggerGait on several datasets, including CCPG, CAISA-B, SUSTech1K, and CCGR_MINI, and it consistently outperformed existing methods, both in situations where the LVM had seen similar data before and in situations where it hadn't. It's like showing that your friend-finding strategy works just as well at a concert as it does at a football game.
The authors are even making their models and code publicly available, so other researchers can build upon their work! That's what we love to see - open and collaborative science!
So, why does this matter? Well, for security companies, it could mean more accurate and reliable surveillance systems. For healthcare providers, it could mean new tools for diagnosing and monitoring neurological disorders. And for AI researchers, it could mean a better understanding of how LVMs work and how to unlock their full potential.
It also raises some interesting questions:
Could this technology be used to identify people without their knowledge or consent, and what ethical considerations should we be aware of?
How could we use gait recognition to personalize healthcare, such as by detecting early signs of mobility decline in older adults?
What other human characteristics could we potentially identify using LVMs and similar techniques?
That's all for today, learning crew! I hope you found this exploration of BiggerGait as fascinating as I did. Until next time, keep learning and keep questioning!Credit to Paper authors: Dingqing Ye, Chao Fan, Zhanbo Huang, Chengwen Luo, Jianqiang Li, Shiqi Yu, Xiaoming Liu

Hey PaperLedge learning crew, Ernis here! Get ready to level up your knowledge because today we're diving into some seriously cool research about how well AI understands the world through sight and language, just like we do. But instead of textbooks, we're using... video games!
That's right, researchers have created a new challenge called VideoGameBench. Think of it as an obstacle course for AI, using classic 90s video games like Super Mario World, The Legend of Zelda: A Link to the Past, Kirby Super Star, and more. The goal? To see if cutting-edge vision-language models (VLMs) – that's AI that can "see" images and "understand" text – can actually play these games from start to finish.
Now, these VLMs are already pretty amazing. They can solve complex math problems and even write code! But the researchers noticed something: these AIs are really good at tasks that are hard for humans, but still struggle with things that come naturally to us, like figuring out where we are, remembering things, and understanding what we see. It's like they're brilliant at calculus but can't find their way out of a paper bag!
So, why video games? Well, video games are designed to be intuitive for humans. They rely on our natural ability to learn and understand patterns. Plus, they're a fun way to test if an AI can actually perceive, navigate, and remember, all at the same time. This is a big deal!
"Real video games are crafted to be intuitive for humans to learn and master by leveraging innate inductive biases, making them an ideal testbed for evaluating such capabilities in VLMs."
The cool part is, the AI only gets to see the game screen, just like we do. It also gets a simple description of the game's goals and controls. No extra hints or special training! It's a pure test of its ability to understand and interact with the world.
To make things even more interesting, the researchers kept three of the games a secret! This forces the AI to learn general skills instead of memorizing specific solutions. It's like teaching someone to ride a bike instead of just memorizing how to ride one specific bike on one specific path. This is a test of generalization.
So, how did the AIs do? Well... not great. Even the most advanced VLMs struggled to get past the very beginning of the games. Why? It turns out that a major problem is inference latency. That's a fancy way of saying that the AI takes too long to process what it sees and decide what to do next. Imagine trying to play a fast-paced game when you have to pause every second to think about your next move – that's what these AIs are dealing with.
To address this, the researchers created VideoGameBench Lite. In this version, the game pauses while the AI is thinking. Even with this advantage, the best AI, Gemini 2.5 Pro, only completed a tiny fraction of the games (less than 2%).
The researchers hope that this new benchmark will inspire more research into how to make AI better at understanding and interacting with the real world. It's not just about winning video games, it's about building AI that can assist us in all sorts of ways, from helping us navigate complex environments to understanding our needs and preferences.
Now, here are a few things that really got me thinking:
Why is it so much harder for AI to learn these "human" skills compared to complex calculations? Is it a matter of data, algorithms, or something more fundamental?

Could improving AI's "reaction time" in these games translate to real-world benefits in fields like robotics or self-driving cars?

If AI can't even beat Super Mario World, are we overestimating its ability to truly understand and interact with the world around us?

What do you think, learning crew? Let me know your thoughts in the comments. Until next time, keep exploring!Credit to Paper authors: Alex L. Zhang, Thomas L. Griffiths, Karthik R. Narasimhan, Ofir Press