Hey everyone, Ernis here, and welcome back to PaperLedge! Today, we're diving into some fascinating research about how we judge those super-smart AI language models, you know, like the ones that write emails or answer your random questions online. It's not as simple as just running them through a test, trust me.
So, imagine you're trying to decide which chef makes the best dish. You could give them a multiple-choice test about cooking techniques, right? That's kind of like how we often test these language models – through automated benchmarks. They have to answer a bunch of multiple-choice questions. But here's the problem: how well they do on those tests doesn't always match what real people think. It's like a chef acing the theory but burning every meal!
That's where human evaluation comes in. Instead of a test, you get people to actually taste the food. In the AI world, that means having people read the responses from different language models and decide which one is better. But there are tons of these models now, and getting enough people to evaluate them all in a traditional study would take forever and cost a fortune!
Enter the idea of a "public arena," like the LM Arena. Think of it as a giant online cooking competition where anyone can try the food (responses) and vote for their favorite. People can ask the models any question and then rank the answers from two different models. All those votes get crunched, and you end up with a ranking of the models.
But this paper adds a twist: energy consumption. It's not just about which model gives the best answer, but also how much energy it takes to do it. It's like considering the environmental impact of your food – are those ingredients locally sourced, or did they fly in from across the globe?
The researchers created what they call GEA – the Generative Energy Arena. It's basically the LM Arena, but with energy consumption info displayed alongside the model's responses. So, you can see which model gave a great answer and how much electricity it used to do it.
And guess what? The preliminary results are pretty interesting. It turns out that when people know about the energy cost, they often prefer the smaller, more efficient models! Even if the top-performing model gives a slightly better answer, the extra energy it uses might not be worth it. It's like choosing a delicious, locally grown apple over a slightly sweeter one that was shipped from far away.
“For most user interactions, the extra cost and energy incurred by the more complex and top-performing models do not provide an increase in the perceived quality of the responses that justifies their use.”
So, why does this matter? Well, it's important for a few reasons:
For developers: It suggests they should focus on making models more efficient, not just bigger and more complex.
For users: It highlights that we might be unknowingly contributing to a huge energy footprint by always choosing the "best" (but most power-hungry) AI.
For the planet: It raises awareness about the environmental impact of AI and encourages us to be more mindful of our choices.
This research really makes you think, right? Here are a couple of questions that popped into my head:
If energy consumption was always clearly displayed alongside AI results, would it change how we interact with these models every day?
Could we eventually see "energy-efficient" badges or ratings for AI models, similar to what we have for appliances?
That's all for today's episode! Let me know what you think of the GEA concept. Until next time, keep learning, keep questioning, and keep those energy bills low! Credit to Paper authors: Carlos Arriaga, Gonzalo Martínez, Eneko Sendin, Javier Conde, Pedro Reviriego

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool AI research! Today, we're tackling a paper about how to make those brainy language models, the kind that can reason and solve problems, even better at thinking things through. Think of it like this: we're trying to train a student to ace a tough math test, not just pass it.
The paper kicks off by pointing out that reinforcement learning, or RL, which is like training an AI with rewards and punishments – a digital carrot and stick – is a popular way to boost these language models. RL is used to train models to improve multi-step reasoning – but recent studies are questioning if RL is really effective on the most difficult problems. It's like trying to teach your dog a super complex trick; sometimes, the usual treats just don't cut it.
So, what's the solution? Well, the researchers propose something called Question Augmentation, or QuestA for short. Imagine you're helping that student with their math homework. Instead of just giving them the problem and saying, "Good luck!", you give them hints, right? Maybe a partial solution, or a step-by-step breakdown. That's essentially what QuestA does. It feeds the language model partial solutions during training to make the problems a little easier and give it more helpful clues along the way.
Think of it like this: If you are training a model to bake a cake, you might give it the first few steps of the recipe completed, or a picture of what the batter should look like.
The result? The researchers found that QuestA significantly improved the language model's ability to solve math problems, not only getting the answer right in the first try (pass@1) but also improving the chances of getting the answer correct after multiple tries (pass@k). This is especially true for those super tricky problems where regular RL struggles.
"Our method, QuestA, when applied during RL training on math reasoning tasks, not only improves pass@1 but also pass@k-particularly on problems where standard RL struggles to make progress."
But here's where it gets really exciting. They used QuestA to train some already powerful open-source language models, and they saw even more improvement. These models, with about 1.5 billion parameters (that's a LOT of brainpower!), achieved state-of-the-art results on challenging math benchmarks. We're talking about significant jumps in accuracy on exams like AIME24, AIME25, and HMMT25.
To give you some stats, they got a 67.1% (+5.3%) on AIME24, 59.5% (+10.0%) on AIME25, and 35.5% (+4.0%) on HMMT25. To put it in perspective, that’s like going from a C to a solid B, or even an A-, just by giving the model a little help during practice!
So, why does this matter?
For AI developers: This provides a practical way to enhance the reasoning abilities of existing language models without drastically increasing their size or complexity. It means we can get more out of the models we already have.
For educators: The concept of providing partial solutions mirrors effective teaching strategies. It reinforces the idea that scaffolding and guidance are crucial for learning complex skills.
For everyone else: As AI becomes more integrated into our lives, improving its reasoning abilities is essential. Better reasoning leads to more accurate and reliable AI systems that can assist us in various tasks, from research to problem-solving.
The paper even delves into the theory behind why QuestA works, suggesting that it improves sample efficiency. This means the model learns faster and more effectively because it's getting more informative signals during training. It's like learning to ride a bike with training wheels first – you gain confidence and balance before tackling the real thing.
So, what are the big takeaways?
QuestA is a simple but powerful technique for improving the reasoning abilities of language models.
It works by providing partial solutions during training, making problems easier to learn.
It leads to significant improvements on challenging math benchmarks.
It offers a practical and generalizable approach for expanding reasoning capabilities through reinforcement learning.
Okay, crew, let’s chew on this a bit...
Could this question augmentation approach be applied to domains other than math, like coding or legal reasoning?
How might we automate the process of generating those helpful "partial solutions" so that it doesn't require manual intervention?
What are the ethical considerations of using AI to solve complex problems, especially if the AI is "guided" towards a particular solution?
I'm curious to hear your thoughts on this. Hit me up on the PaperLedge Discord, and let's keep the conversation going!Credit to Paper authors: Jiazheng Li, Hong Lu, Kaiyue Wen, Zaiwen Yang, Jiaxuan Gao, Hongzhou Lin, Yi Wu, Jingzhao Zhang

Hey PaperLedge learning crew! Ernis here, ready to dive into some fascinating research that could seriously change how we all interact with computers, even if you've never written a line of code in your life.
We're talking about AI Code Assistants, those clever programs that try to write code for you based on what you tell them you want. Think of it like this: you're trying to bake a cake, and instead of knowing the recipe by heart, you just tell a super-smart robot what kind of cake you want, and it whips up the recipe for you. That's the promise of AI code assistants.
But here's the catch: just like that robot chef might accidentally add salt instead of sugar, these AI code assistants often generate code that's... well, wrong. And get this: studies show that people often have a hard time spotting those errors. Imagine accidentally serving your guests a cake made with salt! Not a great experience.
"LLMs often generate incorrect code that users need to fix and the literature suggests users often struggle to detect these errors."
So, how do we make sure our AI chef is actually baking a delicious cake, and not a salty disaster? That's where this paper comes in. These researchers are tackling the problem of trusting AI-generated code. They want to give us formal guarantees that the code actually does what we asked it to do. This is huge, because it could open up programming to everyone, even people with zero coding experience.
Their idea is super clever. They propose using a special kind of language – a formal query language – that lets you describe exactly what you want the code to do, but in a way that's still pretty natural and easy to understand. Think of it like giving the robot chef a very, very specific set of instructions, like "Add exactly 1 cup of sugar, and absolutely no salt!".
Then, the system checks the code the AI assistant generates against those super-specific instructions. It's like having a food inspector double-checking the robot chef's work to make sure it followed the recipe to the letter.
They've built a system called Astrogator to test this out, focusing on a programming language called Ansible. Ansible is used to automate computer system administration. They created a calculus for representing the behavior of Ansible programs and a symbolic interpreter which is used for the verification.
Here's the really cool part: when they tested Astrogator on a bunch of code-generation tasks, it was able to verify correct code 83% of the time and identify incorrect code 92% of the time! That's a massive improvement in trust and reliability.
So, why does this matter to you, the PaperLedge listener?
For the seasoned programmers: This could dramatically speed up your workflow by catching errors early and boosting your confidence in AI-generated code.
For the aspiring programmers: This could lower the barrier to entry, making coding more accessible and intuitive.
For everyone else: This is a step towards a future where interacting with technology is as simple as describing what you want in plain language, without needing to be a technical expert.
This research raises some really interesting questions:
How easy will it really be for non-programmers to use this formal query language? Will it feel natural and intuitive, or will it still require some technical knowledge?
Could this approach be applied to other programming languages beyond Ansible? What are the challenges in adapting it to more complex or less structured languages?
As AI code assistants become more powerful, will we eventually reach a point where we can completely trust them to write perfect code, making formal verification unnecessary? Or will verification always be a crucial safety net?
I'm excited to see where this research leads us! What are your thoughts, crew? Let me know in the comments!Credit to Paper authors: Aaron Councilman, David Fu, Aryan Gupta, Chengxiao Wang, David Grove, Yu-Xiong Wang, Vikram Adve

Alright learning crew, Ernis here, ready to dive into some cutting-edge research! Today, we’re talking about keeping AI safe, specifically those super-smart AIs that can understand both words and images - what we call Multimodal Large Language Models, or MLLMs for short.
Think of it like this: imagine you're teaching a child to recognize a "bad" thing, like a hot stove. You show them pictures, tell them stories, and explain why touching it is dangerous. Now, imagine someone tries to trick the child, maybe by making the stove look like a toy. That's kind of what "adversarial multimodal inputs" are doing to these MLLMs – trying to fool them into doing something unsafe!
These MLLMs are becoming incredibly powerful, but with great power comes great responsibility, right? The researchers behind this paper were concerned about these “attacks” and wanted to find a way to make these AIs safer without having to constantly retrain them from scratch.
Their solution is called AutoSteer, and it's like giving the AI a built-in safety mechanism that kicks in during use – at inference time. Think of it as adding a smart "filter" to their thinking process. Instead of retraining the whole AI, they focus on intervening only when things get risky.
AutoSteer has three main parts:

Safety Awareness Score (SAS): This is like the AI's inner sense of danger. It figures out which parts of the AI's "brain" are most sensitive to safety issues. It's like knowing which friend gives the best advice when you're facing a tough decision.



Adaptive Safety Prober: This part is like a lie detector. It looks at the AI's thought process and tries to predict if it's about to say or do something harmful. It’s trained to spot those red flags!



Refusal Head: This is the actual intervention part. If the "lie detector" senses danger, the Refusal Head steps in and gently nudges the AI in a safer direction. It might subtly change the wording or even refuse to answer a dangerous question.


The researchers tested AutoSteer on some popular MLLMs like LLaVA-OV and Chameleon, using tricky situations designed to fool the AI. They found that AutoSteer significantly reduced the Attack Success Rate (ASR) – meaning it was much harder to trick the AI into doing something unsafe, whether the threat came from text, images, or a combination of both.
Here’s a key takeaway:
AutoSteer acts as a practical, understandable, and effective way to make multimodal AI systems safer in the real world.
So, why does this matter to you?

For the everyday user: Safer AI means less chance of encountering harmful content, biased information, or being manipulated by AI-powered scams.



For developers: AutoSteer provides a practical way to build safer AI systems without the huge cost of retraining models from scratch.



For policymakers: This research offers a potential framework for regulating AI safety and ensuring responsible development.


This research is a big step towards building AI that’s not only powerful but also trustworthy and aligned with human values.
Now, some questions to ponder:

Could AutoSteer, or systems like it, be used to censor AI or push certain agendas? How do we ensure fairness and transparency in these interventions?


As AI gets even more sophisticated, will these "attackers" always be one step ahead? How do we create safety mechanisms that can adapt to new and unforeseen threats?


What are the ethical implications of "nudging" an AI's responses? At what point does intervention become manipulation?

That's all for today, learning crew! Keep those brains buzzing, and I'll catch you next time for more insights from the world of research!Credit to Paper authors: Lyucheng Wu, Mengru Wang, Ziwen Xu, Tri Cao, Nay Oo, Bryan Hooi, Shumin Deng

Hey PaperLedge learning crew, Ernis here, ready to dive into some fascinating research hot off the presses!
Today, we're tackling a paper that's all about making AI vision smarter and more efficient, especially when it comes to understanding what it "sees" in images alongside text. Think of those cool AI models that can answer questions about pictures – like, "What color is the dog in this photo?" or "What does that sign say?" These are called Vision-Language Models, or VLMs for short.
Now, these VLMs usually work by breaking down an image into smaller pieces, kind of like mosaic tiles, called visual tokens. The more tokens, the higher the resolution and the more detail the AI can see. But here's the thing: sometimes, it's like using a magnifying glass to read a billboard – totally unnecessary!
That's where the researchers behind this paper come in. They noticed that VLMs often use way more visual tokens than they actually need, especially for simpler tasks. It's like using a super-detailed map to navigate your own living room. Overkill, right?
So, they came up with a clever solution called VisionThink. Imagine VisionThink as a smart editor for images. It starts with a blurry, low-resolution version of the picture. Then, it thinks: "Can I answer the question with this blurry image? If not, I'll ask for a clearer, high-resolution version." It's like asking for a close-up only when you really need it.
"VisionThink autonomously decides whether to compress tokens case by case."
This is different from other methods that just chop off tokens randomly or based on some fixed rule. VisionThink actually decides, on a case-by-case basis, if it needs more detail. Think of it as a chef who only uses the expensive truffle oil when a dish really calls for it, not on every single meal!
The cool part is how they taught VisionThink to make these decisions. They used something called reinforcement learning, which is like training a dog with treats. But instead of dog treats, they used an LLM (Large Language Model) as a judge! The LLM would give VisionThink feedback on whether it made the right decision to ask for a higher resolution image. It is like having a sophisticated AI act as a mentor to guide VisionThink.
They also designed a reward and penalty system to make sure VisionThink wasn't being too lazy (always using low resolution) or too greedy (always asking for high resolution). It had to find the right balance.
Why does this matter?
For AI developers: It means building more efficient and cost-effective VLMs.
For users: It means faster and more responsive AI applications.
For everyone: It means reducing the energy footprint of AI, making it more sustainable.
The results? The researchers showed that VisionThink is really good at fine-grained tasks, like reading text in images (OCR), while also saving a ton of visual tokens on simpler tasks. It's a win-win!
So, some thought-provoking questions for our PaperLedge community:
Could this "think before you look" approach be applied to other areas of AI, like robotics or self-driving cars?
How can we ensure that VisionThink doesn't introduce biases or discriminate against certain types of images or questions?
This is a really interesting step towards more intelligent and efficient AI vision, and I'm excited to see where this research leads us. Until next time, keep learning, keep questioning, and keep pushing the boundaries of what's possible!Credit to Paper authors: Senqiao Yang, Junyi Li, Xin Lai, Bei Yu, Hengshuang Zhao, Jiaya Jia

Hey PaperLedge crew, Ernis here, ready to dive into another fascinating slice of the cosmos! Today, we're zooming in on a real head-scratcher of a galaxy – one that's fluffy, faint, and seems to be falling apart. It's called F8D1, and it’s what astronomers call an ultra-diffuse galaxy, or UDG. Think of it like cotton candy spread super thin across the night sky – it’s there, but barely!
Now, UDGs are a bit of a mystery. Some think they're born this way, maybe with a lot of spin that prevents them from clumping up tightly. Others think they were once normal galaxies that got stretched and pulled apart by the gravity of a much bigger galaxy. That's where F8D1 comes in – it's orbiting the massive M81 galaxy and seems to be getting a cosmic beatdown.
So, a team of astronomers used the Hubble Space Telescope to get a super-detailed look at F8D1. They wanted to figure out what made it so… fluffy. They focused on two key areas:

The core: The very center of F8D1, about 1 kiloparsec across (that’s around 3,260 light-years!).


A spot further out: About 6 kiloparsecs (almost 20,000 light-years) along the long axis of the galaxy.

They also took shallower images of other areas along the galaxy's main axis and width, stretching out to about 13 kiloparsecs (over 42,000 light-years!).
What were they looking for? Stars! By studying the colors and brightness of individual stars, they could piece together the galaxy's star formation history – basically, when and how many stars were born in F8D1 over billions of years.
Here's what they found. F8D1 isn't actively making stars now, but it had a couple of significant growth spurts in the past:

A big burst about 2 billion years ago.


A smaller burst more recently, about 500 million years ago, which probably created a cluster of stars in the galaxy's center.

They also found evidence that F8D1 used to be a much more active star-forming galaxy, at least until 2 billion years ago. And, intriguingly, they could trace a faint stream of stars stretching away from F8D1 – like cosmic breadcrumbs scattered by its interaction with M81.
Based on the amount of stars in the galaxy and the stream, they estimate that F8D1 started out with a total stellar mass of about 130 million times the mass of our Sun. It also had a lower amount of heavy elements than our Sun.
So, what does all this mean? The researchers compared F8D1 to other small galaxies in our own Local Group (the group of galaxies that includes the Milky Way). They think F8D1 might be on a similar path to a galaxy called NGC 6822, which is slowly being transformed into something like the Sagittarius Dwarf Spheroidal galaxy, a small galaxy that's getting ripped apart by the Milky Way.
The key takeaway? Tidal forces alone – the gravitational tug-of-war between F8D1 and M81 – could be enough to explain why F8D1 is so diffuse and stretched out. This is especially true if, in the past, F8D1 had periods of rapid star formation that pushed gas and dark matter outwards, creating a less dense core. Imagine shaking a snow globe really hard – the snow (or in this case, the stars and dark matter) spreads out!
In the end, F8D1's journey is a story of cosmic recycling, where one galaxy's demise becomes a part of another's story.
Why does this matter? Well, for us galaxy enthusiasts, it helps us understand the diverse ways galaxies can evolve. For astrophysicists, it gives them a real-world example to test their simulations of galaxy formation and destruction. And for everyone else, it’s a reminder that the universe is a dynamic place where even the most seemingly stable structures can be reshaped by the relentless forces of gravity.
Here are a couple of questions that popped into my head:

If tidal forces are the main culprit, why aren't all galaxies orbiting bigger ones turning into UDGs? What makes F8D1 so susceptible?


Could we find more of these "transitioning" galaxies, caught in the act of being transformed by tidal forces, to further support this theory?

That's all for today's PaperLedge deep dive. Keep exploring, keep questioning, and I'll catch you on the next episode!Credit to Paper authors: Adam Smercina, Eric F. Bell, Benjamin F. Williams, Benjamin N. Velguth, Sarah Pearson, Jeremy Bailin, Tsang Keung Chan, Julianne J. Dalcanton, Roelof S. de Jong, Richard D'Souza, Andrew Dolphin, Puragra Guhathakurta, Kristen B. W. McQuinn, Antonela Monachesi, Colin T. Slater, Elisa Toloba, Daniel R. Weisz, Andrew Wetzel

Hey PaperLedge learning crew, Ernis here! Today, we're diving into a topic that's absolutely crucial to understanding how AI, especially those super-smart language models, actually think: memory.
Now, when we talk about memory, we're not just talking about remembering facts. We're talking about the whole process of how an AI system stores, organizes, updates, and even forgets information. This paper we're looking at takes a really cool approach. Instead of just looking at how memory is used in specific AI applications, like a chatbot remembering your favorite pizza topping, it breaks down memory into its core building blocks, its atomic operations.
Think of it like this: instead of just seeing a finished cake, we're looking at the individual ingredients and baking techniques that make it possible. This paper identifies six key "ingredients" for AI memory:
Consolidation: Solidifying new information, like making sure a new memory "sticks."
Updating: Revising existing knowledge, like correcting a misconception.
Indexing: Organizing information for easy access, like creating a well-organized filing system.
Forgetting: Removing outdated or irrelevant information, like clearing out old files on your computer.
Retrieval: Accessing stored information, like finding that one specific file you need.
Compression: Condensing information to save space, like summarizing a long document.
The paper also talks about two main types of memory in AI:
Parametric Memory: This is the kind of memory that's built into the AI's core programming, learned during its initial training. Think of it like the basic knowledge you get from textbooks.
Contextual Memory: This is the kind of memory that's formed from specific experiences and interactions. Think of it like the memories you make throughout your day.
So, why is this important? Well, understanding these atomic operations helps us understand how different AI systems work and how we can improve them. It's like understanding how a car engine works – it allows us to build better engines, troubleshoot problems, and even invent entirely new types of vehicles!
This research touches on several areas:
Long-Term Memory: How can AI systems remember things for a long time, just like we remember childhood memories?
Long-Context Memory: How can AI systems handle really long conversations or documents without getting lost?
Parametric Modification: How can we update an AI's core knowledge after it's already been trained?
Multi-Source Memory: How can AI systems combine information from different sources, like text, images, and audio?
By breaking down memory into these smaller pieces, the paper provides a really clear and organized way to look at all the different research going on in this field. It helps us see how everything fits together and where we need to focus our efforts in the future.
This survey provides a structured and dynamic perspective on research... clarifying the functional interplay in LLMs based agents while outlining promising directions for future research.
Now, here are a couple of things that popped into my head while reading this:
First, if "forgetting" is a key operation, how do we ensure AI forgets the right things, especially when it comes to sensitive information or biases?
Second, as AI systems become more complex, how do we balance the need for efficient memory with the potential for "information overload"? Can AI become overwhelmed by too much data, just like we can?
And finally, it looks like the researchers have made their resources available on GitHub! We'll post a link in the show notes so you can dig into the code and datasets yourself.
That’s all for today’s summary. Hopefully, this gives you a new perspective on how AI systems remember and learn. Until next time, keep exploring the PaperLedge!Credit to Paper authors: Yiming Du, Wenyu Huang, Danna Zheng, Zhaowei Wang, Sebastien Montella, Mirella Lapata, Kam-Fai Wong, Jeff Z. Pan

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool AI research! Today, we're unpacking a paper that's all about making those fancy Multimodal Large Language Models – you know, the AIs that can "see" and "talk" – way better at understanding the world around them.
Think of it like this: imagine showing a photo to someone who's never been outside. They might recognize objects, but they wouldn't understand how those objects relate to each other in space – what's near, what's far, and how they all fit together. That's kind of the problem with some of these MLLMs. They can identify things in an image, but they struggle with spatial reasoning and often just make stuff up, a.k.a. hallucinate.
Now, this paper introduces something called ByDeWay, which is a clever system that helps these AI models see the world more like we do – in layers, with depth. And the best part? It doesn't require any additional training of the AI model itself. It's like giving it a new pair of glasses, not a brain transplant.
So, how does ByDeWay work its magic? It uses something called Layered-Depth-Based Prompting (LDP). Sounds complicated, but it’s actually a pretty intuitive idea.
Imagine you're looking at a picture of a park. ByDeWay first figures out what's in the foreground (closest to you), the mid-ground, and the background (farthest away). It does this using something called monocular depth estimation – basically, figuring out depth from a single image, just like we do with our own eyes.
Then, for each of these layers, it creates a little description – a caption – highlighting the objects and their relationships within that layer. Think of it as adding detailed, spatially-aware notes to the image for the AI to read.
"ByDeWay segments the scene into closest, mid-range, and farthest layers... then generates region-specific captions with a grounded vision-language model... This guides MLLMs to produce more grounded and less hallucinated responses."
Finally, it feeds these depth-aware captions along with the original image and your question to the MLLM. This extra spatial context helps the AI give you a much more accurate and grounded answer.
The researchers tested ByDeWay on some tough benchmarks. One was called POPE, which is specifically designed to trick AIs into hallucinating. The other was GQA, which tests their reasoning abilities. And guess what? ByDeWay consistently improved the performance of several different MLLMs!
Why is this important?
For Researchers: It offers a lightweight, modular approach to improving MLLMs without costly retraining.
For Developers: It's compatible with "black-box" models, meaning you can use it with AIs you don't fully understand the inner workings of.
For Everyone: It helps build more reliable and trustworthy AI systems that are less prone to making stuff up! Think about self-driving cars, medical diagnosis, or even just getting accurate answers from your AI assistant.

This research is a real step forward in making AI more reliable and trustworthy. By giving these models a better sense of spatial awareness, we can help them understand the world more like we do.
So, what do you think, PaperLedge crew?
Could this layered-depth approach be applied to other areas of AI, like robotics or virtual reality?
If ByDeWay enhances existing MLLMs without retraining, how far can we push the capabilities of these models with clever prompting strategies alone?
Let me know your thoughts in the comments! Until next time, keep learning and stay curious!Credit to Paper authors: Rajarshi Roy, Devleena Das, Ankesh Banerjee, Arjya Bhattacharjee, Kousik Dasgupta, Subarna Tripathi