The Mathematics Search Engine

2+ min ago (517+ words) The new approach takes a big step forward by generating realistic future X-rays quickly and by pinpointing the areas of the joint most likely to change. Scientists from the University of Surrey, UK, have developed an AI that predicts what a person's knee X-ray will look like in a year, helping track osteoarthritis progression. Osteoarthritis is a degenerative joint disorder that affects more than 500 million people globally. It is the leading cause of disability among older adults. The new tool provides both a visual forecast and a risk score, offering doctors and patients a clearer understanding of the disease. This technology is faster and more interpretable than earlier systems and it could soon be expanded to predict other conditions like lung or heart disease. The new research was recently presented at the International Conference on Medical Image Computing and Computer…...

17+ min ago (288+ words) Woke up at 9 AM today. Sounds impressive but don't get too excited, I don't think that's continuing tomorrow. I'm back at my grandma's place and she's pretty old, so gotta visit. The schedule gets thrown off when you're not in your usual environment. That's one of those things about consistency, it's easier when your routine is stable. Change locations and suddenly your 9 AM wake-up becomes noon again. But whatever, you adapt. Finished scikit learn sections 1.1.1, 1.1.2, 1.1.3, 1.1.5, and 1.1.15 today. Just to be clear - I didn't blast through all of these in one day. It took time to work through them properly, understand what each section was covering, actually absorb the information. Too many people speed-run documentation and wonder why nothing sticks. I'm trying to avoid that trap. Tomorrow I'll work on the practical implementations of these concepts. Theory is one thing, actually…...

37+ min ago (196+ words) We've been building a code-first RAG tool that actually understands how codebases work, not just how text looks in embeddings. The goal is simple: when you ask a question, you get the right functions, related calls, and supporting code, not random nearby snippets. A clean async ingestion pipeline with strict tool " agent " storage boundaries Semantic vector search as the starting point, not the end Built lazily from chunk metadata No persistence, no globals, no backend shortcuts Context expansion via BFS over calls and imports to pull in code that's actually connected Backend-agnostic vector store layer, so storage can change without rewriting logic Why We Think This Is Useful You get related code paths, not just similar text Context stays small, relevant, and debuggable The architecture avoids hidden state and scaling surprises What We'd Love Feedback On If you've worked with…...

55+ min ago (892+ words) In recent years, the integration of advanced technologies into the medical field has propelled research into previously uncharted territories, particularly in the diagnosis and treatment of chronic ailments. A collaborative study led by Jin et al. has undertaken a pioneering project focusing on lumbar disc degeneration. This groundbreaking research employs machine learning algorithms to enhance [] In recent years, the integration of advanced technologies into the medical field has propelled research into previously uncharted territories, particularly in the diagnosis and treatment of chronic ailments. A collaborative study led by Jin et al. has undertaken a pioneering project focusing on lumbar disc degeneration. This groundbreaking research employs machine learning algorithms to enhance the classification and understanding of a condition that affects millions globally. With the potential for significant implications in clinical settings, the study sheds light on the intricate interactions between clinical…...

59+ min ago (209+ words) An LLM cannot truly store past conversations. Its only "memory" is the context window, a fixed-length input buffer (e.g., 128k tokens in GPT-4.1, 200k+ in Claude 3.5 Sonnet, and up to 2 million tokens in Gemini 1.5 Pro). When the conversation exceeds that limit, the orchestrator must perform three critical steps for the next query: For developers building custom agents, this crucial orchestration layer does not come out of the box even when integrating APIs provided by these hyperscale AI assistants. You have to build your own, and that necessity is where the idea for MemCortex originated. This process is nearly identical to how enterprise AI systems handle long-term coherence, only they do it at a massive scale with additional scoring and ranking algorithms. Memcortex is simply the lightweight, developer-friendly version aimed at demystifying how long-term context is handled. When building a sophisticated AI agent, you…...

1+ hour, 12+ min ago (807+ words) A new MIT framework called SEAL allows language models to create their own study notes and choose how to train, improving learning without human-designed data. (CREDIT: Wikimedia / CC BY-SA 4.0) "Just like humans, complex AI systems can't remain static for their entire lifetimes. These LLMs are not deployed in static environments. They are constantly facing new inputs from users. We want to make a model that is a bit more human-like " one that can keep improving itself," says Jyothish Pari, an MIT graduate student and co-lead author. SEAL applies this human habit to machines. Rather than handing a model fixed training data and rigid instructions, the system lets the model reshape what it studies and how it studies it. The goal is not just better short-term answers, but lasting internal change. At the center of SEAL is a concept called a…...

1+ hour, 37+ min ago (910+ words) In this guide, we'll break down why it is so hard and walk through how to optimize and run models on Android devices. We'll also demonstrate how you can test it on different devices without needing physical access to a wide range of hardware. Noah's experience isn't unusual. In fact, it's one of the most common issues Android developers run into when working with on-device AI. Apps that work perfectly on a few phones but feel slow or broken on others, frustrated users leaving negative reviews, and developers end up removing the on-device feature " losing many of the benefits of running AI on-device in the first place. To understand why situations like Noah's happen, we need to look more closely at why the same model can show completely different latency, stability, and device-specific performance across devices, making on-device AI development…...

1+ hour, 38+ min ago (285+ words) Moreover, the study emphasizes the importance of data privacy and ethical considerations in the deployment of AI in education. As educational institutions harness data to drive decision-making, safeguarding the privacy of students becomes crucial. Lai's research advocates for robust policies that ensure data is used responsibly and transparently, fostering trust among students and educators alike. Lai's findings also address the importance of collaboration between technology developers and educational institutions. By working together, they can design AI tools that meet the specific needs of educators and students. Such partnerships can ensure that the technology is user-friendly, relevant, and accurately aligned with educational goals, thus enhancing its impact. As educational institutions embark on this journey toward AI integration, Lai's research serves as a vital resource, offering insights and guidance rooted in data-driven analysis. The transition to AI-enhanced education management is not merely…...

1+ hour, 57+ min ago (559+ words) One of my favorite AI papers is "Lets Think Dot By Dot, which finds that LLMs can use meaningless filler tokens (like ".) to improve their performance, but I was overestimating the implications until recently[1]and I think other people might be too. This means that if a problem can be broken down into sub-problems, but the model isn'twide enough to process it in one pass, the model can instead parallelize across multiple filler token positions and then combine the results. However, if the problem requires step-by-step thinking and the model isn'tdeep enough, filler tokens don't help. In comparison, Chain of Thought helps in both situations. My metaphor for this is that filler tokens allow a model to dynamically increase the size of layers, but CoT allows the model to dynamically add layers. Every layer in an LLM operates in parallel,…...

2+ hour, 6+ min ago (270+ words) Retrieval-Augmented Generation (RAG) systems face a fundamental challenge: LLMs have context window limits, yet documents often exceed these limits. Simply stuffing an entire document into a prompt isn't feasible for large corpora. This is where chunking becomes critical. Without proper chunking, RAG systems suffer from: Chunking is the process of breaking down large documents into smaller, semantically meaningful segments that can be: Effective chunking balances two competing goals: The optimal chunking strategy depends on your document type, retrieval task, and downstream LLM usage. The Agentic Memory library includes an extensible chunking framework that allows you to split documents into optimal chunks for semantic search and retrieval. All chunking strategies are part of the core framework in the io.github.vishalmysore.rag.chunking package. The example code simply demonstrates how to use these strategies. ChunkingStrategy interface - Base interface for all chunking…...