Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach
arXiv:2601.00024v1 Announce Type: new
Abstract: The 13-card variant of Classic Indian Rummy is a sequential game of incomplete information that requires probabilistic reasoning and combinatorial decision-making. This paper proposes a rule-based framework for strategic play, driven by a new hand-eva...
New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced brain-like activity without any training at all. This challenges today’s data-hungry approach to AI...
DeepSeek Researchers Apply a 1967 Matrix Normalization Algorithm to Fix Instability in Hyper Connections
DeepSeek researchers are trying to solve a precise issue in large language model training. Residual connections made very deep networks trainable, hyper connections widened that residual stream, and training then became unstable at scale. The new method mHC, Manifold Constrained Hyper Connections, k...
A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems
The post Optimizing Data Transfer in AI/ML Workloads appeared first on Towards Data Science.
DeepSeek mHC: Stabilizing Large Language Model Training
Large AI models are scaling rapidly, with bigger architectures and longer training runs becoming the norm. As models grow, however, a fundamental training stability issue has remained unresolved. DeepSeek mHC directly addresses this problem by rethinking how residual connections behave at scale. Thi...
Recursive Language Models (RLMs): From MIT’s Blueprint to Prime Intellect’s RLMEnv for Long Horizon LLM Agents
Recursive Language Models aim to break the usual trade off between context length, accuracy and cost in large language models. Instead of forcing a model to read a giant prompt in one pass, RLMs treat the prompt as an external environment and let the model decide how to inspect it with code, then re...
A Coding Implementation to Build a Self-Testing Agentic AI System Using Strands to Red-Team Tool-Using Agents and Enforce Safety at Runtime
In this tutorial, we build an advanced red-team evaluation harness using Strands Agents to stress-test a tool-using AI system against prompt-injection and tool-misuse attacks. We treat agent safety as a first-class engineering problem by orchestrating multiple agents that generate adversarial prompt...
How AI is reshaping work and who gets to do it, according to Mercor’s CEO
Three-year-old startup Mercor has become a $10 billion middleman in AI’s data gold rush. The company connects AI labs like OpenAI and Anthropic with former employees of Goldman Sachs, McKinsey, and white-shoe law firms, paying them up to $200 an hour to share their industry expertise and train the A...
AI Quantum Intelligence & Pic of the week (2026&01&02)
What do you get when you prompt Google Gemini (Nano Banana) to generate a creative image with no limitations - no scope, no style, no purpose, no requirements at all. This "AI Pic of the week" was the answer to that question. To be determined if the same request will produce the same result in the f...
Drift Detection in Robust Machine Learning Systems
A prerequisite for long-term success of machine learning systems
The post Drift Detection in Robust Machine Learning Systems appeared first on Towards Data Science.
In 2026, here's what you can expect from the AI industry: new architectures, smaller models, world models, reliable agents, physical AI, and products designed for real-world use.
Power BI is an influential tool, shaping raw data into informative visuals and reports. With a user-friendly interface and formidable functionalities, Power BI is an invaluable platform for individuals to refine their skills through hands-on projects. By engaging in Power BI practice projects, begin...
Liquid Foundation Models (LFM 2) define a new class of small language models designed to deliver strong reasoning and instruction-following capabilities directly on edge devices. Unlike large cloud-centric LLMs, LFM 2 focuses on efficiency, low latency, and memory awareness while still maintaining c...
Applications are now open for OpenAI Grove Cohort 2, a 5-week founder program designed for individuals at any stage, from pre-idea to product. Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team.
Spectral Capital Signs Agreement to Acquire Telvantis Voice Services, Inc.
Advancing Path Toward Profitable Scale and Anticipated $450 Million in 2026 Revenue Spectral Capital Corporation (“Spectral” or the “Company“), a digital infrastructure and AI-forward platform company, today announced that it has signed a Definitive Stock Purchase Agreement to acquire Telvantis Voic...
A Message for 2026: The Year of Human&Curated Progress (Part 4)
The fourth and final installment in a short series of articles from various AI models and how they view the upcoming year in 2026. A 2026 New Year’s message inspiring AI, ML, robotics, and automation innovators to build boldly and shape the future responsibly. This edition is from DeepSeek.
EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas
How to build, score, and interpret RFM segments step by step
The post EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas appeared first on Towards Data Science.
Deep Reinforcement Learning: The Actor-Critic Method
Robot friends collaborate to learn to fly a drone
The post Deep Reinforcement Learning: The Actor-Critic Method appeared first on Towards Data Science.
Google T5Gemma-2 Explained: Trying Out a Laptop-Friendly Multimodal AI Model
Google just dropped T5Gemma-2, and it is a game-changer for someone working with AI models on everyday hardware. Built on the Gemma 3 family, this encoder-decoder powerhouse squeezes multimodal smarts and massive context into tiny packages. Imagine running 270M parameters running smoothly on your la...
The Drill-Down and Fabricate Test (DDFT): A Protocol for Measuring Epistemic Robustness in Language Models
arXiv:2512.23850v1 Announce Type: new
Abstract: Current language model evaluations measure what models know under ideal conditions but not how robustly they know it under realistic stress. Static benchmarks like MMLU and TruthfulQA cannot distinguish a model that lacks knowledge from one whose veri...
CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
arXiv:2512.23880v1 Announce Type: new
Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an earl...
A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming
arXiv:2512.23932v1 Announce Type: new
Abstract: Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-qual...