A Visualization for Comparative Analysis of Regression Models
arXiv:2603.19291v1 Announce Type: new
Abstract: As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very difficult and relies on...
Maximizing mutual information between user-contexts and responses improve LLM personalization with no additional data
arXiv:2603.19294v1 Announce Type: new
Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existing data has already been exploited, and new high-quality data is expens...
TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly
arXiv:2603.19296v1 Announce Type: new
Abstract: To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these methods highly rely on calibration data, domain shift issues may arise for unseen...
When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)
arXiv:2603.19429v1 Announce Type: new
Abstract: Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. R...
arXiv:2603.19461v1 Announce Type: new
Abstract: Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limi...
arXiv:2603.19500v1 Announce Type: new
Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforcement learning following supervised fine-tuning. Our approach is enable...
Learning to Disprove: Formal Counterexample Generation with Large Language Models
arXiv:2603.19514v1 Announce Type: new
Abstract: Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones. However, current AI efforts in mathematics focus almost exclusively on proof ...
ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
arXiv:2603.19515v1 Announce Type: new
Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent s...
To address the novel safety challenges posed by a state-of-the-art video model as well as a new social creation platform, we’ve built Sora 2 and the Sora app with safety at the foundation. Our approach is anchored in concrete protections.
Optimal Splitting of Language Models from Mixtures to Specialized Domains
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models at ICLR 2026.
Language models achieve impressive performance on a variety of knowledge, language, and reasoning tasks due to the scale and diversity of pretraining data available. The standard tr...
Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent
In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the C...
Meet GitAgent: The Docker for AI Agents that is Finally Solving the Fragmentation between LangChain, AutoGen, and Claude Code
The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally commit to one of several competing ecosystems: LangChain, AutoGen, CrewAI, OpenAI Assistants, or the more recent Claude Code. Each of these ...
Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow
A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a bird's wing
The post Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow appeared first on Towards Data Science.
A Coding Implementation for Building and Analyzing Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Materials Project Integration
In this tutorial, we explore the capabilities of the pymatgen library for computational materials science using Python. We begin by constructing crystal structures such as silicon, sodium chloride, and a LiFePO₄-like material, and then investigate their lattice properties, densities, and composition...
Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)
Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the existing production model can be risky. Offline evaluation rarely captures the full complexity of real-wo...
A Coding Implementation to Build an Uncertainty-Aware LLM System with Confidence Estimation, Self-Evaluation, and Automatic Web Research
In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those answers. We implement a three-stage reasoning pipeline in which the model first produces an answer along with a self-reported confidence score and a ...
Most data platforms don’t break overnight; they grow into complexity, query by query. Over time, business logic spreads across SQL scripts, dashboards, and scheduled jobs until the system becomes a “SQL jungle.” This article explores how that happens and how to bring structure back.
The post Escapin...
A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations
Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP
solvers like Gurobi.
The post A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations appeared first on Towards Data Science.
New court filing reveals Pentagon told Anthropic the two sides were nearly aligned — a week after Trump declared the relationship kaput
Anthropic submitted two sworn declarations to a California federal court late Friday afternoon, pushing back on the Pentagon's assertion that the AI company poses an "unacceptable risk to national security" and arguing that the government's case relies on technical misunderstandings and claims that ...
NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities
NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models. Nem...