Path-Based Gradient Boosting for Graph-Level Prediction
arXiv:2605.08102v1 Announce Type: new
Abstract: We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. Building on a previous work, which was tailored to a specific c...
Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning
arXiv:2605.08109v1 Announce Type: new
Abstract: Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks, but simulating them requires being able to predict particle migration, and thus particle lift for...
BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
arXiv:2605.08110v1 Announce Type: new
Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point-estimate updates limit expressiveness, leave a persistent gap relative to full fine-tuning accuracy,...
Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction
arXiv:2605.08220v1 Announce Type: new
Abstract: The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (LLMs) show promise, their accuracy on non-standardized charts remains a challenge. This raises a ke...
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
arXiv:2605.08354v1 Announce Type: new
Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional, multi-dimensional structure of human judgment. Prevailing RLHF approaches reduce this structure to scalar or pairwise labels, collapsin...
On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
arXiv:2605.08368v1 Announce Type: new
Abstract: Debates about large language model post-training often treat supervised fine-tuning (SFT) as imitation and reinforcement learning (RL) as discovery. But this distinction is too coarse. What matters is whether a training procedure increases the probabi...
What Parameter Golf taught us about AI-assisted research
Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, coding agents, quantization, and novel model design under strict constraints.
GM just laid off hundreds of IT workers to hire those with stronger AI skills
Some of the positions focus on AI-native development, data engineering and analytics, cloud-based engineering, and agent and model development as well as prompt engineering and new AI workflows.
Modern large language models are no longer trained only on raw internet text. Increasingly, companies are using powerful “teacher” models to help train smaller or more efficient “student” models. This process, broadly known as LLM distillation or model-to-model training, has become a key technique f...
Learning Word Vectors for Sentiment Analysis: A Python Reproduction
How to build sentiment-aware word representations from IMDb reviews using semantic learning, star ratings, and linear SVM classification
The post Learning Word Vectors for Sentiment Analysis: A Python Reproduction appeared first on Towards Data Science.
Build an AI-Powered Learning Management System That Actually Trains People
Learn how to build an AI-powered Learning Management System from scratch using Ollama, FastAPI, and React. A step-by-step guide for beginner and intermediate developers.
In finance departments that have long been defined by precision and control, AI has arrived less as a neatly managed upgrade than as a quiet insurgency. Employees are already using it while leadership races to impose structure, governance, and strategy after the fact. The result is a paradox: one of...
Implementing Prompt Compression to Reduce Agentic Loop Costs
Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.
Human-in-the-Loop becomes an operational bottleneck In my previous article, ”The Missing Layer in Agentic AI,” I argued that AI agents need a deterministic execution kernel—a privileged “Kernel Space” that validates every proposed action before it touches the real world. That article focused on what...
A step-by-step guide to understanding distributed data, lazy logic, and your first DataFrame.
The post PySpark for Beginners: Mastering the Basics appeared first on Towards Data Science.
Sakana AI and NVIDIA Introduce TwELL with CUDA Kernels for 20.5% Inference and 21.9% Training Speedup in LLMs
Sakana AI and NVIDIA Researchers demonstrate that simple L1 regularization can induce over 99% sparsity in feedforward layers with negligible downstream performance impact, and translate that sparsity into real GPU throughput gains using new sparse data formats and fused CUDA kernels.
The post Sakan...
A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications
In this tutorial, we implement how Memori serves as an agent-native memory infrastructure layer for building more persistent, context-aware LLM applications. We start by setting up Memori in a Google Colab environment and connecting it to both synchronous and asynchronous OpenAI clients, so that eve...
OpenAI launches DeployCo to help businesses build around intelligence
OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.