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.
GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
arXiv:2605.06671v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is still unsatisfying, since graphs are naturally more complex in topology and often require systemat...
More Thinking, More Bias: Length-Driven Position Bias in Reasoning Models
arXiv:2605.06672v1 Announce Type: new
Abstract: Chain-of-thought (CoT) reasoning and reasoning-tuned models such as DeepSeek-R1 are commonly assumed to reduce shallow heuristic biases by thinking carefully. We test this on position bias in multiple-choice QA and find a different story: within any r...
Fast and Effective Redistricting Optimization via Composite-Move Tabu Search
arXiv:2605.06682v1 Announce Type: new
Abstract: Spatial redistricting is a practical combinatorial optimization problem that demands high-quality solutions, rapid turnaround, and flexibility to accommodate multi-criteria objectives and interactive refinement. A central challenge is the contiguity c...
State Representation and Termination for Recursive Reasoning Systems
arXiv:2605.06690v1 Announce Type: new
Abstract: Recursive reasoning systems alternate between acquiring new evidence and refining an accumulated understanding. Two design choices are typically left implicit: how to represent the evolving reasoning state, and when to stop iterating. This paper addre...
Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations
arXiv:2605.06696v1 Announce Type: new
Abstract: Collections of interacting AI agents can form coalitions, creating emergent group-level organization that is critical for AI safety and alignment. However, observing agent behavior alone is often insufficient to distinguish genuine informational coupl...
RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory
arXiv:2605.06675v1 Announce Type: new
Abstract: Large language models cache all previously computed key-value (KV) pairs during generation, and this KV cache grows linearly with sequence length, making it a primary memory bottleneck for serving. Quantizing the KV cache to fewer bits reduces this co...
LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction
arXiv:2605.06676v1 Announce Type: new
Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compression paradigms are fundamentally limited by heuristics: heuristic budgeting relies on statistical prio...
A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence
arXiv:2605.06678v1 Announce Type: new
Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion USD between 1970 and 2000 to 180--200 billion USD between 2001 and 2020. Reports from organization...
Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
arXiv:2605.06679v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free infer...