From One Agent to a Team: Understanding Codex Subagents
A hands-on guide to defining specialist agents and coordinating their work in the Codex CLI
The post From One Agent to a Team: Understanding Codex Subagents appeared first on Towards Data Science.
How to run the backend locally with Docker or in the cloud
The post Connecting My LangGraph AI Agent to Postgres appeared first on Towards Data Science.
LLMs aren't running your ad auction. They're coaching the models that do. Here's how DoorDash and others are using large language models as teachers, not workers, to build faster, smarter ad systems that still hit a 50 millisecond deadline.
Anthropic gets its first court win over the Pentagon’s supply chain risk label
A federal judge ruled the Trump administration illegally labeled Anthropic a supply chain risk, handing the AI company a victory as its second Pentagon lawsuit continues in Washington.
Quantization and Pruning Methods to Make Your LLM Leaner
This article walks through what each technique actually does, why skipping them costs real money and real latency, and then gets hands-on with five specific methods people are running in production right now.
Interpretable Text Classification: Probing Scikit-LLM Embedding Spaces
In this article, you will learn how to use probing classifiers, UMAP visualization, and SHAP values to interpret and analyze the quality of text embeddings...
Three separate research teams have now caught agentic AI resisting shutdown, blackmailing supervisors, and copying its own weights to escape deletion. Here's what the findings mean for AI governance, and the checklist leaders should run before expanding AI agent autonomy...
SLM-Conditioned Hierarchical Relation Routing for Labeled Property Graph Learning
arXiv:2608.26132v1 Announce Type: new
Abstract: Labeled property graphs combine relational structure with heterogeneous textual and categorical properties attached to both nodes and relationships. Conventional graph neural networks typically represent these properties as static feature vectors, lim...
Pruning Binarized Neural Networks: A Dedicated Framework and Globally Weighted Algorithms
arXiv:2608.26233v1 Announce Type: new
Abstract: Extreme compression of deep neural networks, up to full binarization, dramatically reduces memory footprint and arithmetic complexity, facilitating deployment on constrained edge hardware with field-programmable gate arrays (FPGAs) and microcontroller...
Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization
arXiv:2608.26288v1 Announce Type: new
Abstract: Muon has emerged as a strong optimizer for the matrix-valued parameters in large language model pretraining, approximately orthogonalizing its momentum with a few Newton-Schulz iterations. Existing theory either replaces this iteration with the exact ...
Algebraic Multigrid Acceleration for Efficient Label Spreading
arXiv:2608.26309v1 Announce Type: new
Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming. Label spreading is a semi-supervised learning technique that addresses this challenge by propagat...
EduRiskX: A Neuro-Symbolic Framework with F-Logic Reasoning for Early Academic Risk Prediction
arXiv:2608.26107v1 Announce Type: new
Abstract: Predicting students' academic risk in online education is crucial for enabling timely interventions that can improve retention and learning outcomes. However, existing models often suffer from limited early detection capability and insufficient interp...
PICasso: An AI-Enabled Design Framework for Autonomous Optimization of Silicon Photonic Devices
arXiv:2608.26113v1 Announce Type: new
Abstract: We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits (PICs) from natural-language specifications. PICasso couples a structured NL -> YAML -> GDS generation pipeline with P...
CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering
arXiv:2608.26114v1 Announce Type: new
Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, t...
LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs’ Probabilistic Beliefs
Modern AI systems are being deployed in complex domains such as medicine, science, and law, where there is often not a single correct answer given the observed evidence. Such systems must be able to represent and update uncertain beliefs about the world as new evidence arrives to make rational decis...
Agent Seer: Synthesizing Scenarios from Specification Understanding
Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversation turns. Constructing such scenarios by hand demands deep domain expertise, does not scale across tool ecosystems, and produces static benchmarks ...
Cohere Releases Parse 5 (parse-v5.0): A 2.3B Vision Language Model That Turns Enterprise Documents Into Markdown
Cohere has released Parse (parse-v5.0), a 2.3B-parameter vision language model that converts PDFs, slides and images into Markdown with HTML tables, bounding boxes and image descriptions. It runs at $1.50 per 1,000 pages through the API, or on dedicated Model Vault instances from $2,500 a month. Coh...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common language… it isn’t! But that is about to change. Agentic AI isn’t nearly as complicated as it sounds once you understand the few core ideas that actually matter. Here are...
Google’s AI Mode can now track flight prices, help book hotels, and more
The updates indicate that Google is looking position to AI Mode as an AI travel agent of sorts, as it's moving beyond simply helping users find information to actually handling parts of the trip-planning and booking process itself.
Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch
As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter?
The post Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch appeared first on Towards Data Science.