Vector Tiered vector (MemGPT-style virtual context) Working set + vector archive Better reuse of important info, bounded context size Paging policy errors, per-agent divergence Graph Temporal KG ...

How can developers reliably generate, control, and inspect large volumes of realistic dialogue data without building a custom simulation stack every time? Meet SDialog, an open sourced Python toolkit ...

How far can we push large language model speed by reusing “free” GPU compute, without giving up autoregressive level output quality? NVIDIA researchers propose TiDAR, a sequence level hybrid language ...

In this tutorial, we build an advanced Reflex web application entirely in Python that runs seamlessly inside Colab. We design the app to demonstrate how Reflex enables full-stack development with no ...

Most text to video models generate a single clip from a prompt and then stop. They do not keep an internal world state that ...

Shobha is a data analyst with a proven track record of developing innovative machine-learning solutions that drive business value.

How do you tell whether a model is actually noticing its own internal state instead of just repeating what training data said about thinking? In a latest Anthropic’s research study ‘Emergent ...

Meta has released MobileLLM-R1, a family of lightweight edge reasoning models now available on Hugging Face. The release includes models ranging from 140M to 950M parameters, with a focus on efficient ...

Optical character recognition has moved from plain text extraction to document intelligence. Modern systems must read scanned and digital PDFs in one pass, preserve layout, detect tables, extract key ...

Orchestration Host routes across many servers/tools App-local chaining Agent/toolkit routes intents → operations ...

Feedforward neural networks are also known as Multi-layered Network of Neurons (MLN). These network of models are called feedforward because the information only travels forward in the neural network, ...

Code-oriented large language models moved from autocomplete to software engineering systems. In 2025, leading models must fix real GitHub issues, refactor multi-repo backends, write tests, and run as ...