In this tutorial, we discover the Advanced Model Context Protocol (MCP) and exhibit how to use it to tackle one in all the most unusual challenges in trendy AI programs: enabling real-time interplay between AI fashions and exterior knowledge or instruments. Traditional fashions function in isolation, restricted to their coaching knowledge, however by means of…
Reinforcement Learning RL post-training is now a significant lever for reasoning-centric LLMs, however not like pre-training, it hasn’t had predictive scaling guidelines. Teams pour tens of hundreds of GPU-hours into runs and not using a principled strategy to estimate whether or not a recipe will hold enhancing with extra compute. A brand new analysis from…
In this tutorial, we discover the superior capabilities of PyTest, one of probably the most highly effective testing frameworks in Python. We construct a whole mini-project from scratch that demonstrates fixtures, markers, plugins, parameterization, and customized configuration. We give attention to displaying how PyTest can evolve from a easy check runner into a strong, extensible…
Tuning LLM outputs is essentially a decoding drawback: you form the mannequin’s next-token distribution with a handful of sampling controls—max tokens (caps response size beneath the mannequin’s context restrict), temperature (logit scaling for extra/much less randomness), top-p/nucleus and top-k (truncate the candidate set by chance mass or rank), frequency and presence penalties (discourage repetition or…
ServiceNow Research has launched DRBench, a benchmark and runnable surroundings to judge “deep analysis” brokers on open-ended enterprise duties that require synthesizing information from each public net and personal organizational knowledge into correctly cited experiences. Unlike web-only testbeds, DRBench phases heterogeneous, enterprise-style workflows—information, emails, chat logs, and cloud storage—so brokers should retrieve, filter, and attribute…
In this tutorial, we discover Ivy’s outstanding skill to unify machine studying improvement throughout frameworks. We start by writing a totally framework-agnostic neural community that runs seamlessly on NumPy, PyTorch, TensorFlow, and JAX. We then dive into code transpilation, unified APIs, and superior options like Ivy Containers and graph tracing, all designed to make deep…
Evaluating LLM purposes, notably these utilizing RAG (Retrieval-Augmented Generation), is essential however typically uncared for. Without correct analysis, it’s nearly unimaginable to affirm in case your system’s retriever is efficient, if the LLM’s solutions are grounded within the sources (or hallucinating), and if the context measurement is perfect. Since preliminary testing lacks the mandatory actual…
Google AI Research group has introduced a manufacturing shift in Voice Search by introducing Speech-to-Retrieval (S2R). S2R maps a spoken question instantly to an embedding and retrieves info without first changing speech to textual content. The Google group positions S2R as an architectural and philosophical change that targets error propagation within the traditional cascade modeling…
What if you happen to might tune multimodal retrieval at serve time—buying and selling accuracy, latency, and index dimension—just by selecting what number of learnable Meta Tokens (e.g., 1→16 for queries, 1→64 for candidates) to make use of? Meta Superintelligence Labs introduces MetaEmbed, a late-interaction recipe for multimodal retrieval that exposes a single management floor…
Google has open-sourced a Model Context Protocol (MCP) server that exposes read-only entry to the Google Ads API for agentic and LLM functions. The repository googleads/google-ads-mcp implements an MCP server in Python that surfaces two instruments right this moment: search (GAQL queries over Ads accounts) and list_accessible_customers (enumeration of buyer assets). It contains setup through…