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…
Table of contents Comparison Table Strengths and Limits Security and Governance Ecosystem Signals (Portability/Adoption) Decision Rules (When to Use Which) References: MCP (Model Context Protocol): Open, transport-agnostic protocol that standardizes discovery and invocation of instruments/assets throughout hosts and servers. Best for moveable, multi-tool, multi-runtime methods. Function Calling: Vendor characteristic the place the mannequin selects a…
Why deal with LLM inference as batched kernels to DRAM when a dataflow compiler can pipe tiles via on-chip FIFOs and stream converters?StreamTensor is a compiler that lowers PyTorch LLM graphs (GPT-2, Llama, Qwen, Gemma) into stream-scheduled dataflow accelerators on AMD’s Alveo U55C FPGA. The system introduces an iterative tensor (“itensor”) kind to encode tile/order…
AWS launched an open-source Model Context Protocol (MCP) server for Amazon Bedrock AgentCore, offering a direct path from natural-language prompts in agentic IDEs to deployable brokers on AgentCore Runtime. The package deal ships with automated transformations, atmosphere provisioning, and Gateway/tooling hooks designed to compress typical multi-step integration work into conversational instructions. So, what precisely is…