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A New Google AI Research Proposes Deep-Thinking Ratio to Improve LLM Accuracy While Cutting Total Inference Costs by Half

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For the last few years, the AI world has followed a simple rule: if you want a Large Language Model (LLM) to solve a harder problem, make its Chain-of-Thought (CoT) longer. But new research from the University of Virginia and Google proves that ‘thinking long’ is not the same as ‘thinking hard’. The research team reveals that simply adding more tokens to a response can actually make an AI less accurate. Instead of counting words, the Google researchers introduce a new measurement: the Deep-Thinking Ratio (DTR) . https://ift.tt/ymIMfiU The Failure of ‘Token Maxing ‘ Engineers often use token count as a proxy for the effort an AI puts into a task. However, the researchers found that raw token count has an average correlation of r= -0.59 with accuracy. This negative number means that as the model generates more text, it is more likely to be wrong. This happens because of ‘overthinking,’ where the model gets stuck in loops, repeats redundant steps, or amplifies its own mistak...
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How to Design an Agentic Workflow for Tool-Driven Route Optimization with Deterministic Computation and Structured Outputs

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In this tutorial, we build a production-style Route Optimizer Agent for a logistics dispatch center using the latest LangChain agent APIs. We design a tool-driven workflow in which the agent reliably computes distances, ETAs, and optimal routes rather than guessing, and we enforce structured outputs to make the results directly usable in downstream systems. We integrate geographic calculations, configurable speed profiles, traffic buffers, and multi-stop route optimization, ensuring the agent behaves deterministically while still reasoning flexibly through tools. Copy Code Copied Use a different Browser !pip -q install -U langchain langchain-openai pydantic import os from getpass import getpass if not os.environ.get("OPENAI_API_KEY"): os.environ["OPENAI_API_KEY"] = getpass("Enter OPENAI_API_KEY (input hidden): ") from typing import Dict, List, Optional, Tuple, Any from math import radians, sin, cos, sqrt, atan2 from pydantic import Ba...
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Is There a Community Edition of Palantir? Meet OpenPlanter: An Open Source Recursive AI Agent for Your Micro Surveillance Use Cases

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The balance of power in the digital age is shifting. While governments and large corporations have long used data to track individuals, a new open-source project called OpenPlanter is giving that power back to the public. Created by a developer ‘ Shin Megami Boson ‘, OpenPlanter is a recursive-language-model investigation agent. Its goal is simple: help you keep tabs on your government, since they are almost certainly keeping tabs on you. Solving the ‘Heterogeneous Data’ Problem Investigative work is difficult because data is messy. Public records are often spread across 100 different formats. You might have a CSV of campaign finance records, a JSON file of government contracts, and a PDF of lobbying disclosures. OpenPlanter ingests these disparate structured and unstructured data sources effortlessly. It uses Large Language Models (LLMs) to perform entity resolution . This is the process of identifying when different records refer to the same person or company. Once it connec...
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A Coding Guide to High-Quality Image Generation, Control, and Editing Using HuggingFace Diffusers

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In this tutorial, we design a practical image-generation workflow using the Diffusers library. We start by stabilizing the environment, then generate high-quality images from text prompts using Stable Diffusion with an optimized scheduler. We accelerate inference with a LoRA-based latent consistency approach, guide composition with ControlNet under edge conditioning, and finally perform localized edits via inpainting. Also, we focus on real-world techniques that balance image quality, speed, and controllability. Copy Code Copied Use a different Browser !pip -q uninstall -y pillow Pillow || true !pip -q install --upgrade --force-reinstall "pillow<12.0" !pip -q install --upgrade diffusers transformers accelerate safetensors huggingface_hub opencv-python import os, math, random import torch import numpy as np import cv2 from PIL import Image, ImageDraw, ImageFilter from diffusers import ( StableDiffusionPipeline, StableDiffusionInpaintPipeline, ControlN...
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How to Design a Swiss Army Knife Research Agent with Tool-Using AI, Web Search, PDF Analysis, Vision, and Automated Reporting

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In this tutorial, we build a “Swiss Army Knife” research agent that goes far beyond simple chat interactions and actively solves multi-step research problems end-to-end. We combine a tool-using agent architecture with live web search, local PDF ingestion, vision-based chart analysis, and automated report generation to demonstrate how modern agents can reason, verify, and produce structured outputs. By wiring together small agents, OpenAI models, and practical data-extraction utilities, we show how a single agent can explore sources, cross-check claims, and synthesize findings into professional-grade Markdown and DOCX reports. Copy Code Copied Use a different Browser %pip -q install -U smolagents openai trafilatura duckduckgo-search pypdf pymupdf python-docx pillow tqdm import os, re, json, getpass from typing import List, Dict, Any import requests import trafilatura from duckduckgo_search import DDGS from pypdf import PdfReader import fitz from docx import Document from do...
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NVIDIA Releases DreamDojo: An Open-Source Robot World Model Trained on 44,711 Hours of Real-World Human Video Data

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Building simulators for robots has been a long term challenge. Traditional engines require manual coding of physics and perfect 3D models. NVIDIA is changing this with DreamDojo , a fully open-source, generalizable robot world model. Instead of using a physics engine, DreamDojo ‘dreams’ the results of robot actions directly in pixels. https://ift.tt/pmRwriU Scaling Robotics with 44k+ Hours of Human Experience The biggest hurdle for AI in robotics is data. Collecting robot-specific data is expensive and slow. DreamDojo solves this by learning from 44k+ hours of egocentric human videos. This dataset, called DreamDojo-HV , is the largest of its kind for world model pretraining. It features 6,015 unique tasks across 1M+ trajectories. The data covers 9,869 unique scenes and 43,237 unique objects. Pretraining used 100,000 NVIDIA H100 GPU hours to build 2B and 14B model variants. Humans have already mastered complex physics, such as pouring liquids or folding clothes. DreamDojo...
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How to Build Transparent AI Agents: Traceable Decision-Making with Audit Trails and Human Gates

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In this tutorial, we build a glass-box agentic workflow that makes every decision traceable, auditable, and explicitly governed by human approval. We design the system to log each thought, action, and observation into a tamper-evident audit ledger while enforcing dynamic permissioning for high-risk operations. By combining LangGraph’s interrupt-driven human-in-the-loop control with a hash-chained database, we demonstrate how agentic systems can move beyond opaque automation and align with modern governance expectations. Throughout the tutorial, we focus on practical, runnable patterns that turn governance from an afterthought into a first-class system feature. Copy Code Copied Use a different Browser !pip -q install -U langgraph langchain-core openai "pydantic<=2.12.3" import os import json import time import hmac import hashlib import secrets import sqlite3 import getpass from typing import Any, Dict, List, Optional, Literal, TypedDict from openai import Op...
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