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Google AI Introduces PaperBanana: An Agentic Framework that Automates Publication Ready Methodology Diagrams and Statistical Plots

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Generating publication-ready illustrations is a labor-intensive bottleneck in the research workflow. While AI scientists can now handle literature reviews and code, they struggle to visually communicate complex discoveries. A research team from Google and Peking University introduce new framework called ‘ PaperBanana ‘ which is changing that by using a multi-agent system to automate high-quality academic diagrams and plots. https://dwzhu-pku.github.io/PaperBanana/ 5 Specialized Agents: The Architecture PaperBanana does not rely on a single prompt. It orchestrates a collaborative team of 5 agents to transform raw text into professional visuals. https://dwzhu-pku.github.io/PaperBanana/ Phase 1: Linear Planning Retriever Agent : Identifies the 10 most relevant reference examples from a database to guide the style and structure. Planner Agent : Translates technical methodology text into a detailed textual description of the target figure. Stylist Agent : Acts as a desig...
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How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory

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In this tutorial, we build an ultra-advanced agentic AI workflow that behaves like a production-grade research and reasoning system rather than a single prompt call. We ingest real web sources asynchronously, split them into provenance-tracked chunks, and run hybrid retrieval using both TF-IDF (sparse) and OpenAI embeddings (dense), then fuse results for higher recall and stability. We orchestrate multiple agents, planning, synthesis, and repair, while enforcing strict guardrails so every major claim is grounded in retrieved evidence, and we persist episodic memory. Hence, the system improves its strategy over time. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install openai openai-agents pydantic httpx beautifulsoup4 lxml scikit-learn numpy import os, re, json, time, getpass, asyncio, sqlite3, hashlib from typing import List, Dict, Tuple, Optional, Any import numpy as np import httpx from bs4 import BeautifulSoup from pydantic im...
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NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scale

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How do you combine SigLIP2, DINOv3, and SAM3 into a single vision backbone without sacrificing dense or segmentation performance? NVIDIA’s C-RADIOv4 is a new agglomerative vision backbone that distills three strong teacher models, SigLIP2-g-384, DINOv3-7B, and SAM3, into a single student encoder. It extends the AM-RADIO and RADIOv2.5 line, keeping similar computational cost while improving dense prediction quality, resolution robustness, and drop-in compatibility with SAM3. The key idea is simple. Instead of choosing between a vision language model, a self supervised dense model, and a segmentation model, C-RADIOv4 tries to approximate all three at once with one backbone. https://ift.tt/tgvWHiz Agglomerative distillation in RADIO The RADIO family uses agglomerative distillation . A single ViT style student is trained to match both dense feature maps and summary tokens from several heterogeneous teachers. Earlier RADIO models combined DFN CLIP, DINOv2, and SAM. They already sup...
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Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3

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Waymo is introducing the Waymo World Model , a frontier generative model that drives its next generation of autonomous driving simulation. The system is built on top of Genie 3, Google DeepMind’s general-purpose world model, and adapts it to produce photorealistic, controllable, multi-sensor driving scenes at scale. Waymo already reports nearly 200 million fully autonomous miles on public roads. Behind the scenes, the Driver trains and is evaluated on billions of additional miles in virtual worlds. The Waymo World Model is now the main engine generating those worlds, with the explicit goal of exposing the stack to rare, safety-critical ‘long-tail’ events that are almost impossible to see often enough in reality. From Genie 3 to a driving-specific world model Genie 3 is a general-purpose world model that turns text prompts into interactive environments you can navigate in real time at roughly 24 frames per second, typically at 720p resolution. It learns the dynamics of scenes directl...
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Anthropic Releases Claude Opus 4.6 With 1M Context, Agentic Coding, Adaptive Reasoning Controls, and Expanded Safety Tooling Capabilities

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Anthropic has launched Claude Opus 4.6, its most capable model to date, focused on long-context reasoning, agentic coding, and high-value knowledge work. The model builds on Claude Opus 4.5 and is now available on claude.ai, the Claude API, and major cloud providers under the ID claude-opus-4-6 . Model focus: agentic work, not single answers Opus 4.6 is designed for multi-step tasks where the model must plan, act, and revise over time. As per the Anthropic team, they use it in Claude Code and report that it focuses more on the hardest parts of a task, handles ambiguous problems with better judgment, and stays productive over longer sessions. The model tends to think more deeply and revisit its reasoning before answering. This improves performance on difficult problems but can increase cost and latency on simple ones. Anthropic exposes a /effort parameter with 4 levels — low, medium, high (default), and max — so developers can explicitly trade off reasoning depth against speed and c...
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How to Build Production-Grade Data Validation Pipelines Using Pandera, Typed Schemas, and Composable DataFrame Contracts

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Schemas, and Composable DataFrame Contracts In this tutorial, we demonstrate how to build robust, production-grade data validation pipelines using Pandera with typed DataFrame models. We start by simulating realistic, imperfect transactional data and progressively enforce strict schema constraints, column-level rules, and cross-column business logic using declarative checks. We show how lazy validation helps us surface multiple data quality issues at once, how invalid records can be quarantined without breaking pipelines, and how schema enforcement can be applied directly at function boundaries to guarantee correctness as data flows through transformations. Check out the  FULL CODES here .  Copy Code Copied Use a different Browser !pip -q install "pandera>=0.18" pandas numpy polars pyarrow hypothesis import json import numpy as np import pandas as pd import pandera as pa from pandera.errors import SchemaError, SchemaErrors from pandera.typing import Series, D...
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