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Google AI Introduces PaperBanana: An Agentic Framework that Automates Publication Ready Methodology Diagrams and Statistical Plots
PaperBanana is an agentic framework designed to rescue researchers from the manual grind of creating publication-ready academic illustrations. By orchestrating a team of five specialized agents—Retriever, Planner, Stylist, Visualizer, and Critic—it transforms technical descriptions into high-fidelity methodology diagrams and numerically precise statistical plots. The system employs a dual-mode visualization strategy, utilizing image generation for diagrams and executable Matplotlib code for data plots to eliminate "visual hallucinations". Evaluated on the new PaperBananaBench dataset featuring 292 test cases from NeurIPS 2025, the framework outperformed standard baselines with a 17.0% gain in overall quality across faithfulness, conciseness, readability, and aesthetics. Essentially, it provides a professional "NeurIPS look" for AI scientists, ensuring that complex discoveries are as visually impressive as they are technically sound....… Read the full analysis/article here.
ByteDance Releases Protenix-v1: A New Open-Source Model Achieving AF3-Level Performance in Biomolecular Structure Prediction
ByteDance releases Protenix-v1, an AF3-class all-atom biomolecular structure prediction model with open code and weights under Apache 2.0, targeting proteins, DNA, RNA and ligands while explicitly matching AlphaFold3’s training data cutoff, model scale class and inference budget for fair comparison. Benchmarks are run with PXMeter v1.0.0 on more than 6k curated complexes with time-split and domain-specific subsets, showing Protenix-v1 outperforming AF3 and exhibiting clean, log-linear inference-time scaling as the number of sampled candidates increases. The ecosystem includes Protenix-v1-20250630 for applied use, compact Protenix-Mini variants for efficient inference, PXDesign for high-hit-rate binder design and Protenix-Dock for docking, giving engineers an AF3-style reference implementation plus a reproducible evaluation stack they can integrate, profile and extend in real-world pipelines...… Read the full analysis/article here.
Latest Releases in Last 72 Hours
Project Notebooks/Tutorials
▶ How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory Codes Tutorial
▶ Meet CopilotKit: Framework for building agent-native applications with Generative UI, shared state, and human-in-the-loop workflows Codes
▶ How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models Codes Tutorial
▶ How to Orchestrate a Fully Autonomous Multi-Agent Research and Writing Pipeline Using CrewAI and Gemini for Real-Time Intelligent Collaboration Codes Tutorial
▶ A Complete Workflow for Automated Prompt Optimization Using Gemini Flash, Few-Shot Selection, and Evolutionary Instruction Search Codes Tutorial
▶ [Open Source] Rogue: An Open-Source AI Agent Evaluator worth trying Codes & Examples