Alibaba's Metis agent cuts redundant AI tool calls from 98% to 2% — and gets more accurate doing it

Alibaba's HDPO framework trains AI agents to skip unnecessary tool calls, cutting redundant invocations from 98% to 2% while boosting reasoning accuracy.
The New York Times

Trump’s Iran Threats Look Like Self-Incrimination for Potential War Crimes

President Trump, in vowing to systematically destroy civilian infrastructure and annihilate Iran’s entire civilization, appears to be creating evidence about his intentions. By Charlie Savage Charlie ...
VentureBeat

Black Forest Labs' new Self-Flow technique makes training multimodal AI models 2.8x more efficient

To create coherent images or videos, generative AI diffusion models like Stable Diffusion or FLUX have typically relied on external "teachers"—frozen encoders like CLIP or DINOv2—to provide the ...
YourTango

Pregnant Woman’s Husband Forgetting Her Birthday Is Just One Example Of What Happens When You Marry The Embarrassing Boyfriend

It's normal to post your significant other on social media on date night, special occasions, or whenever your heart desires to publicize the positives about being in love. However, when a post about ...
The New York Times

What We Can Learn From Brain Organoids

Lab-grown “reductionist replicas” of the human brain are helping scientists understand fetal development and cognitive disorders, including autism. But ethical questions loom. Brain organoids, which ...
unite

DINOv3 and the Future of Computer Vision: Self-Supervised Learning at Scale

Labeling images is a costly and slow process in many computer vision projects. It often introduces bias and reduces the ability to scale large datasets. Therefore, researchers have been looking for ...
marktechpost

A Coding Guide to Master Self-Supervised Learning with Lightly AI for Efficient Data Curation and Active Learning

In this tutorial, we explore the power of self-supervised learning using the Lightly AI framework. We begin by building a SimCLR model to learn meaningful image representations without labels, then ...
GitHub

TypeError: self._context,self._fmu,self.callBackFunctions,self.callbacks cannot be converted to a Python object for pickling

import pyfmi import pickle model = pyfmi.load_fmu("BouncingBall.fmu") with open("study.pkl", "wb") as f: pickle.dump(model, f) with open("study.pkl", "rb") as f ...
Robohub

Self-supervised learning for soccer ball detection and beyond: interview with winners of the RoboCup 2025 best paper award

Presentation of the best paper award at the RoboCup 2025 symposium. An important aspect of autonomous soccer-playing robots concerns accurate detection of the ball. This is the focus of work by Can ...
IEEE

Self-Supervised Learning of Contrast-Diffusion Models for Land Cover Classification in SAR Images

Abstract: Deep learning (DL) methods have been widely applied to synthetic aperture radar (SAR) land cover classification. The complexity of SAR data and the limited availability of labeled samples ...
blockchain

DINOv3: Self-Supervised Learning for 1.7B-Image, 7B-Parameter AI Model Revolutionizes Dense Prediction Tasks

According to @AIatMeta, DINOv3 leverages self-supervised learning (SSL) to train on 1.7 billion images using a 7-billion-parameter model without the need for labeled data, which is especially ...