Tech Xplore

Interrupting encoder training in diffusion models enables more efficient generative AI

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as ...
The Scientist

Researchers Decode How Protein Language Models Think, Making AI More Transparent

By spreading out tightly packed information in neural networks, a new set of tools could make AI protein models easier to ...
GitHub

Train multi-step agents for real-world tasks using GRPO.

RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the ...

Alibaba’s Tongyi DeepResearch : Open-Source AI for Smarter Problem-Solving

Learn how Tongyi DeepResearch combines cutting-edge reasoning and open-source flexibility to transform advanced research workflows.

Tencent’s new AI technique teaches language models ‘parallel thinking’

The Parallel-R1 framework uses reinforcement learning to teach models how to explore multiple reasoning paths at once, ...
IEEE

QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train

Abstract: Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face challenges ...
IEEE

Low-rank Multi-modal Features Fusion Semantic Autoencoder For Zero-shot Learning

Abstract: In the context of the continuous increase of new things, zero-shot learning (ZSL) has been proposed to reduce recognition costs, with the goal of classifying or predicting new classes that ...