LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models

Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have emerged as leading open-weight models, but their random expert selection mechanism leads to significant data movement overhead. A recent study conducted comprehensive profiling across four state-of-the-art MoE models, revealing insights that can enhance future serving systems and reduce bottlenecks in multi-unit LLM serving.

Jina-VLM, a 2.4 billion parameter vision-language model, has been introduced, achieving state-of-the-art multilingual visual question answering capabilities among open 2B-scale VLMs. It integrates a SigLIP2 vision encoder with a Qwen3 language backbone, allowing for efficient processing of images at arbitrary resolutions.

A new approach called Semantic Soft Bootstrapping (SSB) has been proposed to enhance long context reasoning in large language models (LLMs) without relying on reinforcement learning. This self-distillation technique allows the model to act as both teacher and student, improving its reasoning capabilities by providing varied semantic contexts during training.

A recent study has introduced a taxonomy for cataloguing Open-source Pre-Trained Models (PTMs) from Hugging Face, specifically tailored to Software Engineering (SE) tasks. This classification encompasses 147 SE tasks, aiming to enhance the identification and reuse of models for software development activities. The research involved a comprehensive five-phase methodology, including data collection and validation processes.

Recent research has introduced ReMindView-Bench, a benchmark designed to evaluate how Vision-Language Models (VLMs) construct and maintain spatial mental models across multiple viewpoints. This initiative addresses the challenges VLMs face in achieving geometric coherence and cross-view consistency in spatial reasoning tasks, which are crucial for understanding 3D environments.

TaleFrame has been introduced as an innovative interactive story generation system that utilizes large language models (LLMs) to enhance user control over story creation. By breaking down story structures into fundamental components such as entities, events, relationships, and outlines, TaleFrame aims to improve the accuracy of story outputs based on user intent. This system leverages a preference dataset derived from the Tinystories dataset to fine-tune the Llama model for better performance.