IHUBERT is a monolingual Persian pretrained language model trained on a 45 GB curated subset of the Sepahr-Danesh collection. It uses vector-based semantic deduplication and a domain-balanced pretraining pipeline to improve corpus quality and reduce redundancy, achieving top performance in extractive question answering and strong results in NER and topic classification, though relation extraction remains a challenge.
IHUBERT: Persian Pretrained Model with Semantic Deduplication
CAT-Translate: Compact Japanese-English Models Outperform Multilingual Ones in Real-World Tasks
CAT-Translate introduces a family of small, open-source models specialized for Japanese-English translation. Using synthetic parallel corpora and a two-stage fine-tuning approach, the models achieve superior performance on real-world benchmarks across business, legal, medical, financial, and patent domains, outperforming large multilingual models in practical applications.
NVIDIA introduces Spatial-IQ, a hierarchical diagnostic framework for multimodal model spatial reasoning
Researchers at NVIDIA have introduced Spatial-IQ, a diagnostic framework designed to deconstruct the spatial intelligence of multimodal large language models (MLLMs). Unlike existing benchmarks that treat models as black boxes, this approach decomposes object counting in stacked 3D structures into nine perceptual and cognitive sub-tasks aligned with human developmental stages.
Coding agent plugin tests show token savings do not equal cost reductions
An analysis of coding agent tasks using GPT-5.6-sol and Codex CLI 0.144.1 demonstrates that reducing context tokens by 90% does not result in proportional cost savings for the complete task.
CSB dataset reveals MLLMs eliminate gap in complex social behavior scene description accuracy
This study introduces the Complex Social Behavior (CSB) dataset, containing 100 images of complex social interactions, to evaluate vision-language models over a decade (2017-2025). The research analyzes five visual-cognitive error types across four pre-Multimodal Large Language Models (MLLMs) and five MLLMs.
MedRealMM: Real-World Multimodal Benchmark for Chinese Online Medical Consultation
Researchers introduce MedRealMM, a large-scale benchmark for multimodal online medical consultation constructed from de-identified patient-doctor interactions collected from a nationwide Chinese internet hospital. The dataset utilizes a Multimodal Clinical Challenge Point extraction framework to convert authentic consultation trajectories into standardized next-response generation tasks paired with physician-refined rubrics.