PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory

Google has introduced a new benchmark called 'FACTS' aimed at measuring the factual accuracy of generative AI models, addressing a critical gap in existing benchmarks that focus primarily on task completion rather than the truthfulness of the information generated. This initiative is particularly significant for industries where accuracy is essential, such as legal, finance, and medical sectors.

Recent evaluations of advanced reasoning models on mock Chartered Financial Analyst (CFA) exams have shown impressive results, with models like Gemini 3.0 Pro achieving a record score of 97.6% on Level I. This study involved 980 questions across three levels of the CFA exams, and most models successfully passed all levels, indicating a significant improvement in their performance compared to previous assessments of large language models (LLMs).

A new dataset for Automatic Essay Scoring (AES) and feedback generation in Basque has been introduced, consisting of 3,200 essays annotated by experts. This dataset targets the CEFR C1 proficiency level and includes detailed feedback on various scoring criteria. The study demonstrates that fine-tuning open-source models like Latxa can outperform established systems such as GPT-5 in scoring consistency and feedback quality.

A recent study has demonstrated the potential of large language model (LLM) agents to automate high energy physics data analysis, specifically using the Higgs boson diphoton cross-section measurement as a case study. This hybrid system integrates an LLM-based supervisor-coder agent with the Snakemake workflow manager, allowing for autonomous code generation and execution while ensuring reproducibility and determinism.

A new framework named ReasonBreak has been introduced to address privacy concerns associated with multi-modal large reasoning models (MLRMs), which can infer precise geographic locations from personal images using hierarchical reasoning. This framework employs concept-aware perturbations to disrupt the reasoning processes of MLRMs, aiming to enhance geographic privacy protection.

OpenAI has launched upgrades to its GPT-5 model, introducing GPT-5.1 Instant for improved instruction following, GPT-5.1 Thinking for faster reasoning, and GPT-5.1-Codex-Max for enhanced coding capabilities. These updates aim to enhance user interaction and response quality in AI applications.

The Native Parallel Reasoner (NPR) has been introduced as a teacher-free framework that enhances Large Language Models (LLMs) by enabling them to develop genuine parallel reasoning capabilities. This is achieved through a self-distilled training paradigm, a Parallel-Aware Policy Optimization algorithm, and a robust NPR Engine, resulting in significant performance improvements and faster inference speeds across various reasoning benchmarks.