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Researchers developed the Heterogeneous Block Covariance Model (HBCM) and an associated Variational Expectation-Maximization (VEM) algorithm for community detection among features based on their covariance structure. This model explicitly accounts for heterogeneous feature characteristics and achieves superior accuracy and computational efficiency compared to existing methods across diverse simulation and real-world datasets, including single-cell RNA-seq and stock prices.

Researchers at Scale AI introduce OnlineRubrics, a framework that dynamically updates evaluation criteria during Large Language Model (LLM) training through online elicitation from pairwise comparisons. This approach leads to more robust and higher-performing LLMs by mitigating reward hacking and adapting to emergent behaviors and desired qualities.

Researchers from the University of Arizona, Microsoft Research Montréal, and the Allen Institute for AI introduce SCIENCEWORLD, a new interactive text environment designed to test AI agents' grounded scientific reasoning abilities. State-of-the-art models achieved low scores on these elementary science tasks, suggesting that their success on static question-answering benchmarks does not translate to robust procedural understanding in dynamic environments.

Researchers at AWS AI Labs developed METASYNTH, a meta-prompting-driven agentic framework for generating diverse synthetic data. The method enables significant domain adaptation for large language models, such as improving Mistral-7B-v0.3's performance by up to 13.75% in the biomedicine domain using only 25 million tokens of synthetic data, while maintaining general capabilities and demonstrating superior data diversity compared to traditional template-based approaches.

A survey by researchers from the University of Oregon, Carnegie Mellon University, Adobe Research, and Meta AI provides the first dedicated examination of Small Language Models (SLMs). The work introduces a structured taxonomy and outlines key techniques, applications, and challenges in balancing model performance with practical deployment considerations.

G¨odel Agent is a self-referential agent framework designed to achieve recursive self-improvement by enabling an agent to dynamically modify its own policy and meta-learning algorithm at runtime. This framework outperforms existing agent paradigms across various tasks and demonstrates efficient self-optimization and adaptability by autonomously generating task-specific optimizations.

Researchers from Michigan State University and the University of Arizona demonstrate a novel "Agent-in-the-Middle" (AiTM) attack that exploits communication channels in Large Language Model-based Multi-Agent Systems (LLM-MAS). The attack, which intercepts and manipulates inter-agent messages, achieves high success rates (frequently above 70%) in inducing malicious behaviors or denial-of-service across various LLM-MAS frameworks and real-world applications.

This extensive survey provides a structured overview of alignment and safety in Large Language Models (LLMs), analyzing training paradigms, safety mechanisms, and emerging challenges. It synthesizes current research, identifies industry practices, and outlines open problems to ensure LLMs align with human values and intentions.

Researchers performed the first direct, dynamical measurement of a black hole mass in a 'Little Red Dot' (Abell2744-QSO1) at z=7.04 using JWST NIRSpec IFS data, confirming the validity of single-epoch virial mass estimates for these early universe objects and revealing a black hole significantly overmassive relative to its host galaxy's stellar mass.

Researchers from Clemson, Arizona State, Washington University in St. Louis, Notre Dame, and Arizona developed Diversity-aware Reward Adjustment (DRA) to explicitly integrate semantic diversity into reward computation for R1-Zero-like training of large language models. This approach achieved a state-of-the-art average accuracy of 58.2% on mathematical reasoning benchmarks using a 1.5B parameter model with minimal fine-tuning data (7,000 samples) and low training costs.

A scalable data selection method for pretraining large language models balances data quality and diversity, improving zero-shot accuracy by 1.39% across downstream tasks while maintaining computational efficiency. The approach integrates a Multi-Arm Bandit framework for cluster-based sampling with an enhanced, accelerated influence function that accounts for Transformer attention layers.

This research introduces Multimodal Symbolic Logical Reasoning (MuSLR), a new task and benchmark, MuSLR-Bench, that requires vision-language models (VLMs) to perform formal logical deduction by integrating information from both visual and textual inputs. The proposed LogiCAM framework, developed by the National University of Singapore and collaborators, achieved a 14.13% average accuracy improvement on GPT-4.1, demonstrating enhanced capabilities in applying formal logic to complex multimodal scenarios.