AI

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

Researchers introduce DOVE, a distributional evaluation framework that measures how well large language models align with cultural values through open-ended text generation rather than multiple-choice tests. The framework uses rate-distortion optimization to create a value codebook and unbalanced optimal transport to assess alignment, demonstrating 31.56% correlation with downstream tasks across 12 LLMs while requiring only 500 samples per culture.

The Human Condition as Reflected in Contemporary Large Language Models

A research study analyzes six leading large language models to identify shared cultural patterns revealed in their training data, finding consensus around themes like narrative meaning-making, status competition, and moral rationalization. The findings suggest LLMs function as 'cultural condensates' that compress how humans describe and contest their social lives across massive text datasets.

Governing frontier general-purpose AI in the public sector: adaptive risk management and policy capacity under uncertainty through 2030

A research paper proposes adaptive risk management frameworks for governing frontier AI in public sectors through 2030, arguing that static compliance models are insufficient given rapid capability advancement and incomplete knowledge of AI harms. The work emphasizes that effective governance requires organizational redesign, stronger policy capacity, and scenario-aware regulation rather than purely technical solutions.

SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams

Researchers introduce SensorPersona, an LLM-based system that continuously extracts user personas from mobile sensor data rather than chat histories, achieving 31.4% higher recall in persona extraction and 85.7% win rate in personalized agent responses. The system processes multimodal sensor streams to infer physical patterns, psychosocial traits, and life experiences across longitudinal data collected from 20 participants over three months.

Neural Computers

Researchers propose Neural Computers (NCs), a new computing paradigm where AI models function as executable runtime environments rather than static predictors. The work demonstrates early NC prototypes using video models that process instructions and user actions to generate screen frames, establishing foundational I/O primitives while identifying significant challenges toward achieving general-purpose Completely Neural Computers (CNCs).

In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads

Researchers investigate in-context learning (ICL) in speech language models, revealing that speaking rate significantly affects model performance and acoustic mimicry, while induction heads play a causal role identical to text-based ICL. The study bridges the gap between text and speech domains by analyzing how models learn from demonstrations in text-to-speech tasks.

ChatGPT for finance teams

The article explores how finance teams leverage ChatGPT to enhance operational efficiency across reporting, data analysis, forecasting, and communication. This represents a growing trend of AI adoption in financial services, enabling teams to automate routine tasks and extract deeper insights from complex datasets.

Our response to the Axios developer tool compromise

OpenAI disclosed and responded to a supply chain attack targeting its Axios developer tool by rotating macOS code signing certificates and updating affected applications. The company confirmed that no user data was compromised in the incident, demonstrating both the vulnerability of developer tools in software ecosystems and the importance of rapid security response protocols.

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