OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy.
Meet the startup helping Wall Street put a price on AI compute
The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or f...
In 2021, Maersk, the world’s largest container shipping company, deployed AI agents from a startup called Pactum to negotiate freight lane contracts with its carrier suppliers. The objective was for AI agents to handle negotiations autonomously rather than merely support human procurement staff. Ope...
Amazon makes its AI-powered Alexa+ free on Fire TV, no Prime required
Amazon is making its AI-powered Alexa+ assistant free on all compatible Fire TV devices in the U.S., automatically upgrading users whether or not they subscribe to Prime.
VentureBeat names Rob Strechay as its first Lead Analyst, expanding its enterprise AI research push
Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a founding analyst of VentureBeat Research. His arrival is the next step in a deliberate move at VentureBeat toward deeper specialization: analysis built for...
Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision
Conceptual overview and walkthrough of a solution approach in Python
The post Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision appeared first on Towards Data Science.
The latest round of restrictions and safeguards for frontier models are overly fussy and limiting. A Claude skill that I created demonstrates what happens when guardrails go astray. My skill helps me to find articles and blog posts that go into O’Reilly Radar’s monthly Trends to Watch. It reads roug...
Detecting and Discriminating Operator Misspecification in Hybrid PDE-Parameter Learning: a Reference-Free Instrument, with Discrimination Bounded In Sample
arXiv:2608.16925v1 Announce Type: new
Abstract: We build an instrument that reads, from a single fit and with no oracle, whether the operator a hybrid PDE-parameter estimator postulates is wrong-and separates that from a merely unidentifiable parameter. On one self-adjoint parabolic inverse problem...
Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
arXiv:2608.16926v1 Announce Type: new
Abstract: Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance. However, existing methods usually treat data value as a relatively sta...
Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
arXiv:2608.16927v1 Announce Type: new
Abstract: As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Existing methods often measure diversity directly in the original embeddi...
The Problem Is the Problem: Towards Scalable Mathematical Discovery
arXiv:2608.16977v1 Announce Type: new
Abstract: AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well...
The Price of Thinking: Reasoning Effort as a Model-Specific API Contract
arXiv:2608.16956v1 Announce Type: new
Abstract: API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omission, output rail, service product, prompt, and price schedule. We study the reasoning-effort term thr...
Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution
arXiv:2608.16891v1 Announce Type: new
Abstract: Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from harmful text generation to harmful operational side effects. Prompt-level governance can shape mod...
GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents
arXiv:2608.16890v1 Announce Type: new
Abstract: Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissions, yet LLM-based code generation fails catastrophically on this task: across 11 single-shot attemp...
Benchmarking Classical and Transformer-Based Models for Document Sensitivity Classification
arXiv:2608.16928v1 Announce Type: new
Abstract: Automatic sensitivity classification of organizational documents is a critical yet underserved problem, where the consequences of misclassification range from regulatory violations to security breaches. While AI-based approaches offer a scalable alter...
The P-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs
Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often compile into deeply nested, non-monotonic Boolean queries over text fields. However, standard query evaluation strategies over inverted indices face severe theore...
Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts
Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries. Despite their prevalence, researchers and practitioners lack methods and empirical insig...
Tiny robots powered by light can hunt down and collect bacteria
Researchers have built microscopic, light-driven robots that can rapidly navigate through liquid, collect bacteria, and deposit them in chosen locations. These tiny “cleaners” could open new possibilities for manipulating cells and microbes with remarkable precision.
Strengthening Democratic Oversight in National Security
OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, and expertise.