Researchers from Princeton, Ant Group and Stanford Introduce AQuA: A Two-Part Agentic Framework for Autonomous Factor Discovery and Model Development in Quantitative Finance
Quantitative research agents that write their own experiments can corrupt the evidence they later learn from. A leaky feature that scores well gets stored as a successful precedent and propagated through later iterations. Prompt-level instructions and reviewer agents do not close this, because autho...
Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch
Five failure modes that survive constrained decoding, and why your schema validator will never catch them.
The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch appeared first on Towards Data Science.
Fambot introduces an ‘AI chief of staff’ for families
Fambot is building an AI “chief of staff” to help families manage the emails, calendars, school updates, sports schedules, and other logistics of raising kids.
John Grass | Sr. Manager, EngineeringA Fundamental TransformationAn AI team is fundamentally more than just a group whose members incorporate AI tools into their existing workflows. The journey to becoming an AI team necessitates a fundamental and comprehensive paradigm shift in how the team defines...
Path to Astra: critical capabilities and frontier safeguards
Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, with stronger safeguards for release.
3 Ways to Enhance Your AI Model’s Interpretability
In this article, you will learn three concrete techniques for making machine learning model predictions interpretable, covering both global and local explanations across tree-based and...
ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning
arXiv:2608.28771v1 Announce Type: new
Abstract: Large reasoning models achieve strong performance on complex tasks by generating extended chain-of-thought (CoT) traces via reinforcement learning with verifiable rewards (RLVR). While current RLVR methods have achieved strong results with correctness...
Curvature Cryptanalysis of Smooth Transformer Feed-Forward Networks
arXiv:2608.28843v1 Announce Type: new
Abstract: We show that smooth two-layer feed-forward networks (FFNs) expose an additional structural model extraction channel under a chosen-input raw-output oracle at the FFN branch; consider transformer FFN branches with GELU or SiLU activations under chosen-...
Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
arXiv:2608.28853v1 Announce Type: new
Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how vector information can be transformed as it moves across a graph. We introduce \textsc{ESNN}, an Equiv...
The Halt Vector: Internalizing a Causal Steering Intervention for Efficient Reasoning
arXiv:2608.28859v1 Announce Type: new
Abstract: Reasoning models do not stop when they know the answer. On DeepSeek-R1-Distill-Qwen-7B the chain of thought runs about twice as long as the model's own answer probability takes to settle, and how much of that excess is removable varies from problem to...
DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation
arXiv:2608.28590v1 Announce Type: new
Abstract: Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the agent harness that represents tasks, manages execution state, constrains output artifacts, and provi...
arXiv:2608.28591v1 Announce Type: new
Abstract: Recent advancements in AI are helping scientists achieve breakthroughs in fields such as mathematics, medicine, and materials sciences. New evaluation datasets for AI models contribute to such advancement in AI. In the STEM domain, frontier models hav...
Statutory AI: Aligning Large Language Models With Legal Norms
arXiv:2608.28593v1 Announce Type: new
Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in accordance with legal and ethical standards has become a critical priority. Existing proposals for A...
From Question-First to Analyst-First: Domain-Expert Skills and Verified Knowledge Compilation for Proactive Enterprise Analytics
arXiv:2608.28594v1 Announce Type: new
Abstract: Conversational analytics systems assume the user already has a well-formed question, leaving a non-expert facing a blank query box on an unfamiliar enterprise schema. Commercial 'proactive' tools narrow this gap only by detecting statistical anomalies...
Gradium AI Releases New Default TTS Model: 81.0% Hard-Case Pass Rate at 216 ms Time-to-First-Audio
Speed and accuracy usually pull against each other in text-to-speech. Gradium AI's new default model reports both: an 81.0% human-rated pass rate on 500 hard sentences across five languages, at 216 ms P50 time-to-first-audio on Coval. The evaluation set is open on Hugging Face under CC BY 4.0.
The p...
AI Weekly Issue #528: What are companies building with AI? An Applied AI Deep Dive
We went looking for what companies are actually building with AI. The answer was not more chatbots. It was drones carrying diagnostic samples, driverless Frito-Lay trucks, AI-guided flight paths, repair copilots, and rugged GPU laptops in Ukraine. We reviewed 136 use cases from the last 20 days. The...
Keenable AI Open-Sources NEEDLE: A Live Search Benchmark That Rebuilds Its Query Set Every Hour
How do you benchmark a web search API when the thing being tested can read the answer key? A search agent has a fetch tool. If the gold labels sit in a public dataset, the agent can download them mid-evaluation and skip retrieval entirely. A similar problem arises when the answers are already encode...
Google AI Releases TimesFM-3: A 330M Parameter Zero-Shot Foundation Model For Multivariate Time Series Forecasting
Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting multiple targets, past covariat...