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...
Hugging Face hack could indicate cultural issues at OpenAI
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging F...
GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration.
The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery wit...
Your LLM Can Return Perfect JSON and Still Be Wrong
What I learned after thinking more carefully about Structured Outputs on messy, incomplete data
The post Your LLM Can Return Perfect JSON and Still Be Wrong appeared first on Towards Data Science.
Nvidia’s $3.5B MediaTek bet reveals its plan for tackling Big Tech’s AI chip buildout
Nvidia invests $3.5 billion into Taiwanese chipmaker MediaTek. The deal shows how Nvidia plans to stay essential to AI infrastructure as Big Tech begins to build its own AI chips.
A “quantum bath” puts quantum entanglement on autopilot
Physicists have demonstrated a new way to entangle distant quantum bits without the constant measurements and active control normally required. The team created a “quantum bath,” a shared environment filled with correlated microwave photons that automatically pushes separated qubits into an entangle...
Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline
In this article, you will learn how to build a unified scikit-learn pipeline that combines text embeddings generated by a lightweight open-source language model with...
AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production
The five MLOps monitoring assumptions agents break, and which inherited signals now pass failed runs as healthy.
The post AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production appeared first on Towards Data Science.
Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap
arXiv:2608.27512v1 Announce Type: new
Abstract: Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluati...
arXiv:2608.27513v1 Announce Type: new
Abstract: Softmax attention stores key and value vectors for every preceding token, causing inference memory to grow with sequence length. Recent language models incorporating Gated DeltaNet (GDN) or Kimi Delta Attention (KDA) reduce this cost by replacing the ...
When Muon Meets Task Interference: A Spectral Perspective on Continual Learning and Model Merging
arXiv:2608.27518v1 Announce Type: new
Abstract: Continual learning (CL) and model merging (MM) both aim to obtain a single model that performs well across multiple tasks, challenged respectively by catastrophic forgetting and weight-disentanglement error. In the literature, these difficulties are m...