40 Advanced SQL Window Functions Every Data Scientist Must Know(with examples)
In the world of data science, SQL still remains the powerful tool for defining the data, data manipulation, data aggregation and data analysis. While basic SQL commands are very fundamental, and everyone knows about it. If you want to be the unique in the crowd then you should know advanced features...
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at G...
Elon Musk has lost his lawsuit against Sam Altman and OpenAI
Elon Musk's claim that he was mistreated by his OpenAI cofounders failed after nine California jurors decided in a unanimous verdict that his lawsuits had been filed too late.
Six Choices Every AI Engineer Has to Make (and Nobody Teaches)
The production trade-offs that only appear once your model is live.
The post Six Choices Every AI Engineer Has to Make (and Nobody Teaches) appeared first on Towards Data Science.
Why MCP servers keep losing to CLIs once the agent gets a terminal
The post One Flexible Tool Beats a Hundred Dedicated Ones appeared first on Towards Data Science.
I have been running local models as part of my daily workflow for some time, and what surprised me most is how often local turned out to be the better choice, not a compromise.
Agent Skills Work but the Research Shows Most Teams Are Building Them Wrong
This post was originally published on The Nuanced Perspective and is being reposted here with the authors’ permission. Agent skills are everywhere right now. Atlassian built them into Rovo so agents can automatically triage Jira tickets, draft Confluence pages, and route service requests without any...
OpenAI and Dell partner to bring Codex to hybrid and on-premise enterprise environments
OpenAI and Dell partner to bring Codex to hybrid and on-premise environments, helping enterprises deploy AI coding agents securely across data and workflows.
NVIDIA Introduces a 4-Bit Pretraining Methodology Using NVFP4, Validated on a 12B Hybrid Mamba-Transformer at 10T Token Horizon
NVIDIA introduces a 4-bit pretraining methodology built around the NVFP4 microscaling format — combining selective BF16 layers, 16×16 Random Hadamard Transforms on Wgrad inputs, 2D weight scaling, and stochastic rounding on gradients — validated on a 12B hybrid Mamba-Transformer trained on 10 trilli...
AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
arXiv:2605.15206v1 Announce Type: new
Abstract: Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote, cloud-based agents offer scalability and ease of deployment, they raise...
TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination
arXiv:2605.15207v1 Announce Type: new
Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mode in sequential fine-tuning of shared-context teams: updating one agent...
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
arXiv:2605.15208v1 Announce Type: new
Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this compression on model quality remains poorly understood. Existing studies...
Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation
arXiv:2605.15231v1 Announce Type: new
Abstract: Nonlinear finite element crash simulations are accurate but computationally expensive, limiting their use in iterative design optimisation. Machine-learning surrogate models based on graph neural networks (GNNs) offer a faster alternative. Message-pas...
MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
arXiv:2605.15235v1 Announce Type: new
Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modality missing, where an entire channel is absent, and within-modality miss...
DeepSlide: From Artifacts to Presentation Delivery
arXiv:2605.15202v1 Announce Type: new
Abstract: Presentations are a primary medium for scholarly communication, yet most AI slide generators optimize the artifact (a visually plausible deck) while under-optimizing the delivery process (pacing, narrative, and presentation preparation). We present De...
SDOF: Taming the Alignment Tax in Multi-Agent Orchestration with State-Constrained Dispatch
arXiv:2605.15204v1 Announce Type: new
Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage constraints that govern real business processes. We present SDOF, a framework that treats multi-agent ...
Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations
arXiv:2605.15205v1 Announce Type: new
Abstract: Improving the Theory of Mind (ToM) capability of Large Language Models (LLMs) is crucial for effective social interactions between these AI models and humans. However, the existing benchmarks often measure ToM capability improvement through story-read...
Fair outputs, Biased Internals: Causal Potency and Asymmetry of Latent Bias in LLMs for High-Stakes Decisions
arXiv:2605.15217v1 Announce Type: new
Abstract: Instruction-tuned language models exhibit behavioural fairness in high-stakes decisions while retaining biased associations in their internal representations. However, whether these suppressed representations can affect model outputs - and whether suc...