#data-science
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Decoupling Proposal and Judgment in AI-Driven Investment Research
To prevent false discoveries in AI-driven factor mining, researchers must separate the agent's proposal role from a frozen, anytime-valid statistical referee that judges performance based solely on future market outcomes.
Solving the Robotics Data Bottleneck via Action-Based Video Search
Robotics training is constrained by a lack of high-quality, naturalistic video data. By shifting from keyword-based scraping to action-based video indexing, developers can filter out noise and access billions of hours of real-world physics and behavior.
AI EngineerBuilding Reliable AI Agents: The Data-First Approach
Moving from RAG to agentic workflows requires treating document processing as a multi-step pipeline where data quality, structured representation, and agentic harnesses are critical to preventing compounding errors.
AI EngineerThe Shift from Data Labeling to Data-as-a-Service
Snorkel AI reached a $3.5B valuation by pivoting from automated labeling software to a 'data-as-a-service' model, providing synthetic and expert-curated datasets to meet the massive demand for high-quality AI training data.
NeMo Data Designer: Framework for Multimodal Synthetic Data
NeMo Data Designer provides an extensible, modular framework for generating high-quality synthetic data across multiple modalities, addressing the critical bottleneck of data scarcity in training large-scale AI models.
Making Global Data AI-Ready: The UN System Data Commons
The UN is migrating its global statistics to a new platform built on Google's Data Commons to improve AI accuracy and enable direct data retrieval via the Model Context Protocol (MCP).
Ethical and Privacy Risks in LLM-Enabled GeoAI
Integrating LLMs into Geographic Information Systems (GIS) introduces unique ethical and privacy risks, requiring a shift toward governance-aware autonomous systems to prevent data misuse and spatial bias.
Scaling Legal AI: From Database Thrashing to Object Storage
Legora moved from sharded Postgres to an object-storage-native search architecture (Turbopuffer) to solve cache thrashing, achieve multi-tenant isolation, and support massive legal datasets at a fraction of the cost.
AI EngineerScaling Agreement Data Extraction with Purpose-Built Small Models
Docusign and NVIDIA solved the 'unqueryable agreement' problem by replacing generic LLMs with a 900M-parameter purpose-built vision language model, achieving 20x faster table extraction and significantly lower latency.
Democratizing Data Analysis with ChatGPT Work's Data Agent
OpenAI's new Data agent for ChatGPT Work allows non-technical users to query enterprise data, generate interactive dashboards, and trigger actions using natural language, while maintaining strict administrative governance.
Optimizing Business Processes with Control-Flow Uncertainty
This paper introduces a mathematical framework for scheduling business processes where the execution path is uncertain, using stochastic optimization to balance resource allocation and process completion time.
SciLitBench: Evaluating LLMs for Systematic Literature Reviews
SciLitBench provides a standardized benchmark and design framework for evaluating how LLMs perform in systematic literature reviews, identifying critical gaps in reasoning and evidence extraction for scientific research.
XDOF Reaches $1.2B Valuation by Solving Robot Data Bottlenecks
XDOF, a startup providing teleoperation data for training general-purpose robots, is nearing a $1.2B valuation just three months after its Series A, driven by $50M in annualized revenue and high demand from AI labs.
Connecting EHR and Public Data to ChatGPT for Healthcare
OpenAI has launched an Epic EHR integration and a Healthcare Public Data plugin for ChatGPT, allowing clinical teams to synthesize patient records with nine authoritative medical sources in a HIPAA-compliant, governed workspace.
Moving from Reactive Queries to Proactive Enterprise Analytics
Enterprise analytics should shift from a 'question-first' reactive model to an 'analyst-first' approach that leverages domain-expert skills and verified knowledge compilation to anticipate business needs.
DS-Lighting: Explicit Agent Harnesses for Data Science Automation
DS-Lighting introduces an explicit 'harness' framework to bridge the gap between LLM reasoning and the specialized, multi-step requirements of data science automation, improving reliability in complex analytical workflows.
Digital Sovereignty: Maintaining Control in AI Systems
Digital sovereignty is the ability to maintain control over data, operations, technology, and AI models. Rather than a barrier to innovation, it is an architectural priority that ensures security, trust, and long-term flexibility.
IBM TechnologyReducing LLM Hallucinations with Governed Semantic Definitions
The GROUND framework mitigates LLM hallucinations in enterprise analytics by enforcing a layer of governed semantic definitions, ensuring models query data based on verified business logic rather than raw natural language interpretation.
The 5D Framework for Multi-Table Data Analysis
The 5D framework provides a unified methodology for integrating and reusing complex, multi-table datasets by mapping data across five distinct dimensions to ensure consistency and analytical depth.
Evaluating NL2SQL Performance with ESQ-Bench
ESQ-Bench is a new benchmark designed to test NL2SQL models on dialect generalization and silent semantic divergence, addressing the limitations of existing benchmarks in enterprise environments.
QueryStory: Building Trust in Enterprise AI Analytics
QueryStory is a platform designed to bridge the trust gap in enterprise AI by providing transparent, verifiable data narratives and automated SQL auditing, moving beyond the 'black box' limitations of general-purpose AI agents.
Composable Trust Infrastructure for Manufacturing Knowledge Graphs
This paper proposes a framework for integrating cross-system provenance, temporal reasoning, and decision traceability into manufacturing knowledge graphs to ensure reliable AI-driven industrial operations.
Bridging SQL and Vector Data with Agentic Workflows
Digital librarian AI agents solve the 'what vs. why' data gap by orchestrating queries across structured SQL databases and unstructured vector databases to provide grounded, context-aware answers.
IBM TechnologyScientific Data Skills: Enabling Agent-Ready Data Services
To make scientific data usable by AI agents at scale, data services must move beyond simple APIs and adopt 'Scientific Data Skills'—standardized, machine-interpretable interfaces that allow agents to discover, query, and manipulate complex datasets autonomously.
Generating Synthetic Medical Data via Reverse Inference
When real-world data is too sensitive or restricted to retain, you can generate high-fidelity synthetic datasets by reversing your inference workflow: sample a label, derive a reasoning trace, and reconstruct the source documents.
AI EngineerAgentic Frameworks for Document Layout Analysis in Plant Science
A hybrid approach combining deterministic rules with LLM-based agents to accurately embed and annotate complex, layout-heavy scientific documents.
Training Krea 2: Data-Centric Generative Model Development
Krea 2 prioritizes stylistic diversity and fast iteration over the 'average' consistency of production models, using a data-heavy pipeline that treats model architecture as secondary to high-quality, filtered, and diverse training data.
AI EngineerSemPlan: A Benchmark for Structured Semantic Planning in Enterprise Data
SemPlan introduces a rigorous framework for evaluating how LLMs perform structured semantic planning when querying complex enterprise data, addressing the gap between simple RAG and multi-step reasoning.
Building Resilient Web Data Infrastructure for AI
AI systems require live, reliable data pipelines. Success in this space is not about building once, but maintaining an 'adapt forever' architecture that handles extreme scale, latency, and anti-bot measures.
AI EngineerBuilding Production AI: The Data Science & AI Loop
Production-ready AI systems rely on a continuous feedback loop where robust data science pipelines (ETL, governance) feed AI models, and AI, in turn, generates synthetic data to improve those same pipelines.
IBM TechnologyShowing 30 of 159