#data-science
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Reducing 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 TechnologyNL2SHACL-Bench: Evaluating LLM Performance on SHACL Generation
NL2SHACL-Bench provides a standardized benchmark suite to evaluate how effectively Large Language Models can translate natural language requirements into SHACL (Shapes Constraint Language) for RDF data validation.
Global AI Trends: From Information Seeking to Task Execution
New data from OpenAI Signals reveals that ChatGPT usage is shifting from exploratory 'asking' to productive 'doing,' particularly in professional settings, with rapid adoption growth in Latin America, Africa, and among users over 35.
The Missing Data Layer in AI Systems
Current AI architectures lack a dedicated, standardized data layer, leading to fragmented pipelines; the proposed solution involves a unified abstraction for data management that bridges the gap between raw storage and model inference.
Scaling AI Weather Forecasting: The WindBorne Strategy
WindBorne Systems raised $37M to scale its proprietary weather-sensing balloon network and AI forecasting models, aiming to bridge the gap between high-fidelity data and commercial business decision-making.
Scaling Human Feedback for AI Model Evaluation
DesignArena, a platform for crowdsourced human evaluation of generative AI, has raised $7.9M to provide frontier labs with high-quality preference data, currently generating $60M in ARR.
AlphaSchema: Semantic Frameworks for LLM-Driven Alpha Mining
AlphaSchema introduces a structured semantic framework to improve how LLMs generate and evaluate quantitative trading signals (alphas), moving beyond unstructured prompt engineering to systematic search spaces.
UrbanDS: Graph-Guided Multi-Agent Systems for Urban Data
UrbanDS improves LLM performance on complex urban data tasks by using a graph-guided multi-agent architecture that structures reasoning and data retrieval.
ClinLens: Long-Horizon Coding Agents for Clinical Data Science
ClinLens is an AI agent framework designed to handle the complexities of longitudinal, multimodal clinical data by automating long-horizon coding tasks in data science workflows.
Data Quality as a Compute Multiplier
Data quality is the most underinvested lever in model training. By curating for signal-per-token rather than raw volume, builders can achieve frontier-level performance with significantly less compute, effectively bending scaling laws.
AI EngineerData Curation Strategies for Post-Training LLMs and Agents
Reliability in autonomous agents is achieved through disciplined data and environment curation rather than just compute, utilizing techniques like multi-answer sampling and targeted SFT.
Building Verifiable AI Benchmarks for Biology
To make AI reliable for biological research, we must move beyond Q&A models and build verifiable, task-based benchmarks that force models to reason through raw experimental data, not just memorize scientific literature.
Unified Semantic Modeling for Large-Scale Job Understanding
LinkedIn's framework addresses the challenge of large-scale job understanding by implementing a unified semantic model that maps diverse, unstructured job data into a standardized, machine-readable format.
LLMs vs. Corpora for Specialized Terminology Extraction
While LLMs offer a flexible alternative to traditional corpus-based methods for extracting specialized terminology, they remain prone to hallucinations and lack the verifiable grounding of static corpora, making them best suited as assistants rather than replacements.
Right-sizing Cloud Workloads with Conformal Prediction
The RSR framework uses conformal prediction to provide statistically rigorous, uncertainty-aware resource recommendations for virtual machines, balancing cost-efficiency with performance guarantees.
Grounding AI in Outcomes: Why Context Isn't Experience
Off-the-shelf LLMs suffer from the 'fluent bluff'—they provide confident but often harmful financial advice because they lack real-world experience. The solution is grounding models in proprietary state-action-outcome data.
AI EngineerSchema-Aware Localisation (SAL) for NL2SQL Reliability
Schema-Aware Localisation (SAL) improves NL2SQL accuracy by grounding natural language queries directly against database schemas in real-time, effectively mitigating hallucinations and invalid SQL generation.
Manufacturing Physical AI Data: Beyond Simple Video Annotation
Physical AI models face a critical data scarcity bottleneck. Companies like Encord are moving beyond passive video collection to 'manufacturing' high-fidelity training data using brain-wave sensors, EMG arm sensors, and dense physical annotations.
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