arXiv AI
Jun 6
Data Flow Control: Data Safety Policies for AI Agents
arXiv:2606. 05679v1 Announce Type: cross Abstract: Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users.
Data engineering
Pipelines, warehouses, feature stores and the query engines that feed everything above.
arXiv AI
Jun 6
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
arXiv:2604. 00555v5 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level.
arXiv Machine Learning
Jun 5
No Need to Train Your RDB Foundation Model
arXiv:2602. 13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes.
Hugging Face Trending Papers
Jun 4
Data Flow Control: Data Safety Policies for AI Agents
Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness, correctness is not safety.
arXiv Machine Learning
Jun 4
RIDE: An Open Dataset and Benchmark for Train Delay Prediction
arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.
arXiv AI
Jun 4
QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples
arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.
arXiv AI
Jun 4
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
arXiv:2505. 11166v3 Announce Type: replace-cross Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primarily due to insufficient long-context alignment caused by data quality issues, training inefficiencies, and the lack of well-designed optimization objectives.
arXiv AI
Jun 3
Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines
arXiv:2605. 25645v2 Announce Type: replace-cross Abstract: We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison of TPU and GPU platforms for large language model adaptation.
arXiv AI
Jun 3
SegTune: Structured and Fine-Grained Control for Song Generation
arXiv:2606. 02638v1 Announce Type: cross Abstract: Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts.
arXiv AI
Jun 3
Pretraining Language Models on Historical Text
arXiv:2606. 02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.
arXiv Machine Learning
Jun 3
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
arXiv:2606. 03946v1 Announce Type: cross Abstract: Database vendors recently released AI functions that can be used in filter predicates.
arXiv Machine Learning
Jun 3
Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing
arXiv:2605. 25902v2 Announce Type: replace Abstract: Narrowly finetuned language models memorize implanted content verbatim, but auditing what a deployed model has been taught, without access to its weights or training data, remains an open challenge.
arXiv AI
Jun 3
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
arXiv:2606. 03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions.
arXiv AI
Jun 3
TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
arXiv:2509. 09685v5 Announce Type: replace-cross Abstract: We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline.
arXiv AI
Jun 3
Towards Robust Sequential Decomposition for Complex Image Editing
arXiv:2605. 09233v2 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions.
Towards Data Science
Jun 2
Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn
Exploratory data analysis on the US Census Dataset The post Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn appeared first on Towards Data Science .
arXiv AI
Jun 2
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
arXiv:2606. 02433v1 Announce Type: cross Abstract: The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical prediction.
arXiv AI
Jun 2
BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning
arXiv:2606. 02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks.
arXiv AI
Jun 2
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).
arXiv AI
Jun 2
SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback
arXiv:2606. 01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors.