arXiv AI

LOB-ID: Evaluating Synthetic Market Data by Inception Distances

arXiv:2608. 13082v1 Announce Type: cross Abstract: Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics.

arXiv AI

Jul 15

Scaling Point-in-Time Language Models

arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.

By Bryan Kelly, Semyon Malamud, Johannes Schwab, Teng Andrea Xu

llms fine-tuning benchmarks safety

arXiv AI

Aug 25

Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings

arXiv:2507.12295v2 Announce Type: replace-cross Abstract: Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation id...

By Feng Xiao, Jicong Fan

llms rag nlp benchmarks

arXiv Machine Learning

Aug 14

Incremental Evaluation and Training in Relational Deep Learning

arXiv:2608. 13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning.

By Jakub Pele\v{s}ka, Gustav \v{S}\'ir

fine-tuning benchmarks

arXiv Machine Learning

Aug 13

Task- and dataset-specific information in protein language models

arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.

By Roman Joeres, Ilya Senatorov, Olga V. Kalinina

llms rag nlp

arXiv AI

Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

arXiv Machine Learning

Jul 9

From Jumps to Signatures: a Generative Method for Temporal Point Processes

arXiv:2607. 06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions.

By Niels Cariou-Kotlarek, Vasileios Lampos

rag

arXiv Machine Learning

Jun 4

Generating Financial Time Series by Matching Random Convolutional Features

arXiv:2606. 05138v1 Announce Type: new Abstract: Generating realistic financial time series is challenging as training data is often limited to a single historical path.

By Konrad J. Mueller, Nikita Zozoulenko, Ben Wood, Thomas Cass, Lukas Gonon

diffusion safety

arXiv AI

Jun 10

Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune

arXiv:2606. 10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs.

By Wu Yuerong, Mingni Luo

llms rag fine-tuning

arXiv AI

Aug 12

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

arXiv:2602. 17162v3 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature".

By Ariel Larey, Elay Dahan, Amit Bleiweiss, Raizy Kellerman, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini, Gideon Rechavi, Nicole Bussola, Simon Lee, Shane O'Connell, Dung Hoang, Marissa Wirth, Alexander W. Charney, Nati Daniel, Yoli Shavit

rag diffusion benchmarks safety

arXiv AI

Aug 26

From Gradient-Boosted Trees to Deep Recommenders: Practical Lessons from Migrating a Production Customer Support Recommender

The paper documents the migration of a live conversational recommendation system from a gradient‑boosted multiclass model to a pairwise‑binary deep recommender. It explains how reformulating the task, using negative sampling, noise injection, and attention pooling over transcript chunks enabled the new model to handle dynamic, multimodal data and long conversation context. The authors compare several architectures and loss functions, showing that the deep recommender matches or surpasses the CatBoost baseline, especially in later conversational stages.

By Sonia Sharma, Jeyendran Balakrishnan, Shreya Rajpal, Swapnil Parekh, Nagaraj Janardhana, Andrew Mattarella-Micke

rag agents multimodal benchmarks

arXiv AI

Jun 3

QuITE: Query-Based Irregular Time Series Embedding

arXiv:2605. 28166v3 Announce Type: replace-cross Abstract: Irregular Multivariate Time Series (IMTS) are common in practice, yet their irregular sampling complicates effective modeling.

By Junghoon Lim

rag benchmarks

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