arXiv AI July 15, 2026 By Bryan Kelly, Semyon Malamud, Johannes Schwab, Teng Andrea Xu

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.

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arXiv Computation and Language

6d ago

PALM: Point-in-Time Adaptation for Financial Language Models

arXiv:2609.30316v1 Announce Type: cross Abstract: Language models used in financial backtests suffer from look-ahead bias, as a model trained on text published after the study period has already obse...

By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn

llms safety

arXiv AI

Jun 3

Pretraining Language Models on Historical Text

arXiv:2606. 02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.

By Xiaoxi Luo, Zachary Shinnick, Niclas Griesshaber, Yixuan Wang, Junchi Yu, Freda Shi, Philip Torr, Yao Lu

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jul 23

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

arXiv:2607. 19604v1 Announce Type: cross Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge.

By Nischay Dhankhar, Dos Baha, Abulhair Saparov

llms fine-tuning

arXiv AI

Aug 25

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng

llms rag agents benchmarks

arXiv AI

Aug 26

Relative Time Intervals Representation for Word-level Timestamping with Masked Training

The paper introduces a method for improving fine-grained, temporally aligned outputs in Speech Large Language Models (SpeechLLMs) by replacing absolute timestamps with relative timestamps, which reduces vocabulary size and enhances generalization. It proposes a hybrid fine‑tuning strategy that fully fine‑tunes the timestamp‑augmented embedding layer and language model head while applying LoRA to decoder layers, and introduces a masked timestamp training objective to prevent over‑reliance on ground‑truth timestamps. Experiments show significant gains in timestamp prediction accuracy without compromising transcription quality.

By Quanwei Tang, Zhiyu Tang, Xu Li, Dong Zhang, Shoushan, Guodong Zhou

llms rag fine-tuning

arXiv AI

2d ago

Closing the Loop: Practical Training Recipes for Looped Language Models

arXiv:2610.00673v1 Announce Type: cross Abstract: Looped language models increase effective depth by repeatedly applying a shared block of layers, but existing large-scale recipes require multi-stage...

By Andrei Marchenko, Viacheslav Bezrukov, Oleg Kashurin, Inessa Fedorova, Dmitry Bocharov, Yuliana Shakhvalieva, Maria Tikhonova, Valerii Ternovskii

llms benchmarks

One email a morning, machine-written

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PALM: Point-in-Time Adaptation for Financial Language Models

Pretraining Language Models on Historical Text

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

Relative Time Intervals Representation for Word-level Timestamping with Masked Training

Closing the Loop: Practical Training Recipes for Looped Language Models