arXiv Machine Learning

Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs

arXiv AI

Aug 11

SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection

arXiv:2603. 22213v2 Announce Type: replace-cross Abstract: While large language models (LLMs) are pretrained on massive amounts of data, their knowledge coverage remains incomplete in specialized, data-scarce domains, motivating extensive efforts to study synthetic data generation for knowledge injection.

By Kexian Tang, Jiani Wang, Shaowen Wang, Kaifeng Lyu

llms reinforcement-learning

arXiv AI

2d ago

KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training

arXiv:2609.00082v1 Announce Type: cross Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources su...

By Meghanadh Pulivarthi, Kushagra Bhushan, Vineet Kumar, Gaurav Pandey, Jaydeep Sen, Dinesh Raghu, Sachindra Joshi, Yatin Nandwani

llms

arXiv AI

Jun 24

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.

By Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen

llms benchmarks

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

Jul 14

PRISM Edit: One Vector for All Temporal Answers

arXiv:2607. 11327v1 Announce Type: cross Abstract: Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not always a replacement.

By Chen Huang (Tsinghua University), Qi Zheng (Tsinghua University), Ruiqin Zheng (ByteDance), Long Zeng (Tsinghua University), Yuantong Xu (ByteDance)

llms benchmarks

arXiv Machine Learning

Jul 31

How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data

arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.

By Joel Niklaus, Atsuki Yamaguchi, Michal \v{S}tef\'anik, Guilherme Penedo, Hynek Kydl\'i\v{c}ek, Elie Bakouch, Lewis Tunstall, Edward Emanuel Beeching, Thibaud Frere, Colin Raffel, Leandro von Werra, Thomas Wolf

llms

arXiv AI

Jul 29

Distilling Temporal Search and Reasoning: Evolving LLMs for Future Prediction via Harness-Assisted Efficient Data Synthesis

arXiv:2607. 25554v1 Announce Type: new Abstract: Future event prediction carries broad social impact yet remains challenging.

By Wanxu Cai, Zhengyu Chen, Huaisheng Zhu, Wei Wang, Jingang Wang, Qiang Xu

llms agents efficiency benchmarks

arXiv AI

Aug 18

Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents

arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.

By Tianxin Wei, Zhan Shi, Minhua Lin, Bing He, Zewen Liu, Yisi Sang, Yuanchen Bei, Xuying Ning, Jiaru Zou, Ting-Wei Li, Xiao Lin, Yanjun Zhao, Chi Wang, Benoit Dumoulin, Dakuo Wang, Jingrui He, Hanqing Lu

llms agents benchmarks

arXiv AI

Jun 9

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

arXiv:2510. 27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms fine-tuning benchmarks

arXiv AI

Aug 13

Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing

arXiv:2608. 11660v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.

By Tianci Liu, Zihan Dong, Tianchun Li, Yi-Chung Chen, Qiming Cao, Xingchen Wang, Shiyang Wang, Zichen Miao, Linjun Zhang, Haoyu Wang, Jing Gao

llms efficiency

arXiv AI

Jun 8

Small Language Model Agents Enable Efficient and High-Quality Knowledge Mining

arXiv:2510. 01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions.

By Sipeng Zhang, Shuhuai Lin, Xinpeng Wei, Yihang Chen, Pin Qian, Su Wang, Huan Xu

llms agents benchmarks

arXiv Machine Learning

4d ago

Aligning Agentic World Models via Knowledgeable Experience Learning

arXiv:2601.13247v2 Announce Type: replace-cross Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...

By Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen

llms agents fine-tuning safety

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