Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Machine Learning

Jul 21

Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data

arXiv:2602. 02025v2 Announce Type: replace-cross Abstract: ML models critically depend on feature quality, yet in real-world settings, useful features are often distributed across multiple relational tables rather than a single dataset.

By Serafeim Papadias, Kostas Patroumpas, Dimitrios Skoutas

llms benchmarks

arXiv Machine Learning

Jul 21

Uncertainty Decomposition for Bayes-Filtered Transformers via Bayesian Predictive Inference

arXiv:2602. 04596v2 Announce Type: replace-cross Abstract: Bayes-filtered transformers are transformers meta-learned on sequences from a prior predictive distribution to approximate the corresponding posterior predictive distribution.

By Sandra Fortini, Kenyon Ng, Sonia Petrone, Judith Rousseau, Susan Wei

llms benchmarks

arXiv AI

Jul 21

Otap:Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories

arXiv:2607. 17082v1 Announce Type: new Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results.

By Babak Barazandeh, Subhabrata Majumdar, George Michailidis

llms agents benchmarks

arXiv AI

Jul 21

Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking

arXiv:2607. 17757v1 Announce Type: cross Abstract: Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments.

By Hye-Jung Yoon, Juno Kim, Yesol Park, Jun-Ki Lee, Byoung-Tak Zhang

llms computer-vision robotics fine-tuning

arXiv AI

Jul 21

High-accuracy Low-Bit KV-Cache Quantization via Local Distribution Restoration

arXiv:2607. 16248v1 Announce Type: cross Abstract: Long-context large language model inference relies on the KV cache to avoid redundant attention computation, but incurs high memory and bandwidth overheads.

By Gradwell Dzikanyanga, Yanqi Pan, Weihao Yang, Donglei Wu, Wen Xia, Hao Huang

llms efficiency

arXiv AI

Jul 21

Trace-Based On-Policy Distillation for Masked Diffusion Language Models

arXiv:2607. 16872v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are a promising alternative to autoregressive generation.

By Haolin Ren, Ziyang Huang, Chenhao Yuan, Jun Zhao, Kang Liu

llms diffusion reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 21

Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration

arXiv:2607. 16848v1 Announce Type: cross Abstract: Long-term memory is becoming a core component of LLM agents, but most memory benchmarks evaluate conversations or compact summaries, while research agents need to restore evidence from full scientific papers.

By Maksim Sheverev, David Finkelstein, Sergey Nikolenko

llms rag agents benchmarks

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 21

CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs

arXiv:2603. 27958v2 Announce Type: replace Abstract: Analogical reasoning tests a fundamental aspect of human cognition: mapping the relation from one pair of objects to another.

By Yongkang Du, Xiaohan Zou, Minhao Cheng, Lu Lin

llms multimodal benchmarks

arXiv AI

Jul 21

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.

By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro

llms agents benchmarks

arXiv AI

Jul 21

Trusting sovereign language models as scientific instruments: evidence from Portugal's AMALIA

arXiv:2607. 08731v2 Announce Type: replace-cross Abstract: National language models are becoming publicly funded epistemic infrastructure.

By Manuel Pita

llms

arXiv AI

Jul 21

Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicators

arXiv:2606. 20918v2 Announce Type: replace-cross Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization.

By Reza Ghanavati, Behrooz Mosallaei

llms benchmarks

arXiv Machine Learning

Jul 21

What does a Bayes-filtered transformer believe? A predictive Monte Carlo approach

arXiv:2607. 17060v1 Announce Type: new Abstract: A Bayes-filtered transformer (BFT) is a transformer trained on sequences that are generated in two steps: first a latent task is drawn from a prior, then observations are drawn conditional on that task.

By Afiq Abdillah Effiezal Aswadi, Haotong Ma, Susan Wei

llms diffusion safety

arXiv Machine Learning

Jul 21

ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning

arXiv:2607. 16323v1 Announce Type: cross Abstract: Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR).

By Alexander Selivanov, Friederike Jungmann, Jan Kehrer, Karl-Ludwig Laugwitz, Eimo Martens, Daniel Rueckert

llms multimodal benchmarks

arXiv Machine Learning

Jul 21

Auditing Invisible Weight Updates with Reference Traces

arXiv:2607. 09800v2 Announce Type: replace Abstract: Master weights and stochastic rounding bypass invisible stored-weight updates but do not locate lost direct-storage proposals or parameters worth protecting.

By Zekai Shang

llms

arXiv Machine Learning

Jul 21

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

arXiv:2506. 07691v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability.

By Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

llms fine-tuning safety

arXiv AI

Jul 21

Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding

arXiv:2607. 17999v1 Announce Type: new Abstract: Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information.

By Zhiwei Wei, Yonghe Sun, Zhenjia Liu, Wenjia Xu, Chao He, Weihua Dong, Chunbo Liu, Hua Liao

llms benchmarks

arXiv AI

Jul 21

ZifaMem: Structured Memory for Persona, Preference, and Emotional Continuity in AI Companions

arXiv:2607. 17564v1 Announce Type: new Abstract: AI companions are judged not only by single-turn fluency but by whether they sustain emotional continuity: remembering who the companion is, what the user prefers, and how the relationship has felt.

By Jingzhe Fang, Guozhi Xu, Yunfan Cui, Xiaochen Yang, Zhangyu Hua

llms agents

arXiv AI

Jul 21

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

arXiv:2607. 16207v1 Announce Type: new Abstract: Membership inference attacks (MIAs) test whether a candidate example appeared in a model's training data.

By Yeachan Jun, Albert No

llms diffusion fine-tuning

arXiv AI

Jul 21

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

arXiv:2504. 17356v3 Announce Type: replace Abstract: Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task.

By Weiliang Zhang, Xiaohan Huang, Yi Du, Ziyue Qiao, Qingqing Long, Zhen Meng, Yuanchun Zhou, Meng Xiao

llms agents reinforcement-learning

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