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

Jun 9

OpenCompass: A Universal Evaluation Platform for Large Language Models

arXiv:2605. 19276v3 Announce Type: replace-cross Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs).

By Maosong Cao, Kai Chen, Haodong Duan, Yixiao Fang, Zhiwei Fei, Tong Gao, Ge Jiaye, Mo Li, Hongwei Liu, Junnan Liu, Yuan Liu, Chengqi Lyu, Han Lyu, Ningsheng Ma, Zerun Ma, Yu Sun, Zhiyong Wu, Linchen Xiao, Zhuozhi Xiong, Jun Xu, Haochen Ye, Zhaohui Yu, Yike Yuan, Songyang Zhang, Yufeng Zhao, Fengzhe Zhou, Peiheng Zhou, Dongsheng Zhu, Lin Zhu, Jingming Zhuo

llms benchmarks

arXiv Machine Learning

Jun 9

Teacher-Free Self-Training Amplifies but Does Not Compound: A Pass@$K$ Crossover on a Free-Verifier Domain

arXiv:2606. 07856v1 Announce Type: new Abstract: When a language model trains on its own verified outputs, does it acquire capability beyond its base, or merely get better at expressing capability the base already had?

By Igor Lima Strozzi

llms

arXiv Machine Learning

Jun 9

TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation

arXiv:2606. 07569v1 Announce Type: new Abstract: Accurate carbon emission monitoring is critical for climate policy and emerging regulatory mechanisms such as the EU Carbon Border Adjustment Mechanism, yet city-level high-frequency monitoring data remain extremely scarce, severely limiting data-hungry deep learning models.

By Zesen Wang, Lijuan Lan, Yonggang Li, Chunhua Yang

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 9

Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency

arXiv:2606. 07881v1 Announce Type: new Abstract: Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency.

By Itay Elam, Eliron Rahimi, Avi Mendelson, Chaim Baskin

llms efficiency

arXiv Machine Learning

Jun 9

De novo molecular generation with optical property preconditioning at the token level

arXiv:2606. 08221v1 Announce Type: new Abstract: Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generative models across chemical motifs.

By Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park, Jorge A. Campos-Gonzalez-Angulo, Xinjian Liu, Al\'an Aspuru-Guzik

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning

arXiv:2606. 09138v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative applications such as OpenClaw.

By Daoyu Wang, Mingyue Cheng, Qingchuan Li, Shuo Yu, Jie Ouyang, Qi Liu

llms agents reinforcement-learning

arXiv Machine Learning

Jun 9

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

arXiv:2606. 09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images.

By Zekai Chen, Miao Zhang, Jiayang Xing, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang

llms multimodal benchmarks

arXiv Machine Learning

Jun 9

Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation

arXiv:2606. 07647v1 Announce Type: cross Abstract: Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge.

By Ruipeng Zhang, Zhihao Li, C. L. Philip Chen, Tong Zhang

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 9

Continuous Language Diffusion as a Decoder-Interface Problem

arXiv:2606. 08810v1 Announce Type: cross Abstract: Gaussian-corrupted sentence embeddings have no direct linguistic interpretation, yet continuous diffusion language models can generate fluent text from them.

By Zhicheng Du, Lan Ma

llms rag diffusion

arXiv AI

Jun 9

DN-Hypo-Pipeline: An AI-Driven Workflow for Hypothesis Generation via Large Language Models and Scientific Explanations

arXiv:2606. 08532v1 Announce Type: new Abstract: A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning about scientific phenomena.

By Lei Lin, Ronghao Wang, Chunbao Zhou, Jue Wang, Yangang Wang

llms

arXiv AI

Jun 9

Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control

arXiv:2606. 08714v1 Announce Type: cross Abstract: Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation.

By Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari

llms

arXiv Machine Learning

Jun 9

In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

arXiv:2502. 19049v3 Announce Type: replace Abstract: Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by a diffusion function.

By Patrick Seifner, Kostadin Cvejoski, David Berghaus, Cesar Ojeda, Ramses J. Sanchez

llms diffusion fine-tuning

arXiv Machine Learning

Jun 9

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation

arXiv:2601. 04498v2 Announce Type: replace Abstract: Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information.

By Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

llms diffusion multimodal benchmarks

arXiv Machine Learning

Jun 9

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

arXiv:2601. 09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge.

By Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts

arXiv:2605. 30184v2 Announce Type: replace Abstract: While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons.

By Fanny Lehmann, Firat Ozdemir, Yun Cheng, Torsten Hoefler, Sebastian Schemm, Benedikt Soja, Siddhartha Mishra

llms computer-vision benchmarks

arXiv Machine Learning

Jun 9

DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination

arXiv:2606. 08068v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling.

By Yi Xie, Zhanke Zhou, Chentao Cao, Bo Liu, Bo Han

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models

arXiv:2606. 07522v1 Announce Type: cross Abstract: We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift.

By Julia Kruk, Sanchita Porwal, Amitrajit Bhattacharjee, Mansi Phute

llms fine-tuning

arXiv AI

Jun 9

A retrieval conditioned rebinding circuit for dynamic entity tracking in large language models

arXiv:2606. 08644v1 Announce Type: cross Abstract: To interpret context correctly and retrieve relevant information, large language models must bind entities to their attributes and update these bindings as state changes.

By Soyoung Oh, Vera Demberg

llms

arXiv AI

Jun 9

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation

arXiv:2512. 15116v2 Announce Type: replace-cross Abstract: Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values.

By Runze Li, Hanchen Wang, Wenjie Zhang, Binghao Li, Yu Zhang, Xuemin Lin, Ying Zhang

llms diffusion benchmarks safety

arXiv AI

Jun 9

LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)

arXiv:2606. 09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE).

By Ankai Hao, Ke Chen, Huan Li, Lidan Shou

llms benchmarks

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