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 AI

Jun 19

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models

arXiv:2606. 19534v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks.

By Yueyi Sun, Yuhao Wang, Jason Li, Ye Tian, Tao Zhang, Jacky Mai, Yihan Wang, Haochen Wang, Jinbin Bai, Ling Yang, Yunhai Tong

llms diffusion multimodal benchmarks

arXiv AI

Jun 19

CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models

arXiv:2606. 19788v1 Announce Type: new Abstract: We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models.

By Yuxu Zhou, Ond\v{r}ej Ku\v{z}elka, Yuyi Wang, Yuanhong Wang, Yi Chang

llms benchmarks

arXiv AI

Jun 19

Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer

arXiv:2606. 19346v1 Announce Type: cross Abstract: We study cross-lingual transfer by fine-tuning seven large language models (4B--671B parameters) on Arabic and evaluating zero-shot reading comprehension on Semitic languages and non-Semitic controls.

By Ahmed Haj Ahmed, Ruochen Zhang, Alvin Grissom II

llms fine-tuning benchmarks safety

arXiv AI

Jun 19

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.

By Aueaphum Aueawatthanaphisut

llms rag agents nlp multimodal

arXiv AI

Jun 19

Efficiently Representing Algorithms With Chain-of-Thought Transformers

arXiv:2606. 19697v1 Announce Type: cross Abstract: The increasing popularity of \emph{reasoning} models -- language models that output a series of reasoning or thought tokens before producing an answer -- is justified, in part, by theoretical results showing that chain-of-thought (CoT) transformers can simulate Turing machines, and thus perform arbitrary computation.

By Yanhong Li, Anej Svete, Ashish Sabharwal, William Merrill

llms

arXiv AI

Jun 19

Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

arXiv:2606. 20177v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks.

By Haochen Han, Jue Wang, Alex Jinpeng Wang, Fangming Liu

llms multimodal benchmarks safety

arXiv AI

Jun 19

Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers

arXiv:2606. 19460v1 Announce Type: cross Abstract: We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale.

By Fabio De Sousa Ribeiro, Emma A. M. Stanley, Charles Jones, Tian Xia, Dominic C. Marshall, Laurent Renard Trich\'e, Christopher V. Cosgriff, Panagiotis Dimitrakopoulos, Sotirios A. Tsaftaris, Ben Glocker

llms

arXiv Machine Learning

Jun 19

On the Redundancy of Timestep Embeddings in Diffusion Models

arXiv:2606. 20416v1 Announce Type: new Abstract: Diffusion models rely heavily on explicit timestep embeddings to modulate the denoising process across various noise scales.

By Jos\'e A. Ch\'avez

llms rag diffusion benchmarks

arXiv AI

Jun 19

Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring

arXiv:2606. 20138v1 Announce Type: new Abstract: LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines.

By Po-Chin Chang, Nicholas Hogan, Aske Plaat, Michiel T. van der Meer

llms robotics benchmarks

arXiv AI

Jun 19

Analyzing the Narration Gap in LLM-Solver Loops

arXiv:2606. 19588v1 Announce Type: new Abstract: Formal tools such as SAT and SMT solvers are increasingly embedded in language model reasoning pipelines when a safety or security critical question can be formulated in logic.

By Zunchen Huang, Songgaojun Deng

llms safety

arXiv AI

Jun 19

Confidence-Aware Automated Assessment of Student-Drawn Scientific Models

arXiv:2606. 20264v1 Announce Type: new Abstract: Student-generated drawings are widely used in science education to assess learners' conceptual understanding in modeling-based tasks aligned with the Next Generation Science Standards (NGSS).

By Luyang Fang, Yingchuan Zhang, Jongchan Park, Zhaoji Wang, Ping Ma, Xiaoming Zhai

llms computer-vision

arXiv AI

Jun 19

Contagion Networks: Evaluator Bias Propagation in Multi-Agent LLM Systems

arXiv:2606. 20493v1 Announce Type: cross Abstract: When large language models serve as evaluators in multi-agent systems, their systematic evaluation biases propagate through the agent network.

By Zewen Liu

llms agents safety

arXiv AI

Jun 19

Multi-Agent Transactive Memory

arXiv:2606. 19911v1 Announce Type: new Abstract: The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations.

By To Eun Kim, Xuhong He, Dishank Jain, Ambuj Agrawal, Negar Arabzadeh, Fernando Diaz

llms rag agents

arXiv AI

Jun 19

Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models

arXiv:2606. 19750v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems are sampled during optimization.

By Darrien McKenzie, Nicklas Hansen, Xiaolong Wang

llms reinforcement-learning

arXiv AI

Jun 19

DeXposure-Claw: An Agentic System for DeFi Risk Supervision

arXiv:2606. 19501v1 Announce Type: new Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks.

By Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He

llms agents

arXiv AI

Jun 19

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

arXiv:2606. 20280v1 Announce Type: cross Abstract: Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR).

By Yuhan Liu, Pei Fu, Hang Li, Yukun Qi, Chao Jiang, Jingwen Fu, Zhen Liu, Bin Qin, Zhenbo Luo, Jian Luan, Jingmin Xin

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jun 19

The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self

arXiv:2606. 19924v1 Announce Type: new Abstract: Most artificial intelligence systems are built on the assumption that goals are exogenous and specified by the designer.

By Aritra Sarkar

llms agents

arXiv AI

Jun 19

NRITYAM: Language Models Meet Art and Heritage of Dance

arXiv:2606. 19727v1 Announce Type: cross Abstract: Language models have become essential tools in shaping modern workflows.

By Punit Kumar Singh, Niladri Ghosh, Advait Joshi{\i}nst, Shailee Choudhary, Michael F\"arber, Haiqin Yang

llms multimodal benchmarks

arXiv AI

Jun 19

ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

arXiv:2606. 20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration.

By Tingyue Pan, Mingyue Cheng, Daoyu Wang, Yitong Zhou, Jie Ouyang, Qi Liu, Enhong Chen

llms agents benchmarks

arXiv AI

Jun 19

Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.

By Jelena Meyer, David Garcia, Dirk U. Wulff

llms safety

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