Large language models

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

32,584 stories · RSS feed

arXiv AI

Sep 15

CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection

arXiv:2604.07487v2 Announce Type: replace Abstract: Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineer...

By Linbo Liu, Guande Wu, Han Ding, Yawei Wang, Qiang Zhou, Yuzhe Lu, Zhichao Xu, Huan Song, Panpan Xu, Lin Lee Cheong

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Sep 15

Valley3: Scaling Omni Foundation Models for E-commerce

arXiv:2605.01278v3 Announce Type: replace Abstract: In this work, we present Valley3, an omni multimodal large language model (MLLM) developed for diverse global e-commerce tasks, with unified unders...

By Zeyu Chen, Guanghao Zhou, Min Yang, Qixiang Yin, Ziwang Zhao, Huanjin Yao, Pengjiu Xia, Cen Chen, Minghui Qiu

llms agents multimodal benchmarks

arXiv AI

Sep 15

Post-Reasoning: Improving the Performance of Non-Thinking Models at No Cost

arXiv:2605.06165v2 Announce Type: replace Abstract: As the widespread adoption of Large Language Models (LLMs) accelerates, token consumption from intermediate reasoning traces increasingly contribut...

By Richmond Sin Jing Xuan, Rishabh Bhardwaj, Soujanya Poria

llms efficiency benchmarks

arXiv AI

Sep 15

Whetstones: Measuring Coevolution Between Adaptive Malware and Behavioral Defense

arXiv:2608.00745v3 Announce Type: replace Abstract: The literature on self-adapting malware is open-loop: adaptation is evaluated against static detectors in simulators, with fitness computed by expe...

By Zihan Luo

llms

arXiv AI

Sep 15

ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation

arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large...

By Zhenghan Chen, Zekai Shao, Lidan Tan, Xin Lin, Xingchen Zeng, Yi Shan, Ziyue Lin, Xiaoliang Fu, Xinyuan Liu, Yuetong Guo, Fen Wang, Bongshin Lee, Siming Chen

llms multimodal benchmarks

arXiv AI

Sep 15

Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation

arXiv:2503.06868v2 Announce Type: replace-cross Abstract: Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored....

By Junhao Zhang, Richong Zhang, Fanshuang Kong, Ziyang Miao, Yanhan Ye, Yaowei Zheng

llms rag benchmarks safety

arXiv AI

Sep 15

ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation

arXiv:2503.18460v2 Announce Type: replace-cross Abstract: Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substant...

By Jiahui Xiang, Tong Ye, Peiyu Liu, Yinan Zhang, Wenhai Wang

llms rag fine-tuning benchmarks

arXiv AI

Sep 15

Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation

The paper introduces CORAL, a multimodal framework that combines spatial grounding and concept-level supervision for medical report generation. CORAL uses a prompt-driven segmentation model to localize lesions and a Concept Bottleneck module to predict multi-class clinical attributes, feeding these textual concept tokens and mask-modulated visual features into a multimodal large language model. Experiments on BUS-CoT and IU X-ray datasets show that CORAL improves diagnostic accuracy, concept consistency, and report quality compared to existing general-purpose and medical MLLMs.

By Xinyue Xu, Hongbin Lin, Juangui Xu, Hualiang Wang, Lehan Wang, Lijie Hu, Weiyang Liu, Adrian Weller, Xiaomeng Li

llms computer-vision multimodal safety

arXiv AI

Sep 15

Dissociating performance from compositional feature learning

arXiv:2505.09716v3 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) generalisation is considered a hallmark of human and animal intelligence. To achieve OOD through composition, a sys...

By George Dimitriadis, Spyridon Samothrakis

llms

arXiv AI

Sep 15

Data Security in Large Language Models: Risks, Defense, and Directions

arXiv:2508.02312v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine...

By Kang Chen, Xiuze Zhou, Yuanhui Yu, Yuanguo Lin, Hefeng Chen, Congyu Cai, Li Shen

llms rag agents nlp reinforcement-learning safety

arXiv AI

Sep 15

Exploring the Potential of Diffusion Large Language Models in Code Generation

arXiv:2509.11252v3 Announce Type: replace-cross Abstract: LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code to...

By Chengze Li, Yitong Zhang, Jia Li, Liyi Cai, Ge Li

llms diffusion benchmarks

arXiv AI

Sep 15

Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models

arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect pati...

By Mridul Sharma, Adeetya Patel, Zaneta D' Souza, Samira Abbasgholizadeh Rahimi, Siva Reddy, Sreenath Madathil

llms rag nlp benchmarks

arXiv AI

Sep 15

Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model

arXiv:2608.00929v2 Announce Type: replace Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance...

By Lynnette Hui Xian Ng, Kathleen M. Carley

llms agents

arXiv AI

Sep 15

CapGeo-Bench: Decoupling Visual Perception from Reasoning and Evaluating Geometric Understanding

arXiv:2510.09302v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, eve...

By Yuying Li, Siyi Qian, Hao Liang, Leqi Zheng, Ruichuan An, Linzhuang Sun, Jiajun Zhang, Wentao Zhang

llms multimodal benchmarks

arXiv AI

Sep 15

Mesh-based Super-resolution of Multiscale Detonation Flows with Graph Transformers

arXiv:2511.12041v4 Announce Type: replace-cross Abstract: Super-resolution flow reconstruction using state-of-the-art data-driven techniques is valuable for a variety of applications, such as subgrid...

By Shivam Barwey, Pinaki Pal

llms benchmarks

arXiv AI

Sep 15

Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores

arXiv:2601.13885v2 Announce Type: replace-cross Abstract: Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluatio...

By Esma Balk{\i}r, Alice Pernthaller, Marco Basaldella, Jos\'e Hern\'andez-Orallo, Nigel Collier

llms rag benchmarks

arXiv AI

Sep 15

Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges

arXiv:2602.13576v2 Announce Type: replace-cross Abstract: Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-langu...

By Ruomeng Ding, Yifei Pang, He Sun, Yizhong Wang, Zhiwei Steven Wu, Zhun Deng

llms benchmarks safety

arXiv AI

Sep 15

Context-Dependent Affordance Reports in Vision-Language Models

arXiv:2603.04419v3 Announce Type: replace-cross Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify...

By Murad Farzulla

llms multimodal

arXiv AI

Sep 15

Hindsight-Anchored Policy Optimization: Learning Through Hindsight with Thompson Sampling-Inspired Adaptive Gating

arXiv:2603.11321v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards improves reasoning in large language models, yet on-policy learning often suffers from cold-st...

By Yuning Wu, Ke Wang, Haoran Liu, Chaoqun Jia, Devin Chen, Kai Wei

llms reinforcement-learning benchmarks

arXiv AI

Sep 15

Concertina: Data-Centric Adaptive Pipeline Parallelism for Efficient Heterogeneous Long-Context LLM Training

arXiv:2509.21275v5 Announce Type: replace-cross Abstract: Long context training is crucial for extending LLM context windows. Existing schemes, such as sequence parallelism, incur substantial communi...

By Shiju Wang, Yujie Wang, Fangcheng Fu, Ao Sun, Yinxiao Feng, Zijian Zhu, Bin Cui, Xu Han, Kaisheng Ma

llms

Get the daily briefing