Large language models

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

31,091 stories · RSS feed

arXiv AI

Jun 6

RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention

arXiv:2606. 06256v1 Announce Type: new Abstract: As the input length of large language model (LLM) serving continues to grow, the KV cache has become a dominant bottleneck in AI infrastructure.

By Yang Liu, ZhaoKai Luo, HuaYi Jin, ZhiYong Wang, RuoZhou He, BoYu Wang, Guanjie Chen, Junhao Hu

llms fine-tuning efficiency

arXiv AI

Jun 6

Dense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs

arXiv:2606. 06203v1 Announce Type: cross Abstract: Input length and the position of relevant information are widely cited as the primary causes of degraded LLM long-context performance.

By Giovanni Dettori, Matteo Boffa, Danilo Giordano, Idilio Drago, Marco Mellia

llms benchmarks

arXiv AI

Jun 6

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows

arXiv:2605. 12376v2 Announce Type: replace Abstract: Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines.

By Wei Liu, Yang Gu, Xi Yan, Zihan Nan, Beicheng Xu, Keyao Ding, Bin Cui, Wentao Zhang

llms agents benchmarks

arXiv AI

Jun 6

Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

arXiv:2603. 25158v5 Announce Type: replace Abstract: Large Language Model (LLM) agents increasingly rely on domain-specific skills, yet manually authoring such skills does not scale, and skills generated purely from parametric knowledge often miss critical operational pitfalls.

By Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Erchao Zhao, Xiaoxi Jiang, Guanjun Jiang

llms agents

arXiv AI

Jun 6

Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation

arXiv:2606. 06076v1 Announce Type: new Abstract: While vision-language models excel at general multimodal understanding, they still struggle with visual spatial planning.

By Haocheng Luo, Jiahui Liu, Ruicheng Zhang, Zhizhou Zhong, Jiaqi Huang, Zunnan Xu, Quan Shi, Jun Zhou, Xiu Li

llms efficiency multimodal benchmarks

arXiv AI

Jun 6

UNIVID: Unified Vision-Language Model for Video Moderation

arXiv:2606. 05748v1 Announce Type: cross Abstract: Global-scale video moderation faces a dual challenge: the need for fine-grained multi-modal reasoning and the demand for interpretable outputs to support downstream enforcement.

By Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao

llms multimodal safety

arXiv AI

Jun 6

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

arXiv:2606. 05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly.

By Nicolas Bougie, Xiaotong Ye, Gian Maria Marconi, Narimasa Watanabe

llms agents fine-tuning multimodal

arXiv AI

Jun 6

PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis

arXiv:2606. 05176v1 Announce Type: cross Abstract: While large language models (LLMs) show strong performance in natural language understanding and generation, their evaluation and adaptation to domain-specific constraints in telecommunications customer support remain limited.

By Lucas Tamic, Ilan Jaffeux-Cheniout, Xavier Marjou

llms fine-tuning

arXiv AI

Jun 6

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

arXiv:2606. 05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks.

By Jiateng Liu, Bingxuan Li, Zhenhailong Wang, Rushi Wang, Kaiwen Hong, Cheng Qian, Jiayu Liu, Denghui Zhang, Katherine Driggs-Campbell, Manling Li, Heng Ji

llms agents computer-vision multimodal benchmarks

arXiv AI

Jun 6

Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature

arXiv:2606. 05206v1 Announce Type: cross Abstract: Fragmentation is common in interdisciplinary fields with diverse methods and theoretical commitments.

By Hamed Nejat, Alexander Maier, Jesse Spencer-Smith, Andr\'e M. Bastos

llms

arXiv AI

Jun 6

Assessing the Geographic Diversity of AI's Platial Representations in Image Generation

arXiv:2606. 05188v1 Announce Type: cross Abstract: (Gen)AI diversity is not merely an ethical issue.

By Zilong Liu, Krzysztof Janowicz, Mina Karimi

llms diffusion multimodal benchmarks safety

arXiv AI

Jun 6

ATT-CR: Adaptive Triangular Transformer for Cloud Removal

arXiv:2606. 05999v1 Announce Type: cross Abstract: Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images.

By Yang Wu, Ye Deng, Pengna Li, Wenli Huang, Kangyi Wu, Xiaomeng Xin, Jinjun Wang

llms benchmarks

arXiv AI

Jun 6

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

arXiv:2606. 06087v1 Announce Type: cross Abstract: Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext.

By Aofan Yu, Chenyu Zhou, Tianyi Xu, Zihan Guo, Rong Shan, Zhihui Fu, Jun Wang, Weiwen Liu, Yong Yu, Weinan Zhang, Jianghao Lin

llms agents fine-tuning

arXiv AI

Jun 6

InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning

arXiv:2603. 17310v2 Announce Type: replace Abstract: Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost.

By Chengwei Wei, Jung-jae Kim, Longyin Zhang, Shengkai Chen, Nancy F. Chen

llms reinforcement-learning benchmarks

arXiv AI

Jun 6

Multi-Granularity Reasoning for Natural Language Inference

arXiv:2606. 05181v1 Announce Type: cross Abstract: Natural Language Inference (NLI) is a fundamental task in natural language understanding that requires determining the logical relationship between a premise and a hypothesis.

By Chunling Xi, Di Liang

llms benchmarks

arXiv AI

Jun 6

Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation

arXiv:2606. 05510v1 Announce Type: new Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information.

By Ahmed Alansary, Molham Mohamed, Ali Hamdi

llms fine-tuning

arXiv AI

Jun 6

HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling

arXiv:2510. 00054v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding tasks.

By Xianjie Liu, Yiman Hu, Yixiong Zou, Liang Wu, Jian Xu, Bo Zheng

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 6

Causal Scaffolding for Physical Reasoning: A Benchmark for Causally-Informed Physical World Understanding in VLMs

arXiv:2606. 05966v1 Announce Type: cross Abstract: Understanding and reasoning about the physical world is the foundation of intelligent behavior, yet state-of-the-art vision-language models (VLMs) still fail at causal physical reasoning, often producing plausible but incorrect answers.

By Tianyi Tang, Zhuoyi Lin, Zeyu Feng, Tianyi Ma, Yew-Soon Ong, Ivor Tsang, Haiyan Yin

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 6

Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission

arXiv:2606. 06273v1 Announce Type: cross Abstract: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol probability modeling and reliable delivery over noisy channels.

By Tianqi Ren, Rongpeng Li, Xianfu Chen, Yingyu Li, Zhifeng Zhao

llms diffusion benchmarks

arXiv AI

Jun 6

Beyond Vector Similarity: A Structural Analysis of Graph-Augmented Retrieval for Industrial Knowledge Graphs

arXiv:2606. 06003v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) fails systematically on queries requiring structural reasoning over interconnected entities.

By Grama Chethan

llms rag

Get the daily briefing