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 6

Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

arXiv:2606. 05816v1 Announce Type: cross Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns rather than contextual emotional understanding.

By Jihun Cho, Soo-Yeon Jeong, Sun-Young Ihm

llms diffusion fine-tuning benchmarks

arXiv AI

Jun 6

Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation

arXiv:2606. 05241v1 Announce Type: cross Abstract: Public benchmarks enable fair and reproducible evaluation of LLM reasoning, but they become fragile for deep research agents that actively search the web during inference.

By Yongjie Wang, Xinyue Zhang, Kunhong Yao, Zhiwei Zeng, Kaisong Song, Jun Lin, Zhiqi Shen

llms agents benchmarks

arXiv AI

Jun 6

Multilingual Fine-Tuning via Localized Gradient Conflict Resolution

arXiv:2606. 05613v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems.

By Long P. Hoang, Yiran Zhao, Wei Lu, Wenxuan Zhang

llms fine-tuning

arXiv AI

Jun 6

Whose Alignment? Comparing LLM Process Alignment Across Diverse Organizational Decision Contexts

arXiv:2605. 25256v2 Announce Type: replace Abstract: Steerable pluralism requires a model to faithfully represent one specified perspective.

By Niklas Weller, Emilio Barkett

llms benchmarks safety

arXiv AI

Jun 6

Image Generators are Generalist Vision Learners

arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.

By Valentin Gabeur, Shangbang Long, Songyou Peng, Paul Voigtlaender, Shuyang Sun, Yanan Bao, Karen Truong, Zhicheng Wang, Wenlei Zhou, Jonathan T. Barron, Kyle Genova, Nithish Kannen, Sherry Ben, Yandong Li, Mandy Guo, Suhas Yogin, Yiming Gu, Huizhong Chen, Oliver Wang, Saining Xie, Howard Zhou, Kaiming He, Thomas Funkhouser, Jean-Baptiste Alayrac, Radu Soricut

llms diffusion computer-vision benchmarks

arXiv AI

Jun 6

Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents

arXiv:2606. 06036v1 Announce Type: new Abstract: Despite recent progress, LLM agents still struggle with reasoning over long interaction histories.

By Shuo Ji, Yibo Li, Bryan Hooi

llms agents benchmarks

arXiv AI

Jun 6

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

arXiv:2606. 05979v1 Announce Type: cross Abstract: We propose world-language-action (WLA) models as a new class of embodied foundation models.

By Yi Yang, Zhihong Liu, Siqi Kou, Yiyang Chen, Yanzhe Hu, Jianbo Zhou, Boyuan Zhao, Zhijie Wei, Xiao Xia, Xueqi Li, Pengfei Liu, Zhijie Deng

llms diffusion robotics multimodal benchmarks

arXiv AI

Jun 6

Benchmarking Emergent Coordination in Large-Scale LLM Populations: An Evaluation Framework on the MoltBook Archive

arXiv:2603. 03555v3 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical.

By Brandon Yee, Pairie Koh

llms agents diffusion benchmarks

arXiv AI

Jun 6

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

arXiv:2606. 05513v1 Announce Type: new Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time.

By Yiming Lu, Sihang Zeng, Zhengxu Tang, Max Lau, Fei Liu, Wei Jin

llms agents

arXiv AI

Jun 6

DragOn: A Benchmark and Dataset for Drag-Based GUI Interactions

arXiv:2606. 06322v1 Announce Type: new Abstract: GUI agents - vision-based models that control desktops, web browsers, and mobile devices through graphical user interfaces - promise to automate a wide range of digital tasks.

By Nathan Bout, Maxime Langevin, Ronan Riochet

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 6

A Study of LLMs' Preferences for Libraries and Programming Languages

arXiv:2503. 17181v4 Announce Type: replace-cross Abstract: Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use.

By Lukas Twist, Mark Harman, Don Syme, Joost Noppen, Helen Yannakoudakis, Detlef Nauck, Jie M. Zhang

llms fine-tuning benchmarks

arXiv AI

Jun 6

Agentic Molecular Recovery via Molecule-Aware Exploration

arXiv:2606. 05847v1 Announce Type: new Abstract: Text-guided molecular generation with LLMs often yields invalid SMILES.

By Suwan Yoon, Changhee Lee

llms agents

arXiv AI

Jun 6

LLM Self-Recognition: Steering and Retrieving Activation Signatures

arXiv:2606. 06315v1 Announce Type: new Abstract: Recent advances in interpretability suggest that large language models (LLMs) implicitly encode signals in their generated text that enable self-recognition of their outputs.

By Thibaud Ardoin, Jonas Sch\"afer, Gerhard Wunder

llms rag safety

arXiv AI

Jun 6

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.

By I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 6

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?

arXiv:2504. 10020v4 Announce Type: replace-cross Abstract: Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs).

By Hao Yin, Guangzong Si, Zilei Wang

llms multimodal benchmarks safety

arXiv AI

Jun 6

Staying with the Uncertainty: Uncertainty-Scaffolding Strategies for Artificial Moral Advisors in LLM-to-LLM Simulated Conversations

arXiv:2606. 05890v1 Announce Type: cross Abstract: LLMs are increasingly deployed as Artificial Moral Advisors (AMA) in a variety of contexts: what kind of conversational patterns should they display?

By Salvatore Greco, Hainiu Xu, Jacopo Domenicucci, Yulan He, Sylvie Delacroix

llms agents

arXiv AI

Jun 6

SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations

arXiv:2606. 05563v1 Announce Type: new Abstract: Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and context.

By Taewon Yun, Hyeonseong Park, Jeonghwan Choi, Hayoon Park, Yeeun Choi, Hwanjun Song

llms agents benchmarks safety

arXiv AI

Jun 6

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges

arXiv:2606. 05384v1 Announce Type: new Abstract: LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators.

By Srimonti Dutta, Akshata Kishore Moharir

llms benchmarks

arXiv AI

Jun 6

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

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

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