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 10

Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans

arXiv:2606. 10953v1 Announce Type: new Abstract: Furnished floor plans are fundamental to real estate visualization, interior design, and architectural workflows.

By Fedor Rodionov, Aleksandar Cvejic, Michael Birsak, John Femiani, Peter Wonka

llms fine-tuning multimodal safety

arXiv AI

Jun 10

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

arXiv:2606. 09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor.

By Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah

llms agents benchmarks safety

arXiv AI

Jun 10

AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies

arXiv:2606. 10752v1 Announce Type: new Abstract: Numerical solvers for partial differential equations (PDEs) are core computational tools in science and engineering.

By Huanshuo Dong, Keyao Zhang, Hong Wang, Zhezheng Hao, Zhiwei Zhuang, Ziyan Liu, Jiacong Wang, Gengyuan Liu, Xin Jin

llms agents

arXiv AI

Jun 10

Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study

arXiv:2601. 16700v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) tools have seen rapid adoption among software developers.

By Ludwig Felder, Tobias Eisenreich, Mahsa Fischer, Stefan Wagner, Chunyang Chen

llms agents

arXiv AI

Jun 10

Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

arXiv:2606. 10677v1 Announce Type: new Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions.

By Suozhao Ji, Baodong Wu, Zehao Wang, Lei Xia, Qingping Li, Ruisong Wang, Wenbo Ding, Zhenhua Zhu, Boxun Li, Guohao Dai, Yu Wang

llms agents

arXiv Machine Learning

Jun 10

FailureScope: Cross-Regime Behavioral Diagnosis of Language Model Weaknesses

arXiv:2606. 09878v1 Announce Type: new Abstract: Standard benchmarks report aggregate accuracy, but practitioners need to know which specific capabilities a model lacks.

By Nicholas Saban

llms agents multimodal benchmarks safety

arXiv AI

Jun 10

Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning

arXiv:2606. 10213v1 Announce Type: cross Abstract: Speech sound disorders affect approximately 44% of Korean pediatric communication disorder cases, yet automated assessment tools for Korean toddler speech remain underdeveloped.

By Diane Myung-kyung Woodbridge, Jee Hyun Suh

llms

arXiv AI

Jun 10

SocraticPO: Policy Optimization via Interactive Guidance

arXiv:2606. 09887v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness.

By Zirui Liu, Jie Ouyang, Qi Liu, Xianquan Wang, Jiayu Liu, Tingyue Pan, Qingchuan Li, Jing Sha, Zhenya Huang, Shijin Wang, Enhong Chen

llms reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jun 10

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606. 10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build.

By Joyce Karen Pelaez, Siqi Zhang, Hoo Sang Ko

llms multimodal

arXiv AI

Jun 10

ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning

arXiv:2606. 10532v1 Announce Type: new Abstract: Memory is essential for enabling large language model (LLM) agents to handle long-horizon reasoning tasks.

By Yunhan Jiang, Wenbin Duan, Shasha Guo, Liang Pang, Xiaoqian Sun, Huawei Shen

llms agents efficiency benchmarks

arXiv AI

Jun 10

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff

arXiv:2606. 09932v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become a standard pipeline for Large Language Model (LLM) post-training.

By Runze Liu, Jiashun Liu, Xu Wan, Yuqian Fu, Ling Pan

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 10

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

arXiv:2606. 10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems.

By Reazul Hasan Russel, Mingwei Tang, Rostam Shirani, Xinlong Liu, Navid Madani, Leo Ding, Yawen He, Xiangyu Wang, Mustafa Acar, Ashish Katiyar, Yuhai Li, Alan Yang, Metarya Ruparel, Derek Qiang Xu, Rupert Wu, Rui Yang, Liang Tao, Xinyi Zhao, Larry Zhang, Sri Reddy, Rob Malkin

llms rag

arXiv Machine Learning

Jun 10

PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning

arXiv:2606. 10369v1 Announce Type: cross Abstract: As large language models (LLMs) continue to scale, it becomes increasingly challenging to grow model capacity under fixed computation budgets.

By Xinyue Peng, Yi Qian, Jiaojiao Lin, Wenjian Shao, Yanming Liu

llms efficiency benchmarks

arXiv AI

Jun 10

Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement

arXiv:2603. 08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and cite different sources.

By Ronald Sielinski

llms

arXiv Machine Learning

Jun 10

Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis

arXiv:2606. 09877v1 Announce Type: new Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever the underlying information landscape evolves.

By Juan M. Huerta

llms benchmarks

arXiv AI

Jun 10

V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions

arXiv:2512. 11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space.

By Chenrui Fan, Yijun Liang, Shweta Bhardwaj, Kwesi Cobbina, Ming Li, Tianyi Zhou

llms multimodal benchmarks

arXiv AI

Jun 10

Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines

arXiv:2501. 00745v3 Announce Type: replace-cross Abstract: The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval.

By Xiyang Hu

llms robotics safety

arXiv AI

Jun 10

STORM: Stepwise Token Optimization with Reward-Guided Beam Search

arXiv:2606. 10621v1 Announce Type: cross Abstract: Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes.

By Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong, Habiboulaye Amadou Boubacar, Pablo Piantanida, Benjamin Piwowarski

llms

arXiv AI

Jun 10

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

arXiv:2606. 10684v1 Announce Type: cross Abstract: Modern language agents which perform multi-step reasoning have shown strong performance in knowledge-intensive question answering.

By Jaewan Park, Solbee Cho, Jay-Yoon Lee

llms agents nlp fine-tuning benchmarks

arXiv AI

Jun 10

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks

arXiv:2606. 10066v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years, yet reported accuracy assumes these examples were absent from pretraining.

By Bruce Changlong Xu, Lan Wu, Alexander Ryu

llms multimodal benchmarks

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