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 2

OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence

arXiv:2603. 14771v3 Announce Type: replace Abstract: Large Language Model (LLM)-based Collective Intelligence (CI) presents a promising approach to overcoming the data wall and continuously boosting the capabilities of LLM agents.

By Peigen Liu, Rui Ding, Yuren Mao, Ziyan Jiang, Yuxiang Ye, Yunjun Gao, Ying Zhang, Renjie Sun, Longbin Lai, Zhengping Qian

llms agents benchmarks

arXiv AI

Jun 2

Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation

arXiv:2606. 01725v1 Announce Type: new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes.

By Donghwan Kim, Prakhar Singh, Younghoon Min, Jongryool Kim, Jongse Park, Kiwan Maeng

llms agents benchmarks

arXiv AI

Jun 2

Vision Language Models Cannot Reason About Physical Transformation

arXiv:2603. 07109v2 Announce Type: replace Abstract: Understanding physical transformations is fundamental for reasoning in dynamic environments.

By Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, Siheng Wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma, Hokin Deng

llms robotics

arXiv AI

Jun 2

PolarMem: A Training-Free Polarized Latent Graph Memory for Verifiable Vision-Language Models

arXiv:2602. 00415v2 Announce Type: replace Abstract: Memory is not merely a storage mechanism for intelligent systems, but a structure for organizing evidence and constraining belief.

By Zhisheng Chen, Tingyu Wu, Zijie Zhou, Zhengwei Xie, Jinhan Li, Ziyan Weng, Liang Lin, Jingwei Song, Zikai Xiao, Yingwei Zhang

llms multimodal benchmarks

arXiv Machine Learning

Jun 2

Step-Level Sparse Autoencoder for Reasoning Process Interpretation

arXiv:2603. 03031v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning.

By Xuan Yang, Jiayu Liu, Yuhang Lai, Hao Xu, Zhenya Huang, Ning Miao

llms efficiency safety

arXiv AI

Jun 2

Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry

arXiv:2606. 00094v1 Announce Type: cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space.

By Duoduo Xue, Zhiyu Zhu, Junhui Hou

llms rag diffusion nlp safety

arXiv AI

Jun 2

From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression

arXiv:2606. 02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted modules.

By Elia Cunegatti, Marcus Vukojevic, Erik Nielsen, Giovanni Iacca

llms efficiency

arXiv AI

Jun 2

Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search

arXiv:2606. 02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any candidates found.

By Juan Cruz-Benito, Andrew W. Cross, David Kremer, Ismael Faro

llms

arXiv AI

Jun 2

Cross-modal linkage risk in clinical vision-language models

arXiv:2606. 02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.

By Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung, Daniel Truhn

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 2

Rank-Constrained Deep Matrix Completion for Group Recommendation

arXiv:2606. 01948v1 Announce Type: cross Abstract: The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual preferences.

By Mubaraka Sani Ibrahim, Lehel Csat\'o, Isah Charles Saidu

llms diffusion efficiency

arXiv AI

Jun 2

THRD: A Training-Free Multi-Turn Defense Framework for Jailbreak Attacks on Large Language Models

arXiv:2606. 01738v1 Announce Type: cross Abstract: Multi-turn jailbreak attacks pose a growing threat to LLMs by exploiting conversational dynamics such as gradual escalation and cross-turn coordination.

By Zhiqing Ma, Zhonghao Xu, Dong Yu, Chen Kang, Changliang Li, Pengyuan Liu

llms agents multimodal benchmarks safety

arXiv AI

Jun 2

TechGraphRAG: An Agentic Graph-Augmented RAG Framework for Technical Literature Reasoning

arXiv:2606. 01613v1 Announce Type: cross Abstract: This paper presents an agentic retrieval-augmented generation (RAG) framework for domain-specific technical reasoning support, instantiated over a curated corpus of approximately 2,100 academic papers in intelligent tires, vehicle dynamics, and vehicle control.

By Kanwar Bharat Singh

llms rag agents

arXiv AI

Jun 2

Move the Query, Not the Cache: Characterizing Cross-Instance Latent Attention Redistribution Across GPU Fabrics

arXiv:2606. 01502v1 Announce Type: cross Abstract: Frontier LLMs increasingly decide what a query attends to with a sparse-attention indexer that picks a few KV-cache blocks per query: attention's unit is now a small, reusable chunk.

By Bole Ma, Jan Eitzinger, Harald K\"ostler, Gerhard Wellein

llms agents

arXiv AI

Jun 2

Detect Before You Leap: Mirage Detection in Vision-Language Models

arXiv:2606. 00435v1 Announce Type: cross Abstract: Vision-language models (VLMs) can produce confident visual answers even when the required visual evidence is missing, blank, or unrelated to the question.

By Sayeed Shafayet Chowdhury, Md. Shaown Miah

llms rag nlp multimodal safety

arXiv AI

Jun 2

On the Limits of Token Reduction for Efficient Unified Vision Language Training

arXiv:2606. 01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expensive and largely overlooked from an efficiency perspective.

By Siyi Chen, Weiming Zhuang, Jingtao Li, Lingjuan Lv

llms multimodal

arXiv AI

Jun 2

Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory

arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.

By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu

llms rag agents

arXiv AI

Jun 2

Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing

arXiv:2606. 01393v1 Announce Type: cross Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems.

By Minglai Yang, Xinyan Velocity Yu, Pengyuan Li, Xinyu Guo, Zhenting Qi, Konwoo Kim, Longtian Ye, Xiaolong Luo, Jinhe Bi, Henry Zhang, Haris Riaz, Xuan Zhang, Yunze Xiao, Bangya Liu, Tom Tang, Yunfei Zhao, Qunshu Lin, Zihan Wang, Minghao Liu, Michael Lingzhi Li, Yilun Du, Jesse Thomason, Rogerio Feris, Alex Pentland, Zexue He

llms computer-vision multimodal benchmarks

arXiv AI

Jun 2

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).

By Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance

arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.

By Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky, Jazi Eko Istiyanto, Jun Miura

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 2

IndoBias: A Dual Track Culturally Grounded Benchmark for LLMs Bias Evaluation in Indonesian Languages

arXiv:2606. 01260v1 Announce Type: cross Abstract: Despite being home to more than 1300 ethnic groups and 700 indigenous languages, bias in Large Language Models has not been fully studied in Indonesia, thus leaving a critical gap in evaluating representational fairness and localized stereotypes within its uniquely vast, multilingual, and diverse sociocultural landscape.

By Ikhlasul Akmal Hanif, Muhammad Falensi Azmi, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Fajri Koto

llms benchmarks safety

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