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

Jul 16

Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows

arXiv:2607. 13078v1 Announce Type: cross Abstract: LLMs are now proposed for fraud detection, scam investigation, content moderation, and other trust-and-safety workflows.

By Keyur Gabani

llms agents benchmarks safety

arXiv AI

Jul 16

Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs

arXiv:2607. 13080v1 Announce Type: cross Abstract: Autonomous coding agents force engineering organizations to choose between API-based frontier models -- strong reasoning at high token cost -- and on-premise quantized open-weights models, which promise low-marginal-cost scaling and data sovereignty at some loss of reasoning fidelity.

By Sheng-Wei Peng, Yi-Hsun Lin, Yi-Pei Lee

llms agents efficiency

arXiv AI

Jul 16

Baselines Before Architecture: Evaluating Coding Agents for Autonomous Penetration Testing

arXiv:2607. 13085v1 Announce Type: cross Abstract: Recent autonomous penetration testing papers report high benchmark scores while adding multi-component security harnesses around frontier LLMs.

By Ananda Dhakal, Krish Neupane, Aarjan Chaudhary

llms agents benchmarks

arXiv AI

Jul 16

WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency

arXiv:2607. 13099v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success but raise growing concerns about content provenance and misuse, motivating the need for reliable watermarking techniques.

By Z Sun, Q Jiang, S Sheng, L Xiang

llms efficiency benchmarks

arXiv AI

Jul 16

Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing

arXiv:2607. 13103v1 Announce Type: cross Abstract: Knowledge tracing (KT) aims to predict students' future performance by modeling their evolving knowledge states from historical interactions.

By Duantengchuan Li, Yingqian Bi, Jinsong Chen, Rui Zhang, Mingwen Tong

llms benchmarks safety

arXiv AI

Jul 16

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

arXiv:2607. 13124v1 Announce Type: cross Abstract: Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires.

By Qingyu Zhang, Qianhao Yuan, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Xiang Li, Ming Xu, Jiarui Li, Xiuyin Zhao

llms fine-tuning efficiency benchmarks

arXiv AI

Jul 16

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.

By Guoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, Cheuk Him Chau, Xinyu Fu, Yingzhao Lian, Tom S. Y. Li, Jintao Lin, Bowen Dong, Zian Qian, Yuhao Liu, Yuxuan Hu, Weikang Shi, Bin Zou, Bowen Zheng, Haoxuan Che, Chang Chen, Yuyang He, Heyang Sun, Tianyu Huang, Chong Hou Choi, Cheng Gong, Han Shi, Haoli Bai, Xihui Liu, Hongsheng Li, Qifeng Chen, Chao Huang, Rui Liu, Chenyang Lei

llms agents diffusion multimodal benchmarks data-engineering

arXiv AI

Jul 16

RAGthoven at SemEval-2026 Task 1: A Multi-Stage Pipeline Walks Into a Benchmark and Barely Clears the Bar

arXiv:2607. 13189v1 Announce Type: cross Abstract: We present RAGthoven, our system for SemEval-2026 Task 1 (MWAHAHA), Subtask A (multilingual constrained humor generation in English, Spanish, and Chinese).

By Marek \v{S}uppa, Vikt\'oria Ondrejov\'a, Lucia Ganajov\'a, Gregor Karetka, Daniel Skala

llms rag agents benchmarks

arXiv AI

Jul 16

HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models

arXiv:2607. 13072v1 Announce Type: cross Abstract: Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environment without specific target training.

By Luyuan Jia, Yinfeng Yu

llms agents

arXiv Machine Learning

Jul 16

Design-Specification Tiling for ICL-based CAD Code Generation

arXiv:2603. 12712v2 Announce Type: replace-cross Abstract: Large language models~(LLMs) have demonstrated remarkable capabilities in code generation, yet their performance remains limited on domain-specific tasks such as Computer-Aided Design~(CAD) code generation, largely due to the scarcity of high-quality training data.

By Yali Du, San-Zhuo Xi, Hui Sun, Ming Li

llms

arXiv Machine Learning

Jul 16

Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels

arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.

By Yaqi Zhao, Haoliang Sun, Yating Wang, Yongshun Gong, Yilong Yin

llms multimodal benchmarks

arXiv AI

Jul 16

DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments

arXiv:2607. 13465v1 Announce Type: cross Abstract: LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes.

By Huatao Li, Xinwei Geng, Yuheng Wang, Yutong Li, Runde Yang, Hantao Chen, Shu Yao, Jingru Fan, Xuhui Ren, Yuanyuan Zhao, Fei Huang, Chen Qian

llms agents benchmarks

arXiv AI

Jul 16

Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery

arXiv:2507. 16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery, aiming to support and enhance the Emergent Value Added Product (EVAP) developed by the Taiwan Space Agency (TASA).

By Yi-Shan Chu, Hsuan-Cheng Wei

llms computer-vision

arXiv AI

Jul 16

Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild

arXiv:2607. 13881v1 Announce Type: cross Abstract: Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories.

By Ting Lei, Jialin Liu, Zhu Xu, Yuxin Peng, Yang Liu

llms agents multimodal benchmarks

arXiv AI

Jul 16

Adversarial Prompting Framework for AI Safety Assessment

arXiv:2607. 13453v1 Announce Type: cross Abstract: Artificial Intelligence (AI), especially Generative AI (GenAI), adoption has increased in industries significantly in recent years.

By Yash Bhatnagar, Kunal Banerjee, Anirban Chatterjee

llms safety

arXiv AI

Jul 16

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.

By Zichen Ding, Jiaye Ge, Shufan Jiang, Kai Chen, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tiaohao Liang, Shudong Liu, Zerun Ma, Zixing Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu

llms agents benchmarks

arXiv AI

Jul 16

Consensus as Privileged Context for Label-Free Self-Distillation

arXiv:2607. 13643v1 Announce Type: cross Abstract: Sampling multiple solutions and returning the majority answer is among the most reliable ways to improve the reasoning accuracy of large language models without labels, and a growing family of methods converts this consensus signal into training supervision.

By John Gkountouras, Josip Juki\'c, Ivan Titov

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 16

Data-Efficient Adaptation of LLMs via Attention Head Reweighting

arXiv:2607. 13425v1 Announce Type: cross Abstract: Learning effectively from limited data is critical in domains like security where labeled examples are scarce.

By Tuomas Oikarinen, Zixiao Chen, Charlotte Siska, Tsui-Wei Weng, Chandan Singh, Jianfeng Gao

llms nlp fine-tuning

arXiv Machine Learning

Jul 16

Evaluating Frontier AI Agents as Autonomous Clinical Security Auditors

arXiv:2607. 13411v1 Announce Type: cross Abstract: Clinical AI models can expose patients to harm when adversarial vulnerabilities go undetected, yet formal security auditing requires statistical expertise, specialized tools, and significant time.

By Michael O. Eniolade

llms agents nlp safety

arXiv AI

Jul 16

Reassessing Muon for Matrix Factorization

arXiv:2607. 13246v1 Announce Type: cross Abstract: Muon has recently emerged as a strong optimizer for large-scale deep learning, where it reshapes gradient updates through approximate orthogonalization and has been reported to outperform Adam and AdamW in large language model training.

By Ali Parviz, Gal Mishne, Alex Cloninger

llms benchmarks

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