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

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 Machine Learning

Jun 2

Investigating and Alleviating Harm Amplification in LLM Interactions

arXiv:2606. 02423v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their capabilities through extended interactions.

By Ruohao Guo, Wei Xu, Alan Ritter

llms benchmarks safety

arXiv AI

Jun 2

CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection

arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.

By Huidong Feng, Wentao Chen, Jie Chen, Xinqi Cai, Ruolong Ma, Yinglin Zheng, Yuxin Lin, Ming Zeng

llms diffusion multimodal benchmarks

arXiv AI

Jun 2

PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs

arXiv:2606. 00104v1 Announce Type: cross Abstract: Foundation models are increasingly used to drive autonomous systems, yet existing approaches either keep the model in a tight control loop, raising latency and hallucination risk, or compile natural language into opaque end-to-end policies that are hard to explain, constraint and require domain-specific datasets and fine-tuning.

By Erdem Uysal, Timo Kehrer, Sebastiano Panichella

llms agents computer-vision robotics fine-tuning multimodal safety

arXiv AI

Jun 2

A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering

arXiv:2606. 00402v1 Announce Type: cross Abstract: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining.

By Yi Liu

llms

arXiv AI

Jun 2

Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps

arXiv:2510. 00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via real-time video communication (RTC), just like chatting with real people.

By Jiayang Xu, Xiangjie Huang, Zijie Li, Antariksh Verma, Zili Meng

llms agents benchmarks

arXiv AI

Jun 2

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

arXiv:2606. 00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.

By Vivek Kumar Sharma

llms fine-tuning safety

arXiv Machine Learning

Jun 2

Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs

arXiv:2606. 01710v1 Announce Type: cross Abstract: Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification.

By Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie, Sarah Erfani

llms rag fine-tuning multimodal benchmarks 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

ARCA: Adapter-Residual Credit Assignment When Token Signals Degenerate

arXiv:2606. 00257v1 Announce Type: cross Abstract: Token-level credit assignment for language-model reinforcement learning is usually formulated as if the policy were fully trainable, while practical LLM-RL pipelines often rely on parameter-efficient fine-tuning, especially LoRA.

By Rodney Lafuente-Mercado

llms reinforcement-learning fine-tuning

arXiv AI

Jun 2

Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

arXiv:2606. 00282v1 Announce Type: cross Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback.

By Xiangyu Wang, Yawen He, Shivendra Pratap Singh, Han Huang, Mengtong Hu, Sharath Ciddu, Yi-Hsuan Hsieh, Erik Groving, Yi Ding, Jieming Di, Tony Wang, Min Yun, Xiaoyu Chen, Ling Leng, Rob Malkin

llms efficiency

arXiv Machine Learning

Jun 2

LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models

arXiv:2606. 00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deployment challenging on resource-constrained devices.

By Haiyu Wang, Yutong Wang, Leshu Li, Yihui Ren, Sai Qian Zhang

llms efficiency multimodal

arXiv Machine Learning

Jun 2

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.

By Ziling Lu, Zongsheng Li, Xinke Shen, Kexin Lou, Yingyue Xin, Xiaoqi Chen, Shinan Wang, Xiang Chen, Jiahao Fan, Chenyu Huang, Xin Xu, Zhoujie Hou, Chen Wei, Quanying Liu

llms benchmarks

arXiv AI

Jun 2

MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models

arXiv:2606. 01060v1 Announce Type: cross Abstract: Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes internally.

By Partha Pratim Saha, Samarth Raina, Mayur Parvatikar, Amit Dhanda, Vinija Jain, Aman Chadha, Amitava Das

llms safety

arXiv AI

Jun 2

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models

arXiv:2606. 00275v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive performance on multimodal tasks through scaled architectures and extensive training.

By Zijie Zhou, Dandan Zhu, Hangxiangpan Wang, Heng Zhang, Huishen Jiao, Yi Zhao

llms multimodal safety

arXiv AI

Jun 2

CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space

arXiv:2606. 00472v1 Announce Type: cross Abstract: Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis.

By Hung Q. Vo, Huy Q. Vo, Son T. Ly, Zhihao Wan, Anh-Vu Nguyen, Hong Zhao, Jianting Sheng, Stephen T. C. Wong, Hien V. Nguyen

llms agents computer-vision benchmarks

arXiv AI

Jun 2

MOSS-Audio Technical Report

arXiv:2606. 01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning.

By Chen Yang, Chufan Yu, Hanfu Chen, Jie Zhu, Jingqi Chen, Ke Chen, Wenxuan Wang, Yang Wang, Yaozhou Jiang, Yi Jiang, Zhengyuan Lin, Ziqi Chen, Zhaoye Fei, Chenghao Liu, Jun Zhan, Kang Yu, Kexin Huang, Mingshu Chen, Qinyuan Cheng, Ruixiao Li, Shimin Li, Songlin Wang, Yang Gao, Yiyang Zhang, Xipeng Qiu

llms agents nlp fine-tuning multimodal

arXiv AI

Jun 2

The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs

arXiv:2606. 00674v1 Announce Type: cross Abstract: Large Language Models (LLMs) aligned via outcome-based Reinforcement Learning (RL) frequently exhibit a critical failure mode: they achieve high performance on in-distribution benchmarks while demonstrating brittle reasoning capabilities on out-of-distribution (OOD) tasks.

By Zihan Chen, Yiming Zhang, Wenxiang Geng, Zenghui Ding, Yining Sun

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 2

GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing

arXiv:2606. 00860v1 Announce Type: cross Abstract: Self-report questionnaires remain the prevailing tool for probing the psychological states of persona-conditioned agents (PC-Agents).

By Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun

llms agents benchmarks safety

arXiv Machine Learning

Jun 2

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention

arXiv:2606. 01243v1 Announce Type: cross Abstract: Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT).

By Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu

llms safety

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