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 21

PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model

arXiv:2607. 17806v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations.

By Li Xian, Mingxi Li, Yizheng Wang, Yiming Shen, Qi Chen, Zhuoling Xiao

llms rag agents robotics fine-tuning multimodal safety

arXiv AI

Jul 21

Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models

arXiv:2607. 17779v1 Announce Type: new Abstract: Text-to-Image (T2I) generative models have achieved remarkable progress in synthesizing high-quality visual content, yet they remain vulnerable to adversarial misuse, particularly in generating Not-Safe-For-Work (NSFW) images.

By Dongdong Yang, Deyue Zhang, Zhao Liu, Zonghao Ying, Wenzhuo Xu, Jiankai Jin, Xiangzheng Zhang, Quanchen Zou

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 21

I wanted it to feel more personal: Customization of social AI as AI individualism in practice

arXiv:2607. 17826v1 Announce Type: cross Abstract: Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.

By Marita Skjuve, Anna Gr{\o}ndal Larsen, Asbj{\o}rn F{\o}lstad, Nena van As, Petter Bae Brandtzaeg

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arXiv AI

Jul 21

CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

arXiv:2607. 17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties.

By Ruogu Chen, Weihua Xiao, Ramesh Karri, Jie Han

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arXiv AI

Jul 21

A small language model detects behavioural faithfulness gaps that frontier judges and human raters miss

arXiv:2607. 09306v2 Announce Type: replace-cross Abstract: Whether a language model behaves as it claims is a judgement on which independent human raters cannot agree (Fleiss kappa = 0.

By Kwan Soo Shin, In Seok Kang, Yunkyung Min, Munho Lee

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arXiv AI

Jul 21

Pailitao-MMSearch: Building Native E-Commerce Multimodal Search Foundation

arXiv:2607. 17499v1 Announce Type: new Abstract: The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that seamlessly combine product images, natural language descriptions, and mixed-intent instructions.

By Xiaohan Ye, Xu Chen, Zihan Gong, Jian Ding, Lianyu Du, Baicheng Chen, Yunmeng Shu, Jingqian Zhao, Zhixiang Zhao, Shuaiqi Jia, Chong Ma, Shuwen Xiao, Xiangheng Kong, Yuan Gao, Jun Song, Jinsong Lan, Xiaoyong Zhu, Bo Zheng

llms multimodal

arXiv AI

Jul 21

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

arXiv:2607. 16207v1 Announce Type: new Abstract: Membership inference attacks (MIAs) test whether a candidate example appeared in a model's training data.

By Yeachan Jun, Albert No

llms diffusion fine-tuning

arXiv AI

Jul 21

Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory

arXiv:2607. 17879v1 Announce Type: new Abstract: LLM-based autonomous agents require external memory to overcome their statelessness and limited context window for long-term interaction and dynamic knowledge reasoning.

By Ganesh Senrayan, Moyuru Yamada, Ishan Jindal, Kiran Purohit

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arXiv AI

Jul 21

Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

arXiv:2603. 21693v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risks in clinical settings.

By Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan, Euan Ashley, Ehsan Adeli

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arXiv AI

Jul 21

Memory in the Loop: In-Process Retrieval as Extended Working Memory for Language Agents

arXiv:2607. 05690v2 Announce Type: replace Abstract: Language agents run a loop - observe, reason, act - but the memory they reason over sits outside it: a store queried at most once per turn.

By Yusuf Khan, Carlo Lipizzi

llms rag agents

arXiv AI

Jul 21

mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks

arXiv:2601. 02451v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test.

By Subhankar Mishra

llms benchmarks

arXiv AI

Jul 21

CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs

arXiv:2603. 27958v2 Announce Type: replace Abstract: Analogical reasoning tests a fundamental aspect of human cognition: mapping the relation from one pair of objects to another.

By Yongkang Du, Xiaohan Zou, Minhao Cheng, Lu Lin

llms multimodal benchmarks

arXiv Machine Learning

Jul 21

Efficient Sequential Evaluation of Large Language Models

arXiv:2607. 17409v1 Announce Type: cross Abstract: We study the problem of sequentially evaluating a new large language model (LLM) on a fixed question set using historical performance data from prior LLMs.

By Chia-Yu Hsu, Shubhanshu Shekhar

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arXiv AI

Jul 21

SGA: Plug&Play Geometric Verification for Educational Video Synthesis

arXiv:2607. 18116v1 Announce Type: new Abstract: Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim.

By Lopez Jhon, Hinojosa Carlos, Ghanem Bernard

llms agents benchmarks

arXiv Machine Learning

Jul 21

The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric

arXiv:2607. 18237v1 Announce Type: cross Abstract: Human visual similarity judgments are context-dependent.

By Sheng-Yu Wang, Yotam Nitzan, Aaron Hertzmann, Jun-Yan Zhu, Eli Shechtman, Alexei A. Efros, Richard Zhang

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 21

Stress Testing Concept Erasure with Large Language Model Agents

arXiv:2607. 17890v1 Announce Type: new Abstract: Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment.

By Yuyang Xue, Feng Chen, Zhihua Liu, Edward Moroshko, Jingyu Sun, Steven McDonagh, Sotirios A. Tsaftaris

llms agents

arXiv AI

Jul 21

ReMIND: Orchestrating Modular Large Language Models for Controllable Serendipity A REM-Inspired System Design for Emergent Creative Ideation

arXiv:2601. 07121v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used not only for problem solving but also for creative ideation; however, generating ideas that are both novel and coherent remains challenging.

By Makoto Sato

llms agents

arXiv AI

Jul 21

Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs

arXiv:2607. 18230v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet challenging under cross-model and out-of-distribution shifts.

By Yi Tang, Xinyi Shang, Jiacheng Cui, Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tran Dinh Tien, Ahmed Elhagry, Salwa K. Al Khatib, Tianjun Yao, Yonina C. Eldar, Jing-Hao Xue, Hao Li, Salman Khan, Zhiqiang Shen

llms diffusion robotics multimodal benchmarks

arXiv AI

Jul 21

Constrained Path Reasoning: Measuring When Committed Stages Earn Their Cost

arXiv:2607. 17240v1 Announce Type: new Abstract: When does a committed intermediate stage in an LLM reasoning pipeline earn its cost?

By Honglin Li (ShanghaiTech University)

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arXiv AI

Jul 21

A Geometric Perspective on Stabilizing Value Conflict Resolution

arXiv:2607. 17946v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle to navigate value conflicts when trained with the compressed scalar rewards of Reinforcement Learning from Human Feedback (RLHF).

By Saket Reddy, Andy Liu

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