Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

21,286 stories · RSS feed

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

Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning

arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.

By Zhihao Liu, Tianyu Wang, Xi Vincent Wang, Lihui Wang

llms agents

arXiv AI

Jul 21

A Systematic Evaluation of Trajectory Data Curation for LoRA Fine-Tuning of Code Agents

arXiv:2607. 17205v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) of open-weight LLMs on expert agent trajectories has emerged as a prominent approach to building capable code agents without reliance on proprietary models.

By Yunze Han

llms agents fine-tuning benchmarks

arXiv AI

Jul 21

Constraint-Anchored Reasoning Traces

arXiv:2607. 16727v1 Announce Type: new Abstract: Autoregressive multimodal large language models (MLLMs) suffer from error snowballing: a single incorrect inference early in a chainof-thought (CoT) trace corrupts all downstream reasoning.

By Zehua Cheng, Wei Dai, Jiahao Sun

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 21

FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images

arXiv:2607. 16819v1 Announce Type: new Abstract: In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation.

By Yi Yang, Xiaokun Zhang, Yuxuan Li, Ruyi Zhang, Xinpeng Zhou, Haipeng Wang

llms reinforcement-learning multimodal

arXiv AI

Jul 21

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches

arXiv:2607. 16205v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has emerged as a standard approach for enhancing reasoning in large language models, which typically optimizes the policy by contrasting multiple self generated rollouts.

By Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Liwei Qian, Xin Pei, Jizhou Huang

llms reinforcement-learning benchmarks

arXiv AI

Jul 21

ZifaMem: Structured Memory for Persona, Preference, and Emotional Continuity in AI Companions

arXiv:2607. 17564v1 Announce Type: new Abstract: AI companions are judged not only by single-turn fluency but by whether they sustain emotional continuity: remembering who the companion is, what the user prefers, and how the relationship has felt.

By Jingzhe Fang, Guozhi Xu, Yunfan Cui, Xiaochen Yang, Zhangyu Hua

llms agents

arXiv AI

Jul 21

LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models

arXiv:2607. 16339v1 Announce Type: new Abstract: Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation.

By Xingru Chen, Zelang Liang, Yongjia Ma, Jiqing Zhan, Shuling Yang, Lian Wen, Kun Zhan

llms rag diffusion efficiency

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

llms

arXiv Machine Learning

Jul 21

OpenLanguageModel: Readable and Composable Small-Language-Model Pretraining for Education and Research

arXiv:2607. 16669v1 Announce Type: cross Abstract: OpenLanguageModel (OLM) is an open-source PyTorch library for building and pretraining small language models while keeping their machinery visible.

By Tavish Mankash, Vardhaman Kalloli, Keshava Prasad, Deepan Muthirayan

llms benchmarks

arXiv AI

Jul 21

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis

arXiv:2606. 07559v2 Announce Type: replace-cross Abstract: Fine-tuning a language model often fails silently when its correct completion must outrank a near-synonym competitor.

By Vaibhav Prakash, Jayasri Dontabhaktuni

llms rag fine-tuning

arXiv Machine Learning

Jul 21

Singular Bayesian Neural Networks

arXiv:2602. 00387v4 Announce Type: replace-cross Abstract: Bayesian neural networks promise calibrated uncertainty but require $O(mn)$ parameters for standard mean-field Gaussian posteriors.

By Mame Diarra Toure, David A. Stephens

llms benchmarks safety

arXiv AI

Jul 21

Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution

arXiv:2607. 17712v1 Announce Type: new Abstract: Detecting high-level semantic concepts like negation across modalities remains a challenge for current multimodal systems.

By Ali AbuSaleh, Leon Hammerla, Alexander Mehler

llms rag multimodal

arXiv AI

Jul 21

Medical Imaging Fusing Vision Transformer: Laryngeal Cancer Screening with Explanation

arXiv:2607. 17789v1 Announce Type: cross Abstract: Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes.

By Haiyang Wang, Luca Mainardi

llms computer-vision

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

llms

arXiv Machine Learning

Jul 21

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

arXiv:2506. 07691v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability.

By Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

llms fine-tuning safety

arXiv Machine Learning

Jul 21

Auditing Invisible Weight Updates with Reference Traces

arXiv:2607. 09800v2 Announce Type: replace Abstract: Master weights and stochastic rounding bypass invisible stored-weight updates but do not locate lost direct-storage proposals or parameters worth protecting.

By Zekai Shang

llms

arXiv AI

Jul 21

MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

arXiv:2607. 18006v1 Announce Type: cross Abstract: Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models ($\leq 4 \, \mathrm{B}$ parameters) trained under limited budgets.

By Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Zifeng Ding, Volker Tresp, Yunpu Ma

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 21

ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning

arXiv:2607. 16323v1 Announce Type: cross Abstract: Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR).

By Alexander Selivanov, Friederike Jungmann, Jan Kehrer, Karl-Ludwig Laugwitz, Eimo Martens, Daniel Rueckert

llms multimodal benchmarks

arXiv AI

Jul 21

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification

arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.

By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan

llms diffusion computer-vision fine-tuning multimodal benchmarks

Get the daily briefing