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

Robust Explanations for User Trust in Enterprise NLP Systems

arXiv:2604. 12069v3 Announce Type: replace-cross Abstract: Robust explanations are increasingly required for user trust in enterprise NLP, yet pre-deployment validation is difficult in the common case of black-box deployment (API-only access) where representation-based explainers are infeasible and existing studies provide limited guidance on whether explanations remain stable under real user noise, especially when organizations migrate from encoder classifiers to decoder LLMs.

By Guilin Zhang, Kai Zhao, Jeffrey Friedman, Xu Chu, Amine Anoun, Jerry Ting
arXiv AI
Jul 16

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.

By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu
arXiv AI
Jul 16

Social Simulations: from Agent-Based Modeling to Digital Twins

arXiv:2607. 13693v1 Announce Type: cross Abstract: This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly defined behavioral rules, to AI-enhanced simulations based on Large Language Models and, ultimately, Social Digital Twins: high-fidelity, data-driven representations of real-world socio-technical systems.

By Erica Cau, Andrea Failla, Valentina Pansanella, Giulio Rossetti
arXiv AI
Jul 16

Full-Pipeline Inference Optimization for MiMo-V2.5 Series: Pushing Hybrid SWA Efficiency to the Limit

arXiv:2607. 13095v1 Announce Type: cross Abstract: We present a full-pipeline inference optimization for the MiMo-V2.

By Xiaomi MiMo Team, Anqi Liu, Aoxin Ma, Bo Chen, Bo Yang, Chen Wang, Chen Zhang, Chengda Tang, Chengwei Wang, Chiheng Lou, Depeng Yan, Fuli Luo, Gang Wang, Hailin Zhang, Jiale Sun, Kang Zhou, Rui Huang, Shaohui Liu, Shen Huang, Shijie Cao, Shuaishuai Fan, Tianling Zhou, Xiangwei Deng, Xueyang Xie, Xuli Wang, Yingchun Lai, Yu Yang, Yuan Zhang, Zhen Tang, Zhonghua Deng, Zihan Jiang
arXiv AI
Jul 16

Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System

arXiv:2607. 13608v1 Announce Type: new Abstract: Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe.

By David Krongauz, Arad Zulti, Eran Segal, Teddy Lazebnik
arXiv AI
Jul 16

Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry

arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.

By Oriana Presacan, Andreea Grama, Larisa Irimin\u{a}, Alireza Nik, Jaya Ojha, Vajira Thambawita, Ciprian I. B\u{a}cil\u{a}, Bogdan Ionescu, Michael A. Riegler