Large language models

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

31,367 stories · RSS feed

arXiv AI

Jun 4

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

arXiv:2506. 10630v3 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures.

By Yitong Zhou, Yucong Luo, Mingyue Cheng, Qi Liu, Jiahao Wang, Daoyu Wang, Enhong Chen

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 4

Aligning Deep Implicit Preferences by Learning to Reason Defensively

arXiv:2510. 11194v3 Announce Type: replace Abstract: Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions.

By Peiming Li, Zhiyuan Hu, Yang Tang, Shiyu Li, Xi Chen

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 4

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606. 04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degradation.

By Devleena Das, Rajeev Patwari, Elliott Delaye, Ashish Sirasao

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 4

QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy

arXiv:2606. 04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks.

By Pasindu Wickramasinghe, Achyuta Muthuvelan, Rachmad Vidya Wicaksana Putra, Minghao Shao, Muhammad Shafique

llms nlp efficiency benchmarks

arXiv AI

Jun 4

Ekka: Automated Diagnosis of Silent Errors in LLM Inference

arXiv:2606. 04594v1 Announce Type: cross Abstract: LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations.

By Yile Gu, Zhen Zhang, Shaowei Zhu, Xinwei Fu, Jun Wu, Yida Wang, Baris Kasikci

llms benchmarks

arXiv AI

Jun 4

Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent

arXiv:2606. 05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis.

By Linyao Chen, Qinlao Zhao, Zechen Li, Mingming Li, Likun Ni, Jinyu Chen, Yuhao Yao, Xuan Song, Noboru Koshizuka, Hiroki Kobayashi

llms agents safety

arXiv Machine Learning

Jun 4

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws

arXiv:2602. 05725v3 Announce Type: replace Abstract: Muon updates matrix parameters via the matrix sign of the gradient and has shown strong empirical gains, yet its dynamics and scaling behavior remain unclear in theory.

By Binghui Li, Kaifei Wang, Han Zhong, Pinyan Lu, Liwei Wang

llms safety

arXiv AI

Jun 4

NoRA: Evaluating Grounded Reasonableness in Visual First-person Normative Action Reasoning

arXiv:2606. 04806v1 Announce Type: cross Abstract: LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior.

By Sichao Li, Sai Ma, Daniel Kilov, Secil Yanik Guyot, Zhuang Li, Seth Lazar

llms agents multimodal benchmarks safety

arXiv AI

Jun 4

Adaptive Patching Is Harder Than It Looks For Time-Series Forecasting

arXiv:2606. 04074v1 Announce Type: cross Abstract: Adaptive patching is a recent and compelling proposal for time-series Transformers: allocate finer patches where the sequence looks locally informative.

By Federico Zucchi, Yi Xie, Chao Zhang, Keyuan Luo, Thomas Lampert, Ziyue Li

llms benchmarks safety

arXiv AI

Jun 4

The Biomimetic Architecture of Software 4.0

arXiv:2606. 04025v1 Announce Type: cross Abstract: Dominant programming paradigms inherit an execution model optimised for a bygone era of a single human mind instructing a local machine, leaving contemporary systems burdened with historical path dependencies.

By Philip Sheldrake, Dirk Scheffler

llms

arXiv AI

Jun 4

Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification

arXiv:2606. 04037v1 Announce Type: new Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment.

By Thanh Luong Tuan, Abhijit Sanyal

llms rag agents benchmarks safety

arXiv AI

Jun 4

Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection

arXiv:2606. 04599v1 Announce Type: new Abstract: Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios.

By Yongzi Yu, Ao Li, Le Wang, Ziyue Li, Fugee Tsung, Yuxuan Liang, Man Li

llms agents safety

arXiv AI

Jun 4

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

arXiv:2603. 28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt.

By Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or

llms diffusion multimodal safety

arXiv AI

Jun 4

PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay

arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.

By Rohan Khetan, Ashna Khetan

llms benchmarks safety

arXiv AI

Jun 4

AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms

arXiv:2602. 09464v2 Announce Type: replace-cross Abstract: Vericoding refers to the generation of formally verified code from rigorous specifications.

By Haoyu Zhao, Ziran Yang, Jiawei Li, Deyuan He, Zenan Li, Chi Jin, Venugopal V. Veeravalli, Aarti Gupta, Sanjeev Arora

llms benchmarks

arXiv AI

Jun 4

Bounded Hyperbolic Tangent: A Stable and Efficient Alternative to Pre-Layer Normalization in Large Language Models

arXiv:2601. 09719v3 Announce Type: replace-cross Abstract: Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective transfer learning.

By Hoyoon Byun, Youngjun Choi, Taero Kim, Sungrae Park, Kyungwoo Song

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 4

Test-time reward-guided alignment of language models by importance sampling on pre-logit space

arXiv:2510. 26219v3 Announce Type: replace-cross Abstract: Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs.

By Sekitoshi Kanai, Tsukasa Yoshida, Hiroshi Takahashi, Haru Kuroki, Kazumune Hashimoto

llms fine-tuning safety

arXiv AI

Jun 4

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

arXiv:2506. 05233v2 Announce Type: replace-cross Abstract: Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention.

By Johannes von Oswald, Nino Scherrer, Seijin Kobayashi, Luca Versari, Songlin Yang, Sarthak Mittal, Maximilian Schlegel, Kaitlin Maile, Yanick Schimpf, Oliver Sieberling, Alexander Meulemans, Rif A. Saurous, Guillaume Lajoie, Charlotte Frenkel, Razvan Pascanu, Blaise Ag\"uera y Arcas, Jo\~ao Sacramento

llms benchmarks

arXiv AI

Jun 4

Belief-Aware VLM Model for Human-like Reasoning

arXiv:2604. 09686v2 Announce Type: replace Abstract: Traditional neural network models for intent inference rely heavily on observable states and struggle to generalize across diverse tasks and dynamic environments.

By Anshul Nayak, Shahil Shaik, Yue Wang

llms diffusion reinforcement-learning multimodal

arXiv AI

Jun 4

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

arXiv:2506. 10912v4 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure.

By Fei Lin, Ziyang Gong, Cong Wang, Tengchao Zhang, Yonglin Tian, Yining Jiang, Ji Dai, Chao Guo, Xiaotong Yu, Xue Yang, Gen Luo, Fei-Yue Wang

llms efficiency multimodal benchmarks safety

Get the daily briefing