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

Oct 1

CompoWorld: Compositional Environment Scaling for General Agents

arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly...

By Xiao-Wen Yang, Weiyi Xu, Wen Da, Hang Xu, Canwei Li, Hong-Jie You, Pusen Dong, Yucheng Zeng, Zhaokai Luo, Yu-Feng Li, Yao Hu, Mu Chuan

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Oct 1

Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning

arXiv:2609.34211v2 Announce Type: replace Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However,...

By Linbo Shao, Huilin He, Yating Lou, Dawei Cheng

llms agents multimodal safety

arXiv AI

Oct 1

ACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs

arXiv:2609.37054v2 Announce Type: replace Abstract: Ensuring the safety of reasoning large language models (LLMs) across languages is essential for their reliable deployment. However, when exposed to...

By Xianhui Zhang, Jian Yu, Chengyu Xie, Chenhang Cui, Shuyi Miao, Pengyang Shao, Yu Zheng, Fei Shen, Tat-Seng Chua

llms benchmarks safety

arXiv AI

Oct 1

From Charts to Code: A Hierarchical Benchmark for Multimodal Models

arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (...

By Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

llms multimodal benchmarks

arXiv AI

Oct 1

ResidualKV: Residual-Based KV Cache Compression for Efficient Long-Context Inference

arXiv:2602.08005v2 Announce Type: replace-cross Abstract: Efficient long-context inference faces two coupled bottlenecks: KV-cache memory grows linearly with context length, while attention computati...

By Jitai Hao, Qiang Huang, Yaowei Wang, Min Zhang, Jun Yu

llms efficiency multimodal

arXiv AI

Oct 1

Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation

arXiv:2603.10210v2 Announce Type: replace-cross Abstract: While Diffusion Models excel in text-to-image synthesis, they frequently suffer from catastrophic concept omission when generating complex mu...

By Zitong Wang, Zijun Shen, Haohao Xu, Zhengjie Luo, Weibin Wu

llms diffusion multimodal safety

arXiv AI

Oct 1

Semantic Chunking and the Entropy of Natural Language

arXiv:2602.13194v3 Announce Type: replace-cross Abstract: Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong r...

By Weishun Zhong, Doron Sivan, Tankut Can, Mikhail Katkov, Misha Tsodyks

llms rag

arXiv AI

Oct 1

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

arXiv:2604.09107v2 Announce Type: replace-cross Abstract: Modern LLM reinforcement learning (RL) workloads require a high-performance weight transfer system to scale training across heterogeneous com...

By Chenhao Ye, Huaizheng Zhang, Mingcong Han, Baoquan Zhong, Xiang Li, Qixiang Chen, Xinyi Zhang, Weidong Zhang, Kaihua Jiang, Wang Zhang, He Sun, Wencong Xiao, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau

llms reinforcement-learning

arXiv AI

Oct 1

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation

arXiv:2605.07111v3 Announce Type: replace-cross Abstract: Recent literature on fine-tuning Large Language Models highlights a fundamental debate. While Full Fine-Tuning (FFT) provides greater represe...

By Haozhan Tang, Xiuqi Zhu, Xinyin Zhang, Boxun Li, Virginia Smith, Kevin Kuo

llms fine-tuning data-engineering

arXiv AI

Oct 1

WASIL: In-the-Wild Arabic Spoken Interactions with LLMs

arXiv:2605.16364v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) voice assistants are commonly built as cascaded Automatic Speech recognition (ASR) to LLM systems, where recogni...

By Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury

llms multimodal

arXiv AI

Oct 1

GrepSeek: Training Search Agents for Direct Corpus Interaction

arXiv:2605.29307v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Mo...

By Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, Razieh Rahimi, Fernando Diaz, Hamed Zamani

llms agents reinforcement-learning benchmarks

arXiv AI

Oct 1

Prompting Image Generators for Training-free Primitive Shape Abstraction

arXiv:2607.05568v2 Announce Type: replace-cross Abstract: Compact primitive abstractions represent 3D shapes with a few geometric primitives while preserving recognizable components. Learned methods...

By Gregor Kobsik, Tim Elsner, Leif Kobbelt

llms computer-vision fine-tuning multimodal

arXiv AI

Oct 1

The Hitchhiker's Guide to Monoculture: AI Homogenizes Syntax, Not (Necessarily) Semantics

arXiv:2607.13077v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have been widely reported to homogenize human expression and thought. However, I argue that convergence in langu...

By Gordon Burtch

llms rag

arXiv AI

Oct 1

SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents

arXiv:2609.35596v2 Announce Type: replace-cross Abstract: Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their con...

By Saswat Das, Parvati Viswanathan, Daniel Donnelly, Chang Huang, Sahar Abdelnabi, Ferdinando Fioretto

llms agents benchmarks safety