Large language models

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

24,907 stories · RSS feed

arXiv AI

Jul 8

BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension

arXiv:2607. 05614v1 Announce Type: cross Abstract: Document comprehension is a challenging yet impactful task for Multimodal Large Language Models, especially as these systems see growing adoption in real-world, human-centric applications.

By Abu Tyeb Azad, Ishita Sur Apan, Fahim Ahmed, Sumaiya Karim Katha, Ezharuddin Jubaer, Armun Alam, Pranjal Kumar Nandi, Amin Ahsan Ali, Aman Chadha, Md Mofijul Islam, AKM Mahbubur Rahman

llms multimodal benchmarks

arXiv AI

Jul 8

Few Channels Draw The Whole Picture: Revealing Massive Activations in Diffusion Transformers

arXiv:2605. 13974v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) and related flow-based architectures are now among the strongest text-to-image generators, yet the internal mechanisms through which prompts shape image semantics remain poorly understood.

By Evelyn Turri, Davide Bucciarelli, Sara Sarto, Lorenzo Baraldi, Marcella Cornia

llms diffusion efficiency

arXiv Machine Learning

Jul 8

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

arXiv:2601. 15275v3 Announce Type: replace-cross Abstract: We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can adapt to the geometry of the underlying 3D scene.

By Yu Wu, Minsik Jeon, Jen-Hao Rick Chang, Oncel Tuzel, Shubham Tulsiani

llms computer-vision

arXiv AI

Jul 8

Controlling Tool Use with Heading-Specific Activation Steering

arXiv:2607. 05790v1 Announce Type: new Abstract: Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily.

By Yuqi Chen, Vincent Siu, Yang Liu, Dawn Song, Chenguang Wang

llms agents safety

arXiv AI

Jul 8

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

arXiv:2607. 06223v1 Announce Type: new Abstract: Reinforcement learning has become a promising paradigm for improving large language model (LLM) agents on long-horizon search tasks, where the agent must make a sequence of intermediate decisions before receiving a final outcome.

By Yijun Zhang, Fan Xu, Jiaxin Ding, Yule Xie, Shiqing Gao, Xin Ding, Haoxiang Zhang, Luoyi Fu, Xinbing Wang

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 8

The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities

arXiv:2607. 05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe.

By Mohammadreza Rashidi

llms agents benchmarks safety

arXiv Machine Learning

Jul 8

How Can Mamba Learn In Context with Outliers and Generalize Provably?

arXiv:2510. 00399v2 Announce Type: replace Abstract: The Mamba model has gained significant attention for its computational advantages over Transformer-based models, while achieving comparable performance across a wide range of language tasks.

By Hongkang Li, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Meng Wang

llms fine-tuning

arXiv AI

Jul 8

aiAuthZ: Off-Host, Identity-Bound Authorization for AI Agents

arXiv:2607. 05518v1 Announce Type: cross Abstract: AI agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance of authority.

By Sai Varun Kodathala

llms agents safety

arXiv AI

Jul 8

Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns

arXiv:2607. 05855v1 Announce Type: cross Abstract: Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans.

By Sishun Liu, Sajal Halder, Ke Deng, Yan Wang, Xiuzhen Zhang

llms multimodal

arXiv AI

Jul 8

Narrative-Centered Emotional Reflection: An Early Prototype for AI-Supported Emotional Self-Reflection

arXiv:2504. 20342v2 Announce Type: replace-cross Abstract: Reflexion is an AI-powered prototype designed to explore structured emotional self-reflection.

By Shou-Tzu Han

llms agents

arXiv AI

Jul 8

LEGATO 2: Toward Multimodal Sheet Music Recognition and Understanding

arXiv:2607. 05769v1 Announce Type: cross Abstract: We propose a novel pipeline, Legato 2, for extracting symbolic notation and semantic knowledge from images of sheet music.

By Guang Yang, Brian Siyuan Zheng, Victoria Ebert, Noah A. Smith

llms computer-vision multimodal benchmarks

arXiv AI

Jul 8

CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training

arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.

By Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms diffusion reinforcement-learning

arXiv AI

Jul 8

Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development

arXiv:2607. 06074v1 Announce Type: cross Abstract: Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature.

By Rohit Mehra, Kapil Singi, Vikrant Kaulgud, Vibhu Saujanya Sharma, Swapnajeet Gon Choudhury, Swati Sharma, Adam P. Burden, Majd Sakr

llms agents

arXiv Machine Learning

Jul 8

PGOT: A Physics-Geometry Operator Transformer for Complex PDEs

arXiv:2512. 23192v4 Announce Type: replace Abstract: While Transformers have demonstrated remarkable potential in modeling Partial Differential Equations (PDEs), modeling large-scale unstructured meshes with complex geometries remains a significant challenge.

By Zhuo Zhang, Xi Yang, Ying Miao, Xiaobin Hu, Yifu Gao, Yong Yang, Canqun Yang, Boocheong Khoo

llms benchmarks

arXiv Machine Learning

Jul 8

Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding

arXiv:2607. 05936v1 Announce Type: cross Abstract: Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex and evolving API specifications.

By Daniel Maninger, Leon Chemnitz, Jannis Brugger, Tushar Lamba, Amir Molzam Sharifloo, Mira Mezini

llms rag safety

arXiv Machine Learning

Jul 8

Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES

arXiv:2607. 05691v1 Announce Type: cross Abstract: Every chemical language model reading SMILES begins with a tokenizer, yet the field has inherited byte-pair encoding (BPE) from natural language with little scrutiny.

By Hunter Heidenreich

llms rag computer-vision nlp

arXiv Machine Learning

Jul 8

Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization

arXiv:2607. 05861v1 Announce Type: cross Abstract: Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers.

By Kaishen Wang, Tong Zheng, Xuehao Cui, Ruibo Chen, Tianyi Xiong, Heng Huang

llms nlp reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 8

Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators

arXiv:2607. 05400v1 Announce Type: cross Abstract: Generative AI models, such as Large Language Models (LLMs) and diffusion models, have demonstrated impressive performance across a wide range of tasks.

By Amitash Nanda, Javier Hernandez Nicolau, Madhusudan Gujral, Mahidhar Tatineni, Amitava Majumdar, Debashis Sahoo

llms diffusion fine-tuning efficiency

arXiv AI

Jul 8

Harnessing Code Agents for Automatic Software Verification

arXiv:2607. 06341v1 Announce Type: cross Abstract: Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort.

By Shuangxiang Kan, Shuanglong Kan, Sebastian Ertel

llms agents benchmarks

arXiv AI

Jul 8

LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis

arXiv:2607. 06160v1 Announce Type: cross Abstract: Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches share three limitations: narrow task coverage, insufficient instruction difficulty, and a lack of faithfulness supervision.

By Chenhao Yuan, Yinhao Xu, Shuwen Xu, Xizhi Yang, Jiaxiang Liu, Chenxi Zhou, Shaoping Huang, Haolin Ren, Pengfei Cao, Jun Zhao, Kang Liu

llms fine-tuning

Get the daily briefing