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 24

Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

arXiv:2607. 21482v1 Announce Type: new Abstract: Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models.

By Mack Nixon, Liam Wright, Yevgeniya Kovalchuk, Alison Fang-Wei Wu, Martin Danka, Andy Boyd, David Bann

llms agents benchmarks

arXiv AI

Jul 24

AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics

arXiv:2607. 20452v1 Announce Type: new Abstract: Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments.

By Vinil Pasupuleti, Shyalendar Reddy Allala, Siva Rama Krishna Varma Bayyavarapu, Shrey Tyagi, Srinivasateja Songa

llms agents reinforcement-learning

arXiv Machine Learning

Jul 24

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

arXiv:2607. 20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.

By Hao Liang, Qifeng Cai, Yibo Lin, Jianzhuo Du, Qifeng Xia, Sizhe Qiu, Linzhuang Sun, Meiyi Qiang, Zhaoyang Han, Xiaochen Ma, Bohan Zeng, Ruichuan An, Conghui He, Wentao Zhang

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 24

JAXBench: Benchmarking Autonomous TPU Kernel Optimization

arXiv:2607. 20466v1 Announce Type: new Abstract: Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization by establishing a shared target to hillclimb on, but no equivalent exists for TPUs.

By Arya Tschand, Charles Hong, Julian Walker, Nina Cai, Shangkun Wang, Suvinay Subramanian, Sundar Dev, Vijay Janapa Reddi, Amir Yazdanbakhsh, Sethu Sankaran

llms agents benchmarks

arXiv AI

Jul 24

Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it

arXiv:2607. 21498v1 Announce Type: cross Abstract: A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language models: epanorthosis, the self-correction of the specimen {\guillemotleft}This is not a course.

By Federico Boggia

llms reinforcement-learning fine-tuning

arXiv AI

Jul 24

From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring

arXiv:2511. 10806v1 Announce Type: cross Abstract: Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake.

By Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi, Md. Haider Ali, Md. Mosaddek Khan

llms computer-vision efficiency benchmarks

arXiv Machine Learning

Jul 24

Concept Concentration for Faithful Representation Intervention

arXiv:2505. 18672v2 Announce Type: replace Abstract: Representation intervention aims to localize and modify the representations that encode the underlying concepts in large language models (LLMs) to elicit the aligned and expected behaviors.

By Hongzheng Yang, Yongqiang Chen, Zeyu Qin, Tongliang Liu, Chaowei Xiao, Kun Zhang, Bo Han

llms safety

arXiv AI

Jul 24

AI Security Policy Should Assess Systems, Not Only Models

arXiv:2605. 09504v2 Announce Type: replace-cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, parallel exploration, and evolutionary optimization.

By Michael A. Riegler, Inga Str\"umke

llms agents safety

arXiv AI

Jul 24

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

arXiv:2607. 20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs.

By Paul Furgale, Severin Klingler, James Nolan, Matt Staats, Gaia Di Lorenzo, Elisa Martinez Abad, Christian Sch\"uller, Razvan Dinu, Alessio Devoto, Pascal Berard, Gal Kaplun, Elad Sarafian, Riccardo Roveri, Leon Derczynski, Ricardo Silveira Cabral

llms agents benchmarks

arXiv AI

Jul 24

Reliability-Aware LLM Alignment from Inconsistent Human Feedback

arXiv:2607. 20515v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences.

By Jingyi Huang, Ruohan Zong, Yujun Feng, Liran Ma, Lanyu Shang, Yang Zhang

llms reinforcement-learning benchmarks safety

arXiv AI

Jul 24

Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing

arXiv:2607. 20433v1 Announce Type: cross Abstract: While language models remain frozen at their training state, the world evolves continuously.

By Jea Kwon, Jiwon Kim, Dong-kyum Kim, Meeyoung Cha

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 24

HERMES: Heterogeneous Edge-Relational Multi-Head Embedded SSM Attention for Traffic Conflict Prediction at Signalized Intersections

arXiv:2607. 20505v1 Announce Type: cross Abstract: Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or flatten multi-agent scenes into fixed feature vectors, limiting their ability to represent heterogeneous interaction structure and evolving scene-level risk.

By Md Monzurul Islam, Subasish Das

llms agents safety

arXiv AI

Jul 24

RE-AD: Real-Time Requirement Adherence for Data Labeling

arXiv:2607. 20455v1 Announce Type: cross Abstract: Human-annotated data remains fundamental to training frontier Large Language Models (LLMs).

By Siddarth Malreddy, Ishan Nigam, Akshay Arora, Nikhil Mittal, Subrat Sahu

llms benchmarks

arXiv AI

Jul 24

Robostral Navigate

arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.

By Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi Raghavi Chandu, Ludovic Ho Fuh, Mathieu Poiree, Olivier Duchenne, Rosalie Millner, Srijan Mishra, Theo Cachet, Thomas Chabal

llms reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jul 24

Code Monitor Red Teaming for Public-Test-Passing Code

arXiv:2607. 20852v1 Announce Type: new Abstract: Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness.

By Junchi Liao, Jiawen Deng, Fuji Ren

llms safety

arXiv AI

Jul 24

Robust Critics: Defending LLMs Against Multi-Turn Attacks

arXiv:2607. 20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question?

By Roman Belaire, Arunesh Sinha, Pradeep Varakantham

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jul 24

Automated Synthesis and Adversarial Validation of Executable Causal Research Pipelines

arXiv:2607. 21173v1 Announce Type: new Abstract: While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes successfully yet relies on invalid causal assumptions.

By Irena Girshovitz, Dan Zeltzer, Ran Gilad-Bachrach

llms benchmarks safety

arXiv AI

Jul 24

From Noise to Diversity: Random Embedding Injection in LLM Reasoning

arXiv:2605. 11936v2 Announce Type: replace Abstract: Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned content or from the act of injection itself has not been carefully separated.

By Heejun Kim, Seungpil Lee, Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Taesup Kim, Sundong Kim

llms rag benchmarks

arXiv AI

Jul 24

Answer-then-Edit: Reasoning Skeleton Editing for Anti-Distillation with Preserved Utility

arXiv:2607. 20440v1 Announce Type: cross Abstract: Proprietary large language models (LLMs) entail substantial intellectual and financial investment, making them valuable intellectual property (IP).

By Fan Li, Mengting Pan, Sijia Xu, Xiaoyang Wang, Chen Chen, Wenjie Zhang

llms efficiency benchmarks

arXiv AI

Jul 24

Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants

arXiv:2607. 20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time.

By Bronislav Sidik, Chaya Levi, Nizzan Kimhi

llms agents efficiency benchmarks safety

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