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 Machine Learning

Jul 8

A semantic mutation metric for metamorphic relation adequacy in scientific computing programs

arXiv:2605. 17437v2 Announce Type: replace-cross Abstract: Context.

By Meng Li (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Xiaohua Yang (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Jie Liu (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Shiyu Yan (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China)

llms benchmarks

arXiv Machine Learning

Jul 8

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates

arXiv:2604. 04738v2 Announce Type: replace-cross Abstract: Fine-tuning is the dominant paradigm for adapting large machine learning models, yet current deployment pipelines provide no way to verify how a released model was updated.

By Zhenhang Shang, Yingzhe Yu, Kani Chen

llms fine-tuning efficiency 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 AI

Jul 8

Foundation Models for Automatic CAD Generation

arXiv:2607. 05573v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-language specifications.

By J de Curt\`o, Victoria Guill\'en, I. de Zarz\`a

llms multimodal benchmarks

arXiv AI

Jul 8

Catalyst Papers in Artificial Intelligence Research: A Landscape on ICLR from 2017 to 2025

arXiv:2607. 05401v1 Announce Type: cross Abstract: A small number of methodological contributions, including word2vec, the Transformer, large-scale pre-training, and reinforcement learning from human feedback, have reshaped NLP and AI research over the past decade.

By Fan Huang

llms rag nlp reinforcement-learning

arXiv AI

Jul 8

Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks

arXiv:2607. 05545v1 Announce Type: cross Abstract: LLM conformity is often used to describe cases where a model changes a correct answer toward a peer or group response.

By Yibo Hu, Jiaming Qu

llms benchmarks

arXiv AI

Jul 8

PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation

arXiv:2607. 05915v1 Announce Type: new Abstract: PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers.

By Hyungseok Song, Junseok Park, Won-Seok Choi, Seohui Bae, Han-Seul Jeong, Youngjoon Park, Soonyoung Lee

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 8

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.

By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

llms benchmarks

arXiv AI

Jul 8

PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages

arXiv:2607. 05992v1 Announce Type: cross Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites.

By Daryna Dementieva, Nikolay Babakov, Kathy H\"ammerl, Ilseyar Alimova, Jind\v{r}ich Libovick\'y, Shu Okabe, Miras Baisbay, Lukas Edman, Abrorkhon Inomkhujaev, Antonia Karamolegkou, Mateusz Lango, Volkan \"Ozer, Nikola Selic, Subhankar Swain, Tsedeniya Kinfe Temesgen, Galit Bary Weisberg, Alexander Fraser

llms benchmarks

arXiv AI

Jul 8

Privilege and confidentiality in generative AI workflows

arXiv:2607. 05479v1 Announce Type: cross Abstract: Generative AI (GenAI) systems store and process client data in three distinct ways: in the model's parameters through training and memorisation, in the context window during a live session, and in knowledge databases for retrieval-augmented generation (RAG).

By V\'aclav Jane\v{c}ek, Thomas Melham

llms rag benchmarks

arXiv Machine Learning

Jul 8

Leech Lattice Vector Quantization for Efficient LLM Compression

arXiv:2603. 11021v2 Announce Type: replace Abstract: Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds.

By Tycho F. A. van der Ouderaa, Mart van Baalen, Paul Whatmough, Markus Nagel

llms efficiency benchmarks

arXiv AI

Jul 8

EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping

arXiv:2607. 06105v1 Announce Type: cross Abstract: High-resolution RGB imagery acquired from low-altitude UAV surveys was processed through a modular pipeline incorporating transformer-based semantic segmentation, connected-component vegetation extraction, fine-grained species classification using a ConvNeXt architecture, and grid-based dominance scoring at 2x2m resolution.

By Innocent Onyenonachi, Peter J. Lawerance, Nadia Kanwal

llms computer-vision robotics

arXiv AI

Jul 8

Decision Protocols in Multi-Agent Large Language Model Conversations

arXiv:2607. 05477v1 Announce Type: cross Abstract: Improving the task performance of Large Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs.

By Lars Benedikt Kaesberg

llms agents benchmarks

arXiv AI

Jul 8

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment

arXiv:2607. 05552v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wording - among them an amplified yes-no bias on moral dilemmas, absent in humans.

By Haonan Huang

llms safety

arXiv AI

Jul 8

Universal Algorithm-Implicit Learning

arXiv:2602. 14761v2 Announce Type: replace-cross Abstract: Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability.

By Stefano Woerner, Seong Joon Oh, Christian F. Baumgartner

llms rag nlp multimodal benchmarks

arXiv AI

Jul 8

FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents

arXiv:2607. 05682v1 Announce Type: new Abstract: LLM systems for scientific discovery increasingly assist with ideation, literature synthesis, experiment planning, and report generation, but the first research question they propose can remain difficult to audit: it may sound plausible without exposing the mechanism, falsifier, or assumption that a scientist should inspect.

By Yufeng Wang

llms agents benchmarks

arXiv AI

Jul 8

DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression

arXiv:2607. 06523v1 Announce Type: new Abstract: Long-context language model inference is increasingly limited by the memory bandwidth and capacity required to store key-value caches, yet existing compression methods often apply uniform budgets across layers or tokens and degrade retrieval when lexical cues and semantic states require different preservation.

By Anna Cordoba, Adam Puente Tercero, Nerea Angulo Hijo, Mar Linares Tercero, Julia Barrientos, Ainhoa Miranda, Jesus Olivera

llms nlp efficiency benchmarks

arXiv AI

Jul 8

Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.

By Husnain Amjad, Raja Khurram Shahzad, Aamir Shahzad, Mehwish Fatima

llms fine-tuning benchmarks

arXiv AI

Jul 8

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade

arXiv:2607. 06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantial inference compute before the failure becomes observable.

By Kai Ruan, Zihe Huang, Ziqi Zhou, Qianshan Wei, Xuan Wang, Hao Sun

llms agents

arXiv AI

Jul 8

Akashic: A Low-Overhead LLM Inference Service with MemAttention

arXiv:2607. 05708v1 Announce Type: new Abstract: Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows.

By Yang Liu, Zhaokai Luo, Huayi Jin, Ruozhou He, Chenchen Hong, Zhiyong Wang, Yifei Liu, Yunfei Gu, Chentao Wu, Junhao Hu

llms agents efficiency

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