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

Jun 30

Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management

arXiv:2510. 03310v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate human behavior in business, economics, and the social sciences, offering a low-cost complement to laboratory experiments, field studies, and surveys.

By Runze Zhang, Xiaowei Zhang, Mingyang Zhao

llms

arXiv AI

Jun 30

Internalized Reasoning for Long-Context Visual Document Understanding

arXiv:2604. 02371v2 Announce Type: replace-cross Abstract: Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have not explored reasoning, a capability which has driven leaps in math and code performance.

By Austin Veselka

llms fine-tuning efficiency data-engineering

arXiv Machine Learning

Jun 30

RenderFormer++: Scalable and Physically Grounded Feed-Forward Neural Rendering

arXiv:2606. 30380v1 Announce Type: cross Abstract: We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes.

By Huangsheng Du, Haoran Zhu, Youcheng Cai, Jinyang Meng, Ligang Liu

llms nlp

arXiv AI

Jun 30

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs

arXiv:2606. 28438v1 Announce Type: cross Abstract: Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control.

By Xinyuan Song, Zekun Cai, Liang Zhao

llms fine-tuning benchmarks

arXiv AI

Jun 30

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

arXiv:2606. 30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias.

By Haobo Yang

llms safety

arXiv AI

Jun 30

The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning

arXiv:2606. 28843v1 Announce Type: cross Abstract: Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task.

By Will Hawkins, Kaivalya Rawal, Jonathan Rystr{\o}m, Stratis Tsirtsis, Zihao Fu, Greta Warren, Ryan Brown, Eoin Delaney, Sandra Wachter, Brent Mittelstadt, Chris Russell

llms fine-tuning safety

arXiv Machine Learning

Jun 30

The Calibrated Deepfake Trust Score (CDTS): Competence-Coupled Trust Degradation Across Deepfake Detectors

arXiv:2606. 29484v1 Announce Type: cross Abstract: Modern deepfake detectors are rarely consumed as bare classifiers.

By Md Anas Biswas

llms computer-vision

arXiv AI

Jun 30

Building to the Test: Coding Agents Deliver What You Check, Not What You Requested

arXiv:2606. 28430v1 Announce Type: cross Abstract: Benchmarks are widely used to evaluate task completion by Large Language Models (LLMs), but this approach has accumulated construction-validity problems, and a passing score may not show whether the requested task was delivered.

By Yanuo Ma, Ben Kereopa-Yorke, Ben Schultz

llms agents benchmarks

arXiv AI

Jun 30

Mechanistic Personality Analysis of LLMs Steering Personality via Latent Feature Interventions

arXiv:2606. 28770v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated the ability to simulate human-like OCEAN personality traits in generated text.

By David Courtis, Ting Hu

llms fine-tuning benchmarks safety

arXiv AI

Jun 30

SemJoin: Semantic Join Optimization

arXiv:2606. 29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis.

By Christopher Gou, Aditya Banerjee, Jiaxuan Wang, Chunwei Liu

llms rag agents

arXiv AI

Jun 30

SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport

arXiv:2602. 23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world.

By Simon Roschmann, Paul Krzakala, Sonia Mazelet, Quentin Bouniot, Zeynep Akata

llms rag multimodal safety

arXiv AI

Jun 30

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.

By Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan

llms agents benchmarks data-engineering

arXiv AI

Jun 30

Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models

arXiv:2606. 29091v1 Announce Type: cross Abstract: Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them.

By Tassilo Klein, Johannes Hoffart

llms data-engineering

arXiv AI

Jun 30

Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks

arXiv:2510. 14207v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm.

By Trilok Padhi, Pinxian Lu, Abdulkadir Erol, Tanmay Sutar, Gauri Sharma, Mina Sonmez, Munmun De Choudhury, Ugur Kursuncu

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 30

TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents

arXiv:2606. 28480v1 Announce Type: cross Abstract: As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding.

By Shoufa Chen, Luyuan Wang, Xuan Yang, Zhiheng Liu, Yuren Cong, Yuanfeng Ji, Feiyan Zhou, Xiaohui Zhang, Fanny Yang, Belinda Zeng

llms agents benchmarks

arXiv Machine Learning

Jun 30

DataComp-VLM: Improved Open Datasets for Vision-Language Models

arXiv:2606. 28551v1 Announce Type: cross Abstract: Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curation strategies.

By Matteo Farina, Vishaal Udandarao, Thao Nguyen, Selim Kuzucu, Maximilian B\"other, Andreas Hochlehnert, Adhiraj Ghosh, Marianna Nezhurina, Karsten Roth, Joschka Struber, Yuhui Zhang, Sebastian Dziadzio, Elaine Sui, Soumya Jahagirdar, Dhruba Ghosh, Hasan Hammoud, Thomas De Min, Simone Caldarella, Jehanzeb Mirza, Sedrick Keh, Mehdi Cherti, Hilde Kuehne, Bernt Schiele, Serena Yeung-Levy, Muhammad Ferjad Naeem, Federico Tombari, Ana Klimovic, Elisa Ricci, Matthias Bethge, Sewoong Oh, Ameya Prabhu, Alessio Tonioni, Jenia Jitsev, Massimiliano Mancini, Ludwig Schmidt, Nikhil Parthasarathy

llms multimodal benchmarks

arXiv AI

Jun 30

Accelerating scientific discovery with Co-Scientist

arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.

By Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger, Alessio Orlandi, Dan Popovici, Anil Palepu, Keran Rong, Ryutaro Tanno, Khaled Saab, Fan Zhang, Jacob Blum, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Dina Zverinski, Ivor Rendulic, Elahe Vedadi, Florian Hasler, Luka Rimanic, Marina Boia, Ivan Budiselic, Ben Feinstein, Mathias Bellaiche, Tom Sheffer, Jan Freyberg, Jeremy Ratcliff, Ottavia Bertolli, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Yuan Guan, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago R D Costa, Jos\'e R Penad\'es, Gary Peltz, Yossi Matias, James Manyika, Demis Hassabis, Yunhan Xu, Pushmeet Kohli, Annalisa Pawlosky, Alan Karthikesalingam, Vivek Natarajan

llms agents

arXiv Machine Learning

Jun 30

FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks

arXiv:2606. 28962v1 Announce Type: cross Abstract: Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Conditioned Backdoor (QCB) attacks.

By Aoying Zheng, Anqi Du, Zizhuang Deng, Yuxuan Chen

llms efficiency safety

arXiv AI

Jun 30

Research Entity Extraction and Topic Detection from UKRI Grant Proposals

arXiv:2606. 30304v1 Announce Type: cross Abstract: This paper presents preliminary findings from a UKRI-funded Metascience project comparing three LLM-based approaches, GPT-4o, Mistral, and a bespoke algorithm, DSIT-Taxonomies, for extracting and classifying research entities from funding proposals.

By Xingran Ruan, Angelo Salatino, Rosa Filgueira, Kara Moraw, Alexandru Marcoci, Gemma Derrick, Sarah Callaghan

llms

arXiv Machine Learning

Jun 30

Inexact calculus of variations on the hyperspherical tangent bundle with connections to the attention mechanism

arXiv:2507. 15431v4 Announce Type: replace Abstract: We offer a theoretical mathematical background through Lagrangian optimization on the unit hyperspherical manifold and its tangential structure.

By Andrew Gracyk

llms

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