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

SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics

arXiv:2606. 29894v1 Announce Type: cross Abstract: As agentic AI systems tackle more complex mathematical tasks, they increasingly rely on information retrieval (IR) to search problem databases, theorem libraries, and educational resources.

By Nikolay Georgiev, Maria Drencheva, Kseniia Ibragimova, Ivo Petrov, Dimitar I. Dimitrov, Martin Vechev

llms rag agents benchmarks

arXiv AI

Jun 30

Spectral Perturbation of the Empirical Fisher Information Matrix under Weight Quantization

arXiv:2606. 28432v1 Announce Type: cross Abstract: We study the spectral perturbation of the empirical Fisher Information Matrix (FIM) of a parametric statistical model under two structured perturbations: departure of the input from a reference (in-distribution) ensemble, and finite-precision (quantized) perturbation of the model's parameters.

By Rahid Zahid Alekberli, Hikmat Karimov

llms efficiency

arXiv AI

Jun 30

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation

arXiv:2606. 30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.

By Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer, Mukesh Prasad, Weiping Ding, Yew-Soon Ong

llms diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 30

Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning

arXiv:2606. 29725v1 Announce Type: new Abstract: In this paper, we formulate a new vehicle dispatch optimization problem, called Nursing Care Taxi Dispatch, as a variant of the Vehicle Routing Problem, considering constraints related to wheelchair use, user compatibility, pick-up and drop-off times, and vehicle limitations.

By Riku Nakao, Akihito Hiromori, Hamada Rizk, Hirozumi Yamaguchi

llms

arXiv AI

Jun 30

DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows

arXiv:2606. 29727v1 Announce Type: new Abstract: Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents.

By Ziyang Lian, Qingya Zhang, Hao Wang, Huiwen Xiong, Qi Yang, Lingyi Meng, Xiaoyi Gu, Rui Wang

llms agents

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 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

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

Online Data Selection for Instruction Tuning via Gaussian Processes

arXiv:2606. 30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged as a critical research topic.

By Jun Wang, Quoc Phong Nguyen, Julien Monteil, Vu Nguyen

llms fine-tuning benchmarks data-engineering

arXiv AI

Jun 30

ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval

arXiv:2606. 28326v1 Announce Type: cross Abstract: This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries.

By Ke Chen, Shengyuan Han, Yongfeng Huang, Yujin Zhu, Jingwei Xiong, Liang Xu, Jundong Liu

llms agents benchmarks

arXiv AI

Jun 30

Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking

arXiv:2606. 29088v1 Announce Type: cross Abstract: There are various benchmarks to evaluate bugfixing capabilities of Large Language Models.

By Bal\'azs Szalontai, \'Abel Szauter, Bal\'azs M\'arton, P\'eter Verebics, Bal\'azs Pint\'er, Tibor Gregorics

llms fine-tuning benchmarks

arXiv AI

Jun 30

Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation

arXiv:2602. 12089v3 Announce Type: replace-cross Abstract: As AI usage becomes more prevalent in social contexts, understanding agent-user interaction is critical to designing systems that imp rove both individual and group outcomes.

By Kehang Zhu, Nithum Thain, Vivian Tsai, James Wexler, Crystal Qian

llms agents

arXiv AI

Jun 30

DOPD: Dual On-policy Distillation

arXiv:2606. 30626v1 Announce Type: new Abstract: On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals.

By Xinlei Yu, Gen Li, Qingyi Si, Guibin Zhang, Yuqi Xu, Congcong Wang, Shuai Dong, Kaiwen Tuo, Xiangyu Zeng, Kaituo Feng, Qunzhong Wang, Yang Shi, Xiaobin Hu, Xiangyu Yue, Jiaqi Wang, Shuicheng Yan

llms efficiency multimodal

arXiv AI

Jun 30

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems

arXiv:2606. 28425v1 Announce Type: cross Abstract: Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels.

By Jimmy Laurence Rippin, Simon C. Marshall, David Demitri Africa, Christian Schroeder de Witt

llms agents safety

arXiv Machine Learning

Jun 30

Uncertainty-Aware Generation and Decision-Making Under Ambiguity

arXiv:2606. 30578v1 Announce Type: cross Abstract: With rapidly improving capabilities, Large Language Models (LLMs) are increasingly used in many complex real-world tasks.

By Nico Daheim, Iryna Gurevych

llms

arXiv AI

Jun 30

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping

arXiv:2606. 29983v1 Announce Type: cross Abstract: Looped Transformers, which repeatedly apply a shared transformer block, are an architecturally natural fit for variable-length algorithmic tasks.

By Hsun-Yu Kuo, El Mahdi Chayti, Patrik Reizinger, Wieland Brendel, Martin Jaggi

llms reinforcement-learning

arXiv AI

Jun 30

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks

arXiv:2504. 17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in contrast, are more efficient and tailored to specific domains yet lack general-purpose coverage.

By Yang Liu, Kejia Zhang, Bingjie Yan, Tianyuan Zou, Jianqing Zhang, Zixuan Gu, Xiangsen Chen, Jianbing Ding, Xidong Wang, Jingyi Li, Xiaozhou Ye, Ye Ouyang, Qiang Yang, Ya-Qin Zhang

llms fine-tuning safety

arXiv AI

Jun 30

ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models

arXiv:2606. 28719v1 Announce Type: new Abstract: Test-time adaptation (TTA) of vision-language models (VLMs) is essential for their robust deployment in dynamic, real-world environments.

By Guanglong Sun, Shuang Cui, Bo Lei, Liyuan Wang, Zihan Zhai, Hongwei Yan, Hang Su, Jun Zhu, Yi Zhong

llms multimodal benchmarks

arXiv AI

Jun 30

Memory as an Attack Surface in LLM Agents: A Study on Multiple-Choice Question Answering

arXiv:2606. 29030v1 Announce Type: new Abstract: AI agents extend conventional large language model (LLM) applications by integrating language understanding with task execution, external tool use, and memory mechanisms.

By Shahnewaz Karim Sakib, Anindya Bijoy Das

llms agents nlp robotics

arXiv Machine Learning

Jun 30

Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration

arXiv:2509. 21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples.

By Dongkyu Cho, Miao Zhang, Rumi Chunara

llms safety

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